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Quagliarella</h1><div class="affiliations-container fake-truncate js-profile-affiliations"></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="D." data-follow-user-id="37141433" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="37141433"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p 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class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/DomenicoQuagliarella"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/DomenicoQuagliarella">Domenico Quagliarella</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://odu.academia.edu/IdeenSadrehaghighi"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://odu.academia.edu/IdeenSadrehaghighi">Ideen Sadrehaghighi</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Old Dominion University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/DavideCinquegrana"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/DavideCinquegrana">Davide Cinquegrana</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/CharlesSese"><img class="profile-avatar u-positionAbsolute" border="0" alt="" 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class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/GaitherAdam">Adam Gaither</a></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="37141433" href="https://www.academia.edu/Documents/in/Multiobjective_Optimization"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/DQuagliarella","location":"/DQuagliarella","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/DQuagliarella","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Multiobjective Optimization"]}" data-trace="false" data-dom-id="Pill-react-component-38dd5ff3-5e81-4df8-a238-3c8e4ab219a4"></div> <div id="Pill-react-component-38dd5ff3-5e81-4df8-a238-3c8e4ab219a4"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by D. Quagliarella</h3></div><div class="js-work-strip profile--work_container" data-work-id="94108327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/94108327/A_conservative_sliding_mesh_coupling_procedure_for_U_RANS_flow_simulations"><img alt="Research paper thumbnail of A conservative sliding mesh coupling procedure for U-RANS flow simulations" class="work-thumbnail" src="https://attachments.academia-assets.com/96658480/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/94108327/A_conservative_sliding_mesh_coupling_procedure_for_U_RANS_flow_simulations">A conservative sliding mesh coupling procedure for U-RANS flow simulations</a></div><div class="wp-workCard_item"><span>Aircraft Engineering and Aerospace Technology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose-Simulate unsteady flows with surfaces in relative motion using a multi-block structured f...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose-Simulate unsteady flows with surfaces in relative motion using a multi-block structured flow solver. Design/methodology/approach-A procedure for simulating unsteady flows with surfaces in relative motion was developed, based upon a structured multi-block U-RANS flow solver1. Meshes produced in zones of the flow field with different rotation speed are connected by sliding boundaries. The procedure developed guarantees that the flux conservation properties of the original scheme are maintained across the sliding boundaries during the rotation at every time step. Findings-The solver turns out to be very efficient, allowing computation in scalar mode with single core processors as well as in parallel. It was tested by simulating the unsteady flow on a prop fan configuration with two counter-rotating rotors. The comparison of results and performances with respect to an existing commercial flow solver (unstructured) are reported. Originality/value-This paper fulfils an identified need to allow for efficient unsteady flow computations (structured solver) with different bodies in relative motion.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb0f2e2d1717da02750661bc1a319ff4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96658480,"asset_id":94108327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96658480/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108327]").text(description); $(".js-view-count[data-work-id=94108327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bb0f2e2d1717da02750661bc1a319ff4" } } $('.js-work-strip[data-work-id=94108327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108327,"title":"A conservative sliding mesh coupling procedure for U-RANS flow simulations","internal_url":"https://www.academia.edu/94108327/A_conservative_sliding_mesh_coupling_procedure_for_U_RANS_flow_simulations","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":96658480,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/96658480/thumbnails/1.jpg","file_name":"8ab70fae7f38467ff0d4830c6ad259e46650.pdf","download_url":"https://www.academia.edu/attachments/96658480/download_file","bulk_download_file_name":"A_conservative_sliding_mesh_coupling_pro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/96658480/8ab70fae7f38467ff0d4830c6ad259e46650-libre.pdf?1672603645=\u0026response-content-disposition=attachment%3B+filename%3DA_conservative_sliding_mesh_coupling_pro.pdf\u0026Expires=1740509474\u0026Signature=dSdhhM2QSEVmuLLoG01b9ROds4oTgrEAZ5Dkt-VrwlxbAmijMWg5JChbhfkTkv19y5swkgaF07219RERi8VB-JjCqPwxYyxE1ccw-ZLu6sKN3YIn8WiARjwjG7s10GG~BbNdzi6kzTeBkGcmR4LNUBfHyJfX3~z1nCVJ8ctEOI-MrNEG3UmAbTyCjyxmL8Ezvy6d~0-ZmxRy1zZXuvB-Xl~fJCH9bRIdMbLmV1y6fjhp7Y10x7-n1MyX5fY5HgwbzHC0~bUiH6-JiqciNfE1sueYtpaVNZ0cvz1jojmFOJaOPBikAubW1lJjtcWDjmOvGXBkyBXhMHqP~IUzaMtS0A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108322"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108322/Multi_Objective_Multi_Point_Shape_And_Setting_High_Lift_System_Optimization_By_Means_Of_Genetic_Algorithm_And_2D_Navier_Stokes_Equations"><img alt="Research paper thumbnail of Multi-Objective/Multi-Point Shape And Setting High-Lift System Optimization By Means Of Genetic Algorithm And 2D Navier-Stokes Equations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108322/Multi_Objective_Multi_Point_Shape_And_Setting_High_Lift_System_Optimization_By_Means_Of_Genetic_Algorithm_And_2D_Navier_Stokes_Equations">Multi-Objective/Multi-Point Shape And Setting High-Lift System Optimization By Means Of Genetic Algorithm And 2D Navier-Stokes Equations</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">High-lift systems optimization is a challenging problem in aerospace industry, as it is often cha...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">High-lift systems optimization is a challenging problem in aerospace industry, as it is often characterized by multiple and often conflicting requirements in terms of objectives, constraints and design points. Due to its nature, high-lift system designers must deal with multidisciplinary problems, often of stiff nature. In this paper a realistic high-lift optimization problem is defined and solved via a genetic algorithm coupled to a Navier-Stokes (RANS) solver. Specifically, a 3-element airfoil optimization procedure is presented and applied to a multi-objective/multi-point problem, where both shape and settings of a multi-component airfoil are optimized with respect to both take-off and landing performance, by also including several aerodynamic, airworthiness and manufacturing constraints. Results are discussed in terms of both quality and reliability with reference to industrial requirements.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108322"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108322"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108322; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108322]").text(description); $(".js-view-count[data-work-id=94108322]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108322; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108322']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108322]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108322,"title":"Multi-Objective/Multi-Point Shape And Setting High-Lift System Optimization By Means Of Genetic Algorithm And 2D Navier-Stokes Equations","internal_url":"https://www.academia.edu/94108322/Multi_Objective_Multi_Point_Shape_And_Setting_High_Lift_System_Optimization_By_Means_Of_Genetic_Algorithm_And_2D_Navier_Stokes_Equations","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108320"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108320/Design_and_optimization_of_a_transonic_natural_laminar_flow_airfoil"><img alt="Research paper thumbnail of Design and optimization of a transonic natural laminar flow airfoil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108320/Design_and_optimization_of_a_transonic_natural_laminar_flow_airfoil">Design and optimization of a transonic natural laminar flow airfoil</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108320; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108320]").text(description); $(".js-view-count[data-work-id=94108320]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108320; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108320']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108320]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108320,"title":"Design and optimization of a transonic natural laminar flow airfoil","internal_url":"https://www.academia.edu/94108320/Design_and_optimization_of_a_transonic_natural_laminar_flow_airfoil","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108314"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/94108314/Aerodynamic_Shape_Design_Using_Hybrid_Evolutionary_Computation_and_Fitness_Approximation"><img alt="Research paper thumbnail of Aerodynamic Shape Design Using Hybrid Evolutionary Computation and Fitness Approximation" class="work-thumbnail" src="https://attachments.academia-assets.com/96658479/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/94108314/Aerodynamic_Shape_Design_Using_Hybrid_Evolutionary_Computation_and_Fitness_Approximation">Aerodynamic Shape Design Using Hybrid Evolutionary Computation and Fitness Approximation</a></div><div class="wp-workCard_item"><span>AIAA 1st Intelligent Systems Technical Conference</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose-The purpose of this paper is to propose an accurate and efficient technique for computing...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose-The purpose of this paper is to propose an accurate and efficient technique for computing flow sensitivities by finite differences of perturbed flow fields. It relies on computing the perturbed flows on coarser grid levels only: to achieve the same fine-grid accuracy, the approximate value of the relative local truncation error between coarser and finest grids unperturbed flow fields, provided by a standard multigrid method, is added to the coarse grid equations. The gradient computation is introduced in a hybrid genetic algorithm (HGA) that takes advantage of the presented method to accelerate the gradient-based search. An application to a classical transonic airfoil design is reported. Design/methodology/approach-Genetic optimization algorithm hybridized with classical gradient-based search techniques; usage of fast and accurate gradient computation technique. Findings-The new variant of the prolongation operator with weighting terms based on the volume of grid cells improves the accuracy of the MAFD method for turbulent viscous flows. The hybrid GA is capable to efficiently handle and compensate for the error that, although very limited, is present in the multigrid-aided finite-difference (MAFD) gradient evaluation method. Research limitations/implications-The proposed new variants of HGA, while outperforming the simple genetic algorithm, still require tuning and validation to further improve performance. Practical implications-Significant speedup of CFD-based optimization loops.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="af3e8b0513a5c5cb86cecf33a7e1b9ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96658479,"asset_id":94108314,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96658479/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108314"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108314"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108314; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108314]").text(description); $(".js-view-count[data-work-id=94108314]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108314; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108314']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "af3e8b0513a5c5cb86cecf33a7e1b9ca" } } $('.js-work-strip[data-work-id=94108314]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108314,"title":"Aerodynamic Shape Design Using Hybrid Evolutionary Computation and Fitness Approximation","internal_url":"https://www.academia.edu/94108314/Aerodynamic_Shape_Design_Using_Hybrid_Evolutionary_Computation_and_Fitness_Approximation","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":96658479,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/96658479/thumbnails/1.jpg","file_name":"ec-02-2013-005820230101-1-1hlk4c5.pdf","download_url":"https://www.academia.edu/attachments/96658479/download_file","bulk_download_file_name":"Aerodynamic_Shape_Design_Using_Hybrid_Ev.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/96658479/ec-02-2013-005820230101-1-1hlk4c5-libre.pdf?1672603666=\u0026response-content-disposition=attachment%3B+filename%3DAerodynamic_Shape_Design_Using_Hybrid_Ev.pdf\u0026Expires=1740509474\u0026Signature=WE9oQRTkxShqt6uqGnX90KlVbOlWObNBL5dS6eZ91gWBa5EMrAiiBdlND~eKoivat1a5T2-mGeO3gDVhlMPRqLg5S~vhK9seoadQxS811D9THQAuSUXSNAs5wTNdfm3XtY7IAiHckQ9Ssk9Kzgl6Cw7-h85wEP6VKg61UdETczAWT~ATBqb0SszRccG85Ksssb~Nut4QxU2qsLST-vZW3r9i3POIa4lboaUYTh1QRSKFtgkleS42sbbNpJc70Fc~Wg0MYgrszBAl5u2d2pdAmH6zSPtgSxGWI9dro~tQ9TIwvq7iua2KVP0pEqLyqaA4AyF8wl7f1j5tP-2IXG2cTQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108310"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/94108310/Advanced_High_Lift_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_within_the_European_Project_EUROLIFT_II"><img alt="Research paper thumbnail of Advanced High-Lift Design by Numerical Methods and Wind Tunnel Verification within the European Project EUROLIFT II" class="work-thumbnail" src="https://attachments.academia-assets.com/96658506/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/94108310/Advanced_High_Lift_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_within_the_European_Project_EUROLIFT_II">Advanced High-Lift Design by Numerical Methods and Wind Tunnel Verification within the European Project EUROLIFT II</a></div><div class="wp-workCard_item"><span>25th AIAA Applied Aerodynamics Conference</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The design activity within the European 6 th framework project EUROLIFT II is targeted towards an...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The design activity within the European 6 th framework project EUROLIFT II is targeted towards an improvement of the takeoff performance of a generic transport aircraft configuration by a redesign of the trailing edge flap. The involved partners applied different optimization strategies as well as different types of flow solvers in order to cover a wide range of possible approaches for aerodynamic design optimization. The optimization results obtained by the different partners have been cross-calculated in order to eliminate solver dependencies and to identify the best obtained design. The final selected design has been applied to the wind tunnel model and the test in the European Transonic Wind Tunnel (ETW) at high Reynolds number confirms the predicted improvements.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c9d3c80ff0ea1e0003c34c89809b522" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96658506,"asset_id":94108310,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96658506/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108310"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108310"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108310; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108310]").text(description); $(".js-view-count[data-work-id=94108310]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108310; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108310']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8c9d3c80ff0ea1e0003c34c89809b522" } } $('.js-work-strip[data-work-id=94108310]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108310,"title":"Advanced High-Lift Design by Numerical Methods and Wind Tunnel Verification within the European Project EUROLIFT II","internal_url":"https://www.academia.edu/94108310/Advanced_High_Lift_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_within_the_European_Project_EUROLIFT_II","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":96658506,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/96658506/thumbnails/1.jpg","file_name":"e31d0e5b841f0eedb322251394809436f0c7.pdf","download_url":"https://www.academia.edu/attachments/96658506/download_file","bulk_download_file_name":"Advanced_High_Lift_Design_by_Numerical_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/96658506/e31d0e5b841f0eedb322251394809436f0c7-libre.pdf?1672603666=\u0026response-content-disposition=attachment%3B+filename%3DAdvanced_High_Lift_Design_by_Numerical_M.pdf\u0026Expires=1740509474\u0026Signature=V7sFGxCvls8zRoXaAc4PSFnJKpvPcFNe7ejCfZfXoKrCmU3rHJkifGlR5L5MvZV-GXp8QbkxyrwXRxRRcxq6N8H23cFV1ipjwKf6~g1ZYYnEzxMQ~B~9Pd9TjmLj-pxOpzugMRz2h5evBl7W3AD5iyAa1XHCX0AQzDLwk55rGZC-8Qygyk2B870mM6UPOq--aTXx1pGUPDMBsJFhbcg~2PiJW9K9MpXkcEFoj0uy5MN6woDQcynZX0DySVjYnJ8I7FId3lQ0Ko9B62Xhx46V2a~oPv7EViOYnPJlBZfwXGQErH0~2kmUXAKUUKY-eeFzLoguJNeaHMdKMojBVlLEpg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108306"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108306/Design_and_Test_of_the_UW_5006_Transonic_Natural_Laminar_Flow_Wing"><img alt="Research paper thumbnail of Design and Test of the UW-5006 Transonic Natural-Laminar-Flow Wing" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108306/Design_and_Test_of_the_UW_5006_Transonic_Natural_Laminar_Flow_Wing">Design and Test of the UW-5006 Transonic Natural-Laminar-Flow Wing</a></div><div class="wp-workCard_item"><span>Journal of Aircraft</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A transonic natural-laminar-flow wing design procedure has been set up, integrating a pa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A transonic natural-laminar-flow wing design procedure has been set up, integrating a parametric geometry model with several analysis tools. A direct design strategy has been applied and three levels of aerodynamic analysis have been used: a full-potential method (with which to rapidly iterate to obtain the target pressure distribution), an Euler solution coupled with a boundary-layer solver and a semi-empirical stability analysis method (for an intermediate evaluation that is able to include the laminar flow extension), and a full Navier-Stokes analysis with fixed transition (for a final verification of the design quality). A transonic natural-laminar-flow wing suitable for business aviation able to perform at least 40% of laminar flow in cruise with acceptable wave drag at Mach 0.78 has been designed and tested at flight Reynolds numbers. The laminar flow has been verified by infrared cameras.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108306"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108306"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108306; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108306]").text(description); $(".js-view-count[data-work-id=94108306]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108306; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108306']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108306]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108306,"title":"Design and Test of the UW-5006 Transonic Natural-Laminar-Flow Wing","internal_url":"https://www.academia.edu/94108306/Design_and_Test_of_the_UW_5006_Transonic_Natural_Laminar_Flow_Wing","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108303"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108303/Advanced_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_Within_European_High_Lift_Program"><img alt="Research paper thumbnail of Advanced Design by Numerical Methods and Wind-Tunnel Verification Within European High-Lift Program" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108303/Advanced_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_Within_European_High_Lift_Program">Advanced Design by Numerical Methods and Wind-Tunnel Verification Within European High-Lift Program</a></div><div class="wp-workCard_item"><span>Journal of Aircraft</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The design activity within the European 6th framework project EUROLIFT II is targeted to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The design activity within the European 6th framework project EUROLIFT II is targeted towards an improvement of the take-off performance of a generic transport aircraft configuration by a redesign of the trailing edge flap. The involved partners applied different optimization strategies as well as different types of flow solvers in order to cover a wide range of possible approaches for aerodynamic design optimization. The optimization results obtained by the different partners have been cross-calculated in order to eliminate solver dependencies and to identify the best obtained design. The final selected design has been applied to the wind tunnel model and the test in the European Transonic Wind Tunnel (ETW) at high Reynolds number confirms the predicted improvements.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108303"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108303"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108303; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108303]").text(description); $(".js-view-count[data-work-id=94108303]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108303; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108303']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108303]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108303,"title":"Advanced Design by Numerical Methods and Wind-Tunnel Verification Within European High-Lift Program","internal_url":"https://www.academia.edu/94108303/Advanced_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_Within_European_High_Lift_Program","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108299"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108299/A_probabilistic_non_dominated_sorting_GA_for_optimization_under_uncertainty"><img alt="Research paper thumbnail of A probabilistic non-dominated sorting GA for optimization under uncertainty" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108299/A_probabilistic_non_dominated_sorting_GA_for_optimization_under_uncertainty">A probabilistic non-dominated sorting GA for optimization under uncertainty</a></div><div class="wp-workCard_item"><span>Engineering Computations</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose – A probabilistic non-dominated sorting genetic algorithm (P-NSGA) for multi-objective op...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose – A probabilistic non-dominated sorting genetic algorithm (P-NSGA) for multi-objective optimization under uncertainty is presented. The purpose of this algorithm is to create a tight coupling between the optimization and uncertainty procedures, use all of the possible probabilistic information to drive the optimizer, and leverage high-performance parallel computing. Design/methodology/approach – This algorithm is a generalization of a classical genetic algorithm for multi-objective optimization (NSGA-II) by Deb et al. The proposed algorithm relies on the use of all possible information in the probabilistic domain summarized by the cumulative distribution functions (CDFs) of the objective functions. Several analytic test functions are used to benchmark this algorithm, but only the results of the Fonseca-Fleming test function are shown. An industrial application is presented to show that P-NSGA can be used for multi-objective shape optimization of a Formula 1 tire brake duct, ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108299"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108299"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108299; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108299]").text(description); $(".js-view-count[data-work-id=94108299]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108299; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108299']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108299]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108299,"title":"A probabilistic non-dominated sorting GA for optimization under uncertainty","internal_url":"https://www.academia.edu/94108299/A_probabilistic_non_dominated_sorting_GA_for_optimization_under_uncertainty","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108144"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108144/Robustness_Criteria_In_Optimization_Under_Uncertainty"><img alt="Research paper thumbnail of Robustness Criteria In Optimization Under Uncertainty" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108144/Robustness_Criteria_In_Optimization_Under_Uncertainty">Robustness Criteria In Optimization Under Uncertainty</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Robust Optimization is an extension of conventional optimization pro-cedures and aims at taking i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Robust Optimization is an extension of conventional optimization pro-cedures and aims at taking in account uncertainty in the design procedure. In this work we propose a novel framework characterized by the use of all the possible infor-mations in the probabilistic domain, namely the Cumulative Distribution Function (CDF), which represents the identity card of a design analyzed under uncertainty. Due to this peculiarity this novel approach set itself apart from the conventional methods which rely on the use of few statistical moments as deterministic attributes in replacing the objectives of the optimization process. Additionally the use of an area metric leads to a multi-objective methodology which allows an a posteriori se-lection of the candidate design based on risk/opportunity criteria of the designer. An extension to multi-objective optimization in presence of uncertainty is presented in the last section of this work. The concept of robust optimization is intuitively connected...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108144"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108144"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108144; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108144]").text(description); $(".js-view-count[data-work-id=94108144]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108144; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108144']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108144]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108144,"title":"Robustness Criteria In Optimization Under Uncertainty","internal_url":"https://www.academia.edu/94108144/Robustness_Criteria_In_Optimization_Under_Uncertainty","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76365768"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76365768/High_Lift_Devices_Topology_Optimisation_using_Structured_Chromosome_Genetic_Algorithm"><img alt="Research paper thumbnail of High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/84096111/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76365768/High_Lift_Devices_Topology_Optimisation_using_Structured_Chromosome_Genetic_Algorithm">High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm</a></div><div class="wp-workCard_item"><span>2020 IEEE Congress on Evolutionary Computation (CEC)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configur...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="26b59bc389669f5c960653d307aeaac7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84096111,"asset_id":76365768,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84096111/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76365768"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76365768"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76365768; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76365768]").text(description); $(".js-view-count[data-work-id=76365768]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76365768; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76365768']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "26b59bc389669f5c960653d307aeaac7" } } $('.js-work-strip[data-work-id=76365768]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76365768,"title":"High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm","internal_url":"https://www.academia.edu/76365768/High_Lift_Devices_Topology_Optimisation_using_Structured_Chromosome_Genetic_Algorithm","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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The applications here presented regard both planform and wing section optimization. It is shown how both geometric and aerodynamic constraints can be taken into account, and how the multiobjective approach to optimization can be an effective way to handle conflicting design criteria. An interpolation technique allowing a better approximation of Pareto fronts is described. Two possible ways of improving the computational efficiency of the genetic algorithm, namely a parallel implementation of the code and a hybrid optimization approach, are presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567832"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567832"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567832; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567832]").text(description); $(".js-view-count[data-work-id=64567832]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567832; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567832']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567832]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567832,"title":"A Multiobjective Approach to Transonic Wing Design by Means of Genetic Algorithms","internal_url":"https://www.academia.edu/64567832/A_Multiobjective_Approach_to_Transonic_Wing_Design_by_Means_of_Genetic_Algorithms","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567828"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/64567828/Adaptive_Sampling_Strategies_for_Surrogate_Based_Aerodynamic_Optimization"><img alt="Research paper thumbnail of Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/76545135/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/64567828/Adaptive_Sampling_Strategies_for_Surrogate_Based_Aerodynamic_Optimization">Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization</a></div><div class="wp-workCard_item"><span>Springer Tracts in Mechanical Engineering</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6a0f5401f12c0012d251111f522b914a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76545135,"asset_id":64567828,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76545135/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567828"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567828"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567828; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567828]").text(description); $(".js-view-count[data-work-id=64567828]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567828; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567828']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6a0f5401f12c0012d251111f522b914a" } } $('.js-work-strip[data-work-id=64567828]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567828,"title":"Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization","internal_url":"https://www.academia.edu/64567828/Adaptive_Sampling_Strategies_for_Surrogate_Based_Aerodynamic_Optimization","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":76545135,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/76545135/thumbnails/1.jpg","file_name":"a2040.pdf","download_url":"https://www.academia.edu/attachments/76545135/download_file","bulk_download_file_name":"Adaptive_Sampling_Strategies_for_Surroga.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/76545135/a2040-libre.pdf?1639674717=\u0026response-content-disposition=attachment%3B+filename%3DAdaptive_Sampling_Strategies_for_Surroga.pdf\u0026Expires=1740509474\u0026Signature=KJ65rylN8Ga-3skoTTkRbLAQhs~YwTPS7u2GD~O~zuONRgpHZ-Nuv-WynSH4nwTsZVQSxG3k2NgQ0hUptNf2uv1tLHdKfjlcMnk9yzAREuYWBXTJ5QWT~J-hpKsjwK8HOEIPm6qRefI6wkD7AAFn~LXl2rscum0XBNqBl3AYxGEvAEIuhzw5EThTDhqSuKcUEGkux0SqIKYj~WQdSalChTUYI4veferWMHIRJKYnTVG1J9R1yltAozRRLS~IEPeDuQ0wqBAcfq~910G~YdOY0puUyPtGfehfet3tE0ld1oVpgEeuDatwOV~6YqYj8L3boaVmLjIr9UAjgEYs7rpCQQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567826"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567826/GAs_for_aerodynamic_shape_design_II"><img alt="Research paper thumbnail of GAs for aerodynamic shape design II" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567826/GAs_for_aerodynamic_shape_design_II">GAs for aerodynamic shape design II</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The lecture focuses on multi-objective genetic algorithms with hybrid capabilities, and on their ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The lecture focuses on multi-objective genetic algorithms with hybrid capabilities, and on their application to multi-criteria design problems. A short introduction to multipoint aerodynamic shape design is given, and the advantages of a multi-objective optimization approach to this problem are outlined. The introduction of basic concepts of multi-objective optimization is followed by the description of a multiple objective genetic algorithm. Some techniques for efficiency improvement are introduced; in particular, the gradient based technique for hybrid optimization is extended to multi-objective design problems. Application examples are reported related both to single and multi-element airfoil design in high-lift conditions, and to transonic wing design.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567826"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567826"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567826; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567826]").text(description); $(".js-view-count[data-work-id=64567826]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567826; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567826']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567826]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567826,"title":"GAs for aerodynamic shape design II","internal_url":"https://www.academia.edu/64567826/GAs_for_aerodynamic_shape_design_II","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567825"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567825/Surrogate_based_Aerodynamic_Optimization_via_a_Zonal_POD_Model"><img alt="Research paper thumbnail of Surrogate-based Aerodynamic Optimization via a Zonal POD Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567825/Surrogate_based_Aerodynamic_Optimization_via_a_Zonal_POD_Model">Surrogate-based Aerodynamic Optimization via a Zonal POD Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A computational methodology is proposed to exploit a reduced order model (ROM) as surrogate evalu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A computational methodology is proposed to exploit a reduced order model (ROM) as surrogate evaluator in CFD-based aerodynamic design. The model is based on the Proper Orthogonal Decomposition (POD) of an ensemble of CFD solutions. Two detached domains are identified: the full order domain (FOM), where the high-fidelity CFD solver is used, and the reduced order domain, where the solution is predicted via the POD surrogate model. The reduced order model is integrated in an evolutionary optimization framework and used as fitness evaluator to improve the aerodynamic performances of a two-dimensional airfoil. Finally, the performances of the surrogate-based shape optimization (SBSO) are compared to the efficiency of a meta-model assisted optimization and to the accuracy of a plain optimization, where, instead, each aerodynamic evaluation is performed with the high-fidelity model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567825"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567825"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567825; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567825]").text(description); $(".js-view-count[data-work-id=64567825]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567825; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567825']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567825]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567825,"title":"Surrogate-based Aerodynamic Optimization via a Zonal POD Model","internal_url":"https://www.academia.edu/64567825/Surrogate_based_Aerodynamic_Optimization_via_a_Zonal_POD_Model","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567823"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567823/Wing_shape_optimization_by_surrogate_modeling"><img alt="Research paper thumbnail of Wing shape optimization by surrogate modeling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567823/Wing_shape_optimization_by_surrogate_modeling">Wing shape optimization by surrogate modeling</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper proposes the application of surrogate modelling to the evolutionary optimization of a w...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper proposes the application of surrogate modelling to the evolutionary optimization of a wing shape in transonic conditions. A physics-based approach using the Proper Orthogonal Decomposition (POD) is introduced as meta-model to quickly and accurately evaluate the objective and constraint functions. This method is compared to an high-fidelity optimization, where a CFD solver is used to compute the fitness function, and to the Efficient Global Optimization (EGO), based on a Kriging surrogate with auxiliary function maximization, in order to highlight its advantages and drawbacks. A critical analysis of the obtained results is presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567823"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567823"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567823; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567823]").text(description); $(".js-view-count[data-work-id=64567823]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567823; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567823']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567823]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567823,"title":"Wing shape optimization by surrogate modeling","internal_url":"https://www.academia.edu/64567823/Wing_shape_optimization_by_surrogate_modeling","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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It aims at bringing together specialists from Universities, Research I nstitutions and Industries developing or applying Evolutionary and Detenninistic Methods in opti1nization of design and emphasizing on industrial and societal applications. This series of conferences was originally launched by the European Thematic Network INGENET. EUROGEN&#39;l l is an ECCOMAS Thematic Conference. Starting with EUROGEN 2009, ERCOFfAC, represented by its Special Interest Group (SIG) on Design Opti mization, is the ECCOMAS associate of this ECCOMAS Thematic Conference. The conference includes invited lectures, contributed lectures and five special technological sessions: • Multi-disciplinary Design...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567821"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567821"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567821; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567821]").text(description); $(".js-view-count[data-work-id=64567821]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567821; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567821']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567821]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567821,"title":"EUROGEN 2011 PROCEEDINGS --- Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems","internal_url":"https://www.academia.edu/64567821/EUROGEN_2011_PROCEEDINGS_Evolutionary_and_Deterministic_Methods_for_Design_Optimization_and_Control_with_Applications_to_Industrial_and_Societal_Problems","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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Meshes produced in zones of the flow field with different rotation speed are connected by sliding boundaries. The procedure developed guarantees that the flux conservation properties of the original scheme are maintained across the sliding boundaries during the rotation at every time step. The solver turns out to be very efficient, allowing computation in scalar mode with single core processors as well as in parallel. It was tested by simulating the unsteady flow on a prop fan configuration with two counter-rotating rotors. The comparison of results and performances with respect to an existing commercial flow solver (unstructured) are reported.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567818"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567818"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567818; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567818]").text(description); $(".js-view-count[data-work-id=64567818]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567818; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567818']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567818]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567818,"title":"Flow simulation of a prop-fan configuration based upon structured mesh with sliding boundaries","internal_url":"https://www.academia.edu/64567818/Flow_simulation_of_a_prop_fan_configuration_based_upon_structured_mesh_with_sliding_boundaries","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3866842" id="papers"><div class="js-work-strip profile--work_container" data-work-id="94108327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/94108327/A_conservative_sliding_mesh_coupling_procedure_for_U_RANS_flow_simulations"><img alt="Research paper thumbnail of A conservative sliding mesh coupling procedure for U-RANS flow simulations" class="work-thumbnail" src="https://attachments.academia-assets.com/96658480/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/94108327/A_conservative_sliding_mesh_coupling_procedure_for_U_RANS_flow_simulations">A conservative sliding mesh coupling procedure for U-RANS flow simulations</a></div><div class="wp-workCard_item"><span>Aircraft Engineering and Aerospace Technology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose-Simulate unsteady flows with surfaces in relative motion using a multi-block structured f...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose-Simulate unsteady flows with surfaces in relative motion using a multi-block structured flow solver. Design/methodology/approach-A procedure for simulating unsteady flows with surfaces in relative motion was developed, based upon a structured multi-block U-RANS flow solver1. Meshes produced in zones of the flow field with different rotation speed are connected by sliding boundaries. The procedure developed guarantees that the flux conservation properties of the original scheme are maintained across the sliding boundaries during the rotation at every time step. Findings-The solver turns out to be very efficient, allowing computation in scalar mode with single core processors as well as in parallel. It was tested by simulating the unsteady flow on a prop fan configuration with two counter-rotating rotors. The comparison of results and performances with respect to an existing commercial flow solver (unstructured) are reported. Originality/value-This paper fulfils an identified need to allow for efficient unsteady flow computations (structured solver) with different bodies in relative motion.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb0f2e2d1717da02750661bc1a319ff4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96658480,"asset_id":94108327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96658480/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108327]").text(description); $(".js-view-count[data-work-id=94108327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bb0f2e2d1717da02750661bc1a319ff4" } } $('.js-work-strip[data-work-id=94108327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108327,"title":"A conservative sliding mesh coupling procedure for U-RANS flow simulations","internal_url":"https://www.academia.edu/94108327/A_conservative_sliding_mesh_coupling_procedure_for_U_RANS_flow_simulations","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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Due to its nature, high-lift system designers must deal with multidisciplinary problems, often of stiff nature. In this paper a realistic high-lift optimization problem is defined and solved via a genetic algorithm coupled to a Navier-Stokes (RANS) solver. Specifically, a 3-element airfoil optimization procedure is presented and applied to a multi-objective/multi-point problem, where both shape and settings of a multi-component airfoil are optimized with respect to both take-off and landing performance, by also including several aerodynamic, airworthiness and manufacturing constraints. Results are discussed in terms of both quality and reliability with reference to industrial requirements.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108322"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108322"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108322; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108322]").text(description); $(".js-view-count[data-work-id=94108322]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108322; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108322']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108322]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108322,"title":"Multi-Objective/Multi-Point Shape And Setting High-Lift System Optimization By Means Of Genetic Algorithm And 2D Navier-Stokes Equations","internal_url":"https://www.academia.edu/94108322/Multi_Objective_Multi_Point_Shape_And_Setting_High_Lift_System_Optimization_By_Means_Of_Genetic_Algorithm_And_2D_Navier_Stokes_Equations","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108320"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108320/Design_and_optimization_of_a_transonic_natural_laminar_flow_airfoil"><img alt="Research paper thumbnail of Design and optimization of a transonic natural laminar flow airfoil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108320/Design_and_optimization_of_a_transonic_natural_laminar_flow_airfoil">Design and optimization of a transonic natural laminar flow airfoil</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108320; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108320]").text(description); $(".js-view-count[data-work-id=94108320]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108320; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108320']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108320]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108320,"title":"Design and optimization of a transonic natural laminar flow airfoil","internal_url":"https://www.academia.edu/94108320/Design_and_optimization_of_a_transonic_natural_laminar_flow_airfoil","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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It relies on computing the perturbed flows on coarser grid levels only: to achieve the same fine-grid accuracy, the approximate value of the relative local truncation error between coarser and finest grids unperturbed flow fields, provided by a standard multigrid method, is added to the coarse grid equations. The gradient computation is introduced in a hybrid genetic algorithm (HGA) that takes advantage of the presented method to accelerate the gradient-based search. An application to a classical transonic airfoil design is reported. Design/methodology/approach-Genetic optimization algorithm hybridized with classical gradient-based search techniques; usage of fast and accurate gradient computation technique. Findings-The new variant of the prolongation operator with weighting terms based on the volume of grid cells improves the accuracy of the MAFD method for turbulent viscous flows. The hybrid GA is capable to efficiently handle and compensate for the error that, although very limited, is present in the multigrid-aided finite-difference (MAFD) gradient evaluation method. Research limitations/implications-The proposed new variants of HGA, while outperforming the simple genetic algorithm, still require tuning and validation to further improve performance. Practical implications-Significant speedup of CFD-based optimization loops.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="af3e8b0513a5c5cb86cecf33a7e1b9ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96658479,"asset_id":94108314,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96658479/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108314"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108314"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108314; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108314]").text(description); $(".js-view-count[data-work-id=94108314]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108314; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108314']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "af3e8b0513a5c5cb86cecf33a7e1b9ca" } } $('.js-work-strip[data-work-id=94108314]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108314,"title":"Aerodynamic Shape Design Using Hybrid Evolutionary Computation and Fitness Approximation","internal_url":"https://www.academia.edu/94108314/Aerodynamic_Shape_Design_Using_Hybrid_Evolutionary_Computation_and_Fitness_Approximation","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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The involved partners applied different optimization strategies as well as different types of flow solvers in order to cover a wide range of possible approaches for aerodynamic design optimization. The optimization results obtained by the different partners have been cross-calculated in order to eliminate solver dependencies and to identify the best obtained design. The final selected design has been applied to the wind tunnel model and the test in the European Transonic Wind Tunnel (ETW) at high Reynolds number confirms the predicted improvements.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c9d3c80ff0ea1e0003c34c89809b522" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96658506,"asset_id":94108310,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96658506/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108310"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108310"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108310; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108310]").text(description); $(".js-view-count[data-work-id=94108310]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108310; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108310']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8c9d3c80ff0ea1e0003c34c89809b522" } } $('.js-work-strip[data-work-id=94108310]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108310,"title":"Advanced High-Lift Design by Numerical Methods and Wind Tunnel Verification within the European Project EUROLIFT II","internal_url":"https://www.academia.edu/94108310/Advanced_High_Lift_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_within_the_European_Project_EUROLIFT_II","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":96658506,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/96658506/thumbnails/1.jpg","file_name":"e31d0e5b841f0eedb322251394809436f0c7.pdf","download_url":"https://www.academia.edu/attachments/96658506/download_file","bulk_download_file_name":"Advanced_High_Lift_Design_by_Numerical_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/96658506/e31d0e5b841f0eedb322251394809436f0c7-libre.pdf?1672603666=\u0026response-content-disposition=attachment%3B+filename%3DAdvanced_High_Lift_Design_by_Numerical_M.pdf\u0026Expires=1740509474\u0026Signature=V7sFGxCvls8zRoXaAc4PSFnJKpvPcFNe7ejCfZfXoKrCmU3rHJkifGlR5L5MvZV-GXp8QbkxyrwXRxRRcxq6N8H23cFV1ipjwKf6~g1ZYYnEzxMQ~B~9Pd9TjmLj-pxOpzugMRz2h5evBl7W3AD5iyAa1XHCX0AQzDLwk55rGZC-8Qygyk2B870mM6UPOq--aTXx1pGUPDMBsJFhbcg~2PiJW9K9MpXkcEFoj0uy5MN6woDQcynZX0DySVjYnJ8I7FId3lQ0Ko9B62Xhx46V2a~oPv7EViOYnPJlBZfwXGQErH0~2kmUXAKUUKY-eeFzLoguJNeaHMdKMojBVlLEpg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108306"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108306/Design_and_Test_of_the_UW_5006_Transonic_Natural_Laminar_Flow_Wing"><img alt="Research paper thumbnail of Design and Test of the UW-5006 Transonic Natural-Laminar-Flow Wing" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108306/Design_and_Test_of_the_UW_5006_Transonic_Natural_Laminar_Flow_Wing">Design and Test of the UW-5006 Transonic Natural-Laminar-Flow Wing</a></div><div class="wp-workCard_item"><span>Journal of Aircraft</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A transonic natural-laminar-flow wing design procedure has been set up, integrating a pa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A transonic natural-laminar-flow wing design procedure has been set up, integrating a parametric geometry model with several analysis tools. A direct design strategy has been applied and three levels of aerodynamic analysis have been used: a full-potential method (with which to rapidly iterate to obtain the target pressure distribution), an Euler solution coupled with a boundary-layer solver and a semi-empirical stability analysis method (for an intermediate evaluation that is able to include the laminar flow extension), and a full Navier-Stokes analysis with fixed transition (for a final verification of the design quality). A transonic natural-laminar-flow wing suitable for business aviation able to perform at least 40% of laminar flow in cruise with acceptable wave drag at Mach 0.78 has been designed and tested at flight Reynolds numbers. The laminar flow has been verified by infrared cameras.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108306"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108306"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108306; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108306]").text(description); $(".js-view-count[data-work-id=94108306]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108306; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108306']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108306]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108306,"title":"Design and Test of the UW-5006 Transonic Natural-Laminar-Flow Wing","internal_url":"https://www.academia.edu/94108306/Design_and_Test_of_the_UW_5006_Transonic_Natural_Laminar_Flow_Wing","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108303"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108303/Advanced_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_Within_European_High_Lift_Program"><img alt="Research paper thumbnail of Advanced Design by Numerical Methods and Wind-Tunnel Verification Within European High-Lift Program" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108303/Advanced_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_Within_European_High_Lift_Program">Advanced Design by Numerical Methods and Wind-Tunnel Verification Within European High-Lift Program</a></div><div class="wp-workCard_item"><span>Journal of Aircraft</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The design activity within the European 6th framework project EUROLIFT II is targeted to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The design activity within the European 6th framework project EUROLIFT II is targeted towards an improvement of the take-off performance of a generic transport aircraft configuration by a redesign of the trailing edge flap. The involved partners applied different optimization strategies as well as different types of flow solvers in order to cover a wide range of possible approaches for aerodynamic design optimization. The optimization results obtained by the different partners have been cross-calculated in order to eliminate solver dependencies and to identify the best obtained design. The final selected design has been applied to the wind tunnel model and the test in the European Transonic Wind Tunnel (ETW) at high Reynolds number confirms the predicted improvements.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108303"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108303"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108303; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108303]").text(description); $(".js-view-count[data-work-id=94108303]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108303; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108303']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108303]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108303,"title":"Advanced Design by Numerical Methods and Wind-Tunnel Verification Within European High-Lift Program","internal_url":"https://www.academia.edu/94108303/Advanced_Design_by_Numerical_Methods_and_Wind_Tunnel_Verification_Within_European_High_Lift_Program","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108299"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108299/A_probabilistic_non_dominated_sorting_GA_for_optimization_under_uncertainty"><img alt="Research paper thumbnail of A probabilistic non-dominated sorting GA for optimization under uncertainty" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108299/A_probabilistic_non_dominated_sorting_GA_for_optimization_under_uncertainty">A probabilistic non-dominated sorting GA for optimization under uncertainty</a></div><div class="wp-workCard_item"><span>Engineering Computations</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose – A probabilistic non-dominated sorting genetic algorithm (P-NSGA) for multi-objective op...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose – A probabilistic non-dominated sorting genetic algorithm (P-NSGA) for multi-objective optimization under uncertainty is presented. The purpose of this algorithm is to create a tight coupling between the optimization and uncertainty procedures, use all of the possible probabilistic information to drive the optimizer, and leverage high-performance parallel computing. Design/methodology/approach – This algorithm is a generalization of a classical genetic algorithm for multi-objective optimization (NSGA-II) by Deb et al. The proposed algorithm relies on the use of all possible information in the probabilistic domain summarized by the cumulative distribution functions (CDFs) of the objective functions. Several analytic test functions are used to benchmark this algorithm, but only the results of the Fonseca-Fleming test function are shown. An industrial application is presented to show that P-NSGA can be used for multi-objective shape optimization of a Formula 1 tire brake duct, ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108299"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108299"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108299; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108299]").text(description); $(".js-view-count[data-work-id=94108299]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108299; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108299']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108299]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108299,"title":"A probabilistic non-dominated sorting GA for optimization under uncertainty","internal_url":"https://www.academia.edu/94108299/A_probabilistic_non_dominated_sorting_GA_for_optimization_under_uncertainty","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94108144"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/94108144/Robustness_Criteria_In_Optimization_Under_Uncertainty"><img alt="Research paper thumbnail of Robustness Criteria In Optimization Under Uncertainty" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/94108144/Robustness_Criteria_In_Optimization_Under_Uncertainty">Robustness Criteria In Optimization Under Uncertainty</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Robust Optimization is an extension of conventional optimization pro-cedures and aims at taking i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Robust Optimization is an extension of conventional optimization pro-cedures and aims at taking in account uncertainty in the design procedure. In this work we propose a novel framework characterized by the use of all the possible infor-mations in the probabilistic domain, namely the Cumulative Distribution Function (CDF), which represents the identity card of a design analyzed under uncertainty. Due to this peculiarity this novel approach set itself apart from the conventional methods which rely on the use of few statistical moments as deterministic attributes in replacing the objectives of the optimization process. Additionally the use of an area metric leads to a multi-objective methodology which allows an a posteriori se-lection of the candidate design based on risk/opportunity criteria of the designer. An extension to multi-objective optimization in presence of uncertainty is presented in the last section of this work. The concept of robust optimization is intuitively connected...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="94108144"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94108144"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94108144; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94108144]").text(description); $(".js-view-count[data-work-id=94108144]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94108144; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94108144']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=94108144]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94108144,"title":"Robustness Criteria In Optimization Under Uncertainty","internal_url":"https://www.academia.edu/94108144/Robustness_Criteria_In_Optimization_Under_Uncertainty","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76365768"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76365768/High_Lift_Devices_Topology_Optimisation_using_Structured_Chromosome_Genetic_Algorithm"><img alt="Research paper thumbnail of High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/84096111/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76365768/High_Lift_Devices_Topology_Optimisation_using_Structured_Chromosome_Genetic_Algorithm">High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm</a></div><div class="wp-workCard_item"><span>2020 IEEE Congress on Evolutionary Computation (CEC)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configur...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="26b59bc389669f5c960653d307aeaac7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84096111,"asset_id":76365768,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84096111/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76365768"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76365768"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76365768; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76365768]").text(description); $(".js-view-count[data-work-id=76365768]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76365768; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76365768']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "26b59bc389669f5c960653d307aeaac7" } } $('.js-work-strip[data-work-id=76365768]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76365768,"title":"High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm","internal_url":"https://www.academia.edu/76365768/High_Lift_Devices_Topology_Optimisation_using_Structured_Chromosome_Genetic_Algorithm","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":84096111,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84096111/thumbnails/1.jpg","file_name":"gent20a.pdf","download_url":"https://www.academia.edu/attachments/84096111/download_file","bulk_download_file_name":"High_Lift_Devices_Topology_Optimisation.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84096111/gent20a-libre.pdf?1649889086=\u0026response-content-disposition=attachment%3B+filename%3DHigh_Lift_Devices_Topology_Optimisation.pdf\u0026Expires=1740509474\u0026Signature=BS6wyp7r54fYr7qKjC~yqghzN4X8P4LL6optIO4YDy~JhEG3V-ak~3xa6W7OO4gaVlY1mF95klD~jjOUuUwzf6U22eUVq9JPDfUghBkQ~OtqSb~dFqFt22G3fPMjfrTbjlU4zwsBaSa2hc6b5uYxNFdzT0bydm7L7Lo2LQ-8Oeqf~YSf8GL4R03g9rlahzp40bPqKNVFQLIrx9IHBor3VeoYG0CoYZTTJO~SA4vILJoziX5vX5yc2Zv41JR0OCoxNMw5A9m-kdsMrLLHcAWrCUZ9AlScO~ZYQnAfjMP6UvNuhSYoaUjBpdZCIrKacu1AUnCZ8~j~58cJ89w9JdSt4w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="72395460"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/72395460/Multigrid_Aided_Finite_Difference_Computation_of_Flow_Sensitivities"><img alt="Research paper thumbnail of Multigrid-Aided Finite-Difference Computation of Flow Sensitivities" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/72395460/Multigrid_Aided_Finite_Difference_Computation_of_Flow_Sensitivities">Multigrid-Aided Finite-Difference Computation of Flow Sensitivities</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="72395460"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="72395460"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72395460; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72395460]").text(description); $(".js-view-count[data-work-id=72395460]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72395460; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72395460']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=72395460]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72395460,"title":"Multigrid-Aided Finite-Difference Computation of Flow Sensitivities","internal_url":"https://www.academia.edu/72395460/Multigrid_Aided_Finite_Difference_Computation_of_Flow_Sensitivities","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567832"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567832/A_Multiobjective_Approach_to_Transonic_Wing_Design_by_Means_of_Genetic_Algorithms"><img alt="Research paper thumbnail of A Multiobjective Approach to Transonic Wing Design by Means of Genetic Algorithms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567832/A_Multiobjective_Approach_to_Transonic_Wing_Design_by_Means_of_Genetic_Algorithms">A Multiobjective Approach to Transonic Wing Design by Means of Genetic Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">: In this work a transonic wing design problem is faced by means of a multiobjective genetic algo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">: In this work a transonic wing design problem is faced by means of a multiobjective genetic algorithm, and using a full potential flow model. The applications here presented regard both planform and wing section optimization. It is shown how both geometric and aerodynamic constraints can be taken into account, and how the multiobjective approach to optimization can be an effective way to handle conflicting design criteria. An interpolation technique allowing a better approximation of Pareto fronts is described. Two possible ways of improving the computational efficiency of the genetic algorithm, namely a parallel implementation of the code and a hybrid optimization approach, are presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567832"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567832"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567832; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567832]").text(description); $(".js-view-count[data-work-id=64567832]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567832; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567832']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567832]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567832,"title":"A Multiobjective Approach to Transonic Wing Design by Means of Genetic Algorithms","internal_url":"https://www.academia.edu/64567832/A_Multiobjective_Approach_to_Transonic_Wing_Design_by_Means_of_Genetic_Algorithms","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567828"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/64567828/Adaptive_Sampling_Strategies_for_Surrogate_Based_Aerodynamic_Optimization"><img alt="Research paper thumbnail of Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/76545135/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/64567828/Adaptive_Sampling_Strategies_for_Surrogate_Based_Aerodynamic_Optimization">Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization</a></div><div class="wp-workCard_item"><span>Springer Tracts in Mechanical Engineering</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6a0f5401f12c0012d251111f522b914a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76545135,"asset_id":64567828,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76545135/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567828"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567828"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567828; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567828]").text(description); $(".js-view-count[data-work-id=64567828]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567828; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567828']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6a0f5401f12c0012d251111f522b914a" } } $('.js-work-strip[data-work-id=64567828]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567828,"title":"Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization","internal_url":"https://www.academia.edu/64567828/Adaptive_Sampling_Strategies_for_Surrogate_Based_Aerodynamic_Optimization","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[{"id":76545135,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/76545135/thumbnails/1.jpg","file_name":"a2040.pdf","download_url":"https://www.academia.edu/attachments/76545135/download_file","bulk_download_file_name":"Adaptive_Sampling_Strategies_for_Surroga.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/76545135/a2040-libre.pdf?1639674717=\u0026response-content-disposition=attachment%3B+filename%3DAdaptive_Sampling_Strategies_for_Surroga.pdf\u0026Expires=1740509474\u0026Signature=KJ65rylN8Ga-3skoTTkRbLAQhs~YwTPS7u2GD~O~zuONRgpHZ-Nuv-WynSH4nwTsZVQSxG3k2NgQ0hUptNf2uv1tLHdKfjlcMnk9yzAREuYWBXTJ5QWT~J-hpKsjwK8HOEIPm6qRefI6wkD7AAFn~LXl2rscum0XBNqBl3AYxGEvAEIuhzw5EThTDhqSuKcUEGkux0SqIKYj~WQdSalChTUYI4veferWMHIRJKYnTVG1J9R1yltAozRRLS~IEPeDuQ0wqBAcfq~910G~YdOY0puUyPtGfehfet3tE0ld1oVpgEeuDatwOV~6YqYj8L3boaVmLjIr9UAjgEYs7rpCQQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567826"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567826/GAs_for_aerodynamic_shape_design_II"><img alt="Research paper thumbnail of GAs for aerodynamic shape design II" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567826/GAs_for_aerodynamic_shape_design_II">GAs for aerodynamic shape design II</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The lecture focuses on multi-objective genetic algorithms with hybrid capabilities, and on their ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The lecture focuses on multi-objective genetic algorithms with hybrid capabilities, and on their application to multi-criteria design problems. A short introduction to multipoint aerodynamic shape design is given, and the advantages of a multi-objective optimization approach to this problem are outlined. The introduction of basic concepts of multi-objective optimization is followed by the description of a multiple objective genetic algorithm. Some techniques for efficiency improvement are introduced; in particular, the gradient based technique for hybrid optimization is extended to multi-objective design problems. Application examples are reported related both to single and multi-element airfoil design in high-lift conditions, and to transonic wing design.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567826"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567826"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567826; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567826]").text(description); $(".js-view-count[data-work-id=64567826]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567826; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567826']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567826]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567826,"title":"GAs for aerodynamic shape design II","internal_url":"https://www.academia.edu/64567826/GAs_for_aerodynamic_shape_design_II","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567825"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567825/Surrogate_based_Aerodynamic_Optimization_via_a_Zonal_POD_Model"><img alt="Research paper thumbnail of Surrogate-based Aerodynamic Optimization via a Zonal POD Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567825/Surrogate_based_Aerodynamic_Optimization_via_a_Zonal_POD_Model">Surrogate-based Aerodynamic Optimization via a Zonal POD Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A computational methodology is proposed to exploit a reduced order model (ROM) as surrogate evalu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A computational methodology is proposed to exploit a reduced order model (ROM) as surrogate evaluator in CFD-based aerodynamic design. The model is based on the Proper Orthogonal Decomposition (POD) of an ensemble of CFD solutions. Two detached domains are identified: the full order domain (FOM), where the high-fidelity CFD solver is used, and the reduced order domain, where the solution is predicted via the POD surrogate model. The reduced order model is integrated in an evolutionary optimization framework and used as fitness evaluator to improve the aerodynamic performances of a two-dimensional airfoil. Finally, the performances of the surrogate-based shape optimization (SBSO) are compared to the efficiency of a meta-model assisted optimization and to the accuracy of a plain optimization, where, instead, each aerodynamic evaluation is performed with the high-fidelity model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567825"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567825"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567825; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567825]").text(description); $(".js-view-count[data-work-id=64567825]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567825; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567825']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567825]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567825,"title":"Surrogate-based Aerodynamic Optimization via a Zonal POD Model","internal_url":"https://www.academia.edu/64567825/Surrogate_based_Aerodynamic_Optimization_via_a_Zonal_POD_Model","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567823"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567823/Wing_shape_optimization_by_surrogate_modeling"><img alt="Research paper thumbnail of Wing shape optimization by surrogate modeling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567823/Wing_shape_optimization_by_surrogate_modeling">Wing shape optimization by surrogate modeling</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper proposes the application of surrogate modelling to the evolutionary optimization of a w...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper proposes the application of surrogate modelling to the evolutionary optimization of a wing shape in transonic conditions. A physics-based approach using the Proper Orthogonal Decomposition (POD) is introduced as meta-model to quickly and accurately evaluate the objective and constraint functions. This method is compared to an high-fidelity optimization, where a CFD solver is used to compute the fitness function, and to the Efficient Global Optimization (EGO), based on a Kriging surrogate with auxiliary function maximization, in order to highlight its advantages and drawbacks. A critical analysis of the obtained results is presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567823"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567823"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567823; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567823]").text(description); $(".js-view-count[data-work-id=64567823]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567823; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567823']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567823]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567823,"title":"Wing shape optimization by surrogate modeling","internal_url":"https://www.academia.edu/64567823/Wing_shape_optimization_by_surrogate_modeling","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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It aims at bringing together specialists from Universities, Research I nstitutions and Industries developing or applying Evolutionary and Detenninistic Methods in opti1nization of design and emphasizing on industrial and societal applications. This series of conferences was originally launched by the European Thematic Network INGENET. EUROGEN&#39;l l is an ECCOMAS Thematic Conference. Starting with EUROGEN 2009, ERCOFfAC, represented by its Special Interest Group (SIG) on Design Opti mization, is the ECCOMAS associate of this ECCOMAS Thematic Conference. The conference includes invited lectures, contributed lectures and five special technological sessions: • Multi-disciplinary Design...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567821"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567821"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567821; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567821]").text(description); $(".js-view-count[data-work-id=64567821]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567821; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567821']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567821]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567821,"title":"EUROGEN 2011 PROCEEDINGS --- Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems","internal_url":"https://www.academia.edu/64567821/EUROGEN_2011_PROCEEDINGS_Evolutionary_and_Deterministic_Methods_for_Design_Optimization_and_Control_with_Applications_to_Industrial_and_Societal_Problems","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567819"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567819/IDEA_An_Expert_System_as_a_Support_to_the_Design_of_Airfoils"><img alt="Research paper thumbnail of IDEA: An Expert System as a Support to the Design of Airfoils" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567819/IDEA_An_Expert_System_as_a_Support_to_the_Design_of_Airfoils">IDEA: An Expert System as a Support to the Design of Airfoils</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567819"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567819"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567819; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567819]").text(description); $(".js-view-count[data-work-id=64567819]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567819; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567819']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567819]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567819,"title":"IDEA: An Expert System as a Support to the Design of Airfoils","internal_url":"https://www.academia.edu/64567819/IDEA_An_Expert_System_as_a_Support_to_the_Design_of_Airfoils","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. Quagliarella","url":"https://independent.academia.edu/DQuagliarella"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="64567818"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/64567818/Flow_simulation_of_a_prop_fan_configuration_based_upon_structured_mesh_with_sliding_boundaries"><img alt="Research paper thumbnail of Flow simulation of a prop-fan configuration based upon structured mesh with sliding boundaries" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/64567818/Flow_simulation_of_a_prop_fan_configuration_based_upon_structured_mesh_with_sliding_boundaries">Flow simulation of a prop-fan configuration based upon structured mesh with sliding boundaries</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A procedure for simulating unsteady flows with surfaces in relative motion was developed...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A procedure for simulating unsteady flows with surfaces in relative motion was developed, based upon a structured multiblock U-RANS flow solver 1 . Meshes produced in zones of the flow field with different rotation speed are connected by sliding boundaries. The procedure developed guarantees that the flux conservation properties of the original scheme are maintained across the sliding boundaries during the rotation at every time step. The solver turns out to be very efficient, allowing computation in scalar mode with single core processors as well as in parallel. It was tested by simulating the unsteady flow on a prop fan configuration with two counter-rotating rotors. The comparison of results and performances with respect to an existing commercial flow solver (unstructured) are reported.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="64567818"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="64567818"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64567818; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64567818]").text(description); $(".js-view-count[data-work-id=64567818]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64567818; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64567818']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=64567818]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64567818,"title":"Flow simulation of a prop-fan configuration based upon structured mesh with sliding boundaries","internal_url":"https://www.academia.edu/64567818/Flow_simulation_of_a_prop_fan_configuration_based_upon_structured_mesh_with_sliding_boundaries","owner_id":37141433,"coauthors_can_edit":true,"owner":{"id":37141433,"first_name":"D.","middle_initials":null,"last_name":"Quagliarella","page_name":"DQuagliarella","domain_name":"independent","created_at":"2015-10-28T07:01:02.444-07:00","display_name":"D. 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