An optimization method for dynamics of structures with repetitive component patterns

作     者：Perdahcioglu, D. Akcay Ellenbroek, M. H. M. van der Hoogt, P. J. M. de Boer, A. 

作者机构：Univ Twente Dept Appl Mech NL-7500 AE Enschede Netherlands 

出 版 物：《STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION》 (结构和多学科最佳设计)

年 卷 期：2009年第39卷第6期

页      面：557-567页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 080102[工学-固体力学] 

主　　题：Hybrid design optimization method Repetitive patterns Component mode synthesis Backpropagation neural networks Genetic algorithms Sequential quadratic programming 

摘      要：The occurrence of dynamic problems during the operation of machinery may have devastating effects on a product. Therefore, design optimization of these products becomes essential in order to meet safety criteria. In this research, a hybrid design optimization method is proposed where attention is focused on structures having repeating patterns in their geometries. In the proposed method, the analysis is decomposed but the optimization problem itself is treated as a whole. The model of an entire structure is obtained without modeling all the repetitive components using the merits of the Component Mode Synthesis method. Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques: Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP). GAs are utilized to increase the chance of finding the location of the global optimum and since this optimum may not be exact, SQP is employed afterwards to improve the solution. A theoretical test problem is used to demonstrate the method.