A probabilistic framework for multidisciplinary design: Application to the hydrostructural optimization of supercavitating hydrofoils

Bonfiglio Luca; Perdikaris Paris; águila Jose; Karniadakis George E.

摘要

Summary >The analysis and optimization of complex multiphysics systems presents a series of challenges that limit the practical use of computational tools. Specifically, the optimization of such systems involves multiple interconnected components with competing quantities of interest and high‐dimensional spaces and necessitates the use of costly high‐fidelity solvers to accurately simulate the coupled multiphysics. In this paper, we put forth a data‐driven framework to address these challenges leveraging recent advances in machine learning. We combine multifidelity Gaussian process regression and Bayesian optimization to construct probabilistic surrogate models for given quantities of interest and explore high‐dimensional design spaces in a cost‐effective manner. The synergistic use of these computational tools gives rise to a tractable and general framework for tackling realistic multidisciplinary optimization problems. To demonstrate the specific merits of our approach, we have chosen a challenging large‐scale application involving the hydrostructural optimization of three‐dimensional supercavitating hydrofoils. To this end, we have developed an automated workflow for performing multiresolution simulations of turbulent multiphase flows and multifidelity structural mechanics (combining three‐dimensional and one‐dimensional finite element results), the results of which drive our machine learning analysis in pursuit of the optimal hydrofoil shape. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><abstract xmlns =“http://www.wiley.com/namespaces/wiley”type =“main”xml：lang =“en”xml：id =“nme5923-abs-abs-abs-0001”> <标题类型=“main” >摘要</ title> >复杂多体系统的分析和优化呈现了一系列限制计算工具的实际使用的挑战。具体地，这种系统的优化涉及具有竞争量的竞争量和高维空间的多个互连组件，并且需要使用昂贵的高保真求解器来准确地模拟耦合的多体学。在本文中，我们提出了一种数据驱动的框架，以解决利用机器学习的最近进步的这些挑战。我们将多尺高斯进程回归和贝叶斯优化组合以构建概率的替代模型，以便以经济有效的方式探索高维设计空间。这些计算工具的协同使用产生了解决现实多学科优化问题的贸易和一般框架。为了展示我们方法的具体优点，我们选择了涉及三维超渗透水翼的热结构优化的具有挑战性的大规模应用。为此，我们开发了一种自动化的工作流程，用于执行湍流多相流量和多尺寸结构力学的多分辨率模拟（三维和一维有限元结果组合），结果驱动我们的机器学习分析以追求最佳的水翼形状。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-20801/'>《International Journal for Numerical Methods in Engineering》</a> <b style="margin: 0 2px;">|</b><span>2018年第4期</span><b style="margin: 0 2px;">|</b><span>共24页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Bonfiglio Luca&option=202" target="_blank" rel="nofollow">Bonfiglio Luca;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Perdikaris Paris&option=202" target="_blank" rel="nofollow">Perdikaris Paris;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=águila Jose&option=202" target="_blank" rel="nofollow">águila Jose;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Karniadakis George E.&option=202" target="_blank" rel="nofollow">Karniadakis George E.;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridge Massachusetts;</p> <p>Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridge Massachusetts;</p> <p>Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridge Massachusetts;</p> <p>Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridge Massachusetts;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/6940.html" title="工程数学">工程数学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Bayesian optimization&option=203" rel="nofollow">Bayesian optimization;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=fluid‐structure interaction&option=203" rel="nofollow">fluid‐structure interaction;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=machine learning&option=203" rel="nofollow">machine learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=multifidelity&option=203" rel="nofollow">multifidelity;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=multiphysics&option=203" rel="nofollow">multiphysics;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=supercavitation&option=203" rel="nofollow">supercavitation;</a> </p> <div class="translation"> 机译：贝叶斯优化;流体结构相互作用;机器学习;多消费物;多人学;超级挖掘; </div> </li> </ul> 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