Saravanan Karuppanan | Scientific.Net

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class="page-name-block-text">Papers by Author: Saravanan Karuppanan</h1> </div> <div class="papers-author-content"> <div class="block-search-pagination"> </div> <div class="block-volume-title normal-text-gray"> <p> Paper Title<span>Page</span> </p> </div> <div class="item-block"> <div class="item-link"> <a href="/JERA.71.79">Modeling Stress Concentration Factors for Fatigue Design of KT-Joints Subjected to In-Plane Bending Loads Using Artificial Neural Networks</a> </div> <div class="item-link volume-authors"> <div class="semibold-middle-text"> Authors: Mohsin Iqbal, Saravanan Karuppanan, Veeradasan Perumal, Mark Ovinis, Muhammad Faizan, Adnan Rasul, Muhammad Iqbal </div> </div> <div id="abstractTextBlock605756" class="volume-info volume-info-text volume-info-description"> Abstract: Stress concentration factor (SCF) is an important parameter for the fatigue design of offshore joints. There are many empirical equations for quick estimation of SCF in tubular joints, based on experimental and numerical investigations. However, most of these equations apply at the crown and saddle points only, even though the maximum SCF may not always occur at these points, resulting in overestimated fatigue life. As the maximum SCF location varies due to multiplanar loads, damage, or reinforcement of joints, its location and magnitude are critical for a realistic fatigue life estimation. However, conventional statistical tools cannot approximate the complex behavior of SCF around the brace axis. On the other hand, artificial neural networks (ANN) can efficiently approximate complex phenomena. This study uses ANN to develop empirical models for determining SCF around the weld toe of KT-joints subjected to in-plane bending (IPB) loads. Eighteen hundred and fifty-eight (1858) designs were simulated using finite element analyses to generate data for training the ANN. Two IPB load conditions were focused on, and empirical equations were proposed for SCF around the chord side of the central brace-chord interface. These equations approximate maximum SCF with less than 5% error. This methodology applies to other joints and load configurations also. </div> <div> <a data-readmore="{ block: '#abstractTextBlock605756', lines: 2, expandText: '...more', collapseText: '...less' }"></a> </div> <div class="page-number semibold-large-text"> 79 </div> </div> <div class="item-block"> <div class="item-link"> <a href="/MSF.1103.163">An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load</a> </div> <div class="item-link volume-authors"> <div class="semibold-middle-text"> Authors: Mohsin Iqbal, Saravanan Karuppanan, Veeradasan Perumal, Mark Ovinis, Akram Hina </div> </div> <div id="abstractTextBlock601145" class="volume-info volume-info-text volume-info-description"> Abstract: Stress concentration factor (SCF) is usually used to estimate the fatigue life of an offshore joint. Historically, parametric equations were used to estimate SCF based on a statistical analysis of experimental and finite element analysis (FEA) results, to reduce cost and time. These equations give the SCF at the saddle/crown position for simple joints and basic load cases. However, for modified or defective joints, the location of the maximum SCF can change. In such circumstances, the single-point SCF equation cannot be used to estimate the maximum value of SCF, as its location may have changed from saddle/crown. To our knowledge, there are no general expressions to estimate SCF around the brace axis accurately. As artificial neural networks (ANN) can approximate the trend of complex phenomena better than conventional data fitting, a mathematical model based on ANN is proposed to estimate SCF based on the weights and biases of trained ANN. Nine hundred thirty-seven finite element simulations were performed to generate SCF data for training the ANN. This ANN was used to model an empirical equation for SCF. The proposed empirical model can estimate SCF around the brace axis with less than 5% error. The current study provides a roadmap to using FEA and ANN for empirical modeling of SCF in tubular joints, and this approach can be applied to any joint type, with or without design modification or damage. Once a database of similar equations is available, it can be utilized for quickly estimating SCF instead of costly experimentation and FEA. Optimization of the ANN can further improve the accuracy of the developed mathematical model. </div> <div> <a data-readmore="{ block: '#abstractTextBlock601145', lines: 2, expandText: '...more', collapseText: '...less' }"></a> </div> <div class="page-number semibold-large-text"> 163 </div> </div> <div class="block-bottom-pagination"> <div class="pager-info"> Showing 1 to 2 of 2 Paper Titles </div> </div> </div> </div> </div> </div> </div> <div class="social-icon-popup"> <a href="https://www.facebook.com/Scientific.Net.Ltd/" target="_blank" rel="noopener" title="Scientific.Net"><i class="inline-icon facebook-popup-icon social-icon"></i></a> <a href="https://twitter.com/Scientific_Net/" target="_blank" rel="noopener" title="Scientific.Net"><i class="inline-icon twitter-popup-icon social-icon"></i></a> <a href="https://www.linkedin.com/company/scientificnet/" target="_blank" rel="noopener" title="Scientific.Net"><i class="inline-icon linkedin-popup-icon social-icon"></i></a> </div> </div> <div class="sc-footer"> <div class="footer-fluid"> <div class="container"> <div class="row"> <div class="footer-menu col-md-12 col-sm-12 col-xs-12"> <ul class="list-inline menu-font"> <li><a href="/ForLibraries">For Libraries</a></li> <li><a href="/ForPublication/Paper">For Publication</a></li> <li><a href="/insights" target="_blank">Insights</a></li> <li><a href="/DocuCenter">Downloads</a></li> <li><a href="/Home/AboutUs">About Us</a></li> <li><a href="/PolicyAndEthics/PublishingPolicies">Policy & Ethics</a></li> <li><a href="/Home/Contacts">Contact Us</a></li> <li><a href="/Home/Imprint">Imprint</a></li> <li><a href="/Home/PrivacyPolicy">Privacy Policy</a></li> <li><a href="/Home/Sitemap">Sitemap</a></li> <li><a href="/Conferences">All Conferences</a></li> <li><a href="/special-issues">All Special Issues</a></li> <li><a href="/news/all">All News</a></li> <li><a href="/read-and-publish-agreements">Read & Publish Agreements</a></li> </ul> </div> </div> </div> </div> <div class="line-footer"></div> <div class="footer-fluid"> <div class="container"> <div class="row"> <div class="col-xs-12"> <a href="https://www.facebook.com/Scientific.Net.Ltd/" target="_blank" rel="noopener" title="Scientific.Net"><i class="inline-icon facebook-footer-icon social-icon"></i></a> <a href="https://twitter.com/Scientific_Net/" target="_blank" rel="noopener" title="Scientific.Net"><i class="inline-icon twitter-footer-icon social-icon"></i></a> <a href="https://www.linkedin.com/company/scientificnet/" target="_blank" rel="noopener" title="Scientific.Net"><i class="inline-icon linkedin-footer-icon social-icon"></i></a> </div> </div> </div> </div> <div class="line-footer"></div> <div class="footer-fluid"> <div class="container"> <div class="row"> <div class="col-xs-12 footer-copyright"> <p> © 2024 Trans Tech Publications Ltd. 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