Design optimization of structures including repetitive patterns

It is becoming a common practice to use surrogate models instead of finite element (FE) models in most of the structural optimization problems. The main advantage of these surrogate models is to reduce computation time as well as to make design optimization of complex structures possible. For surrogate modeling, firstly input-target pairs (training set) are required which are obtained by running the FE model for varying values of the design parameter set. Then the relationship between these pairs is defined via curve fitting where the created curve is named as a surrogate model. Once the surrogate model is found, it replaces the FE model in the optimization problem. Finally the optimization is performed using suitably chosen algorithm(s). Since solving an FE model may take very long time for certain applications, gathering the training set is usually the most time consuming part in the overall optimization process. Therefore, in this research the merits of the Component Mode Synthesis (CMS) method are utilized to gather this set for structures including repetitive patterns (e.g. fan inlet case). The reduced FE model of only one repeating pattern is created using CMS and the obtained information is shared with the rest of the repeating patterns. Therefore, the model of an entire structure is obtained without modeling all the repetitive patterns. In the developed design optimization strategy Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques. Genetic Algorithms (GAs) are utilized to increase the chance of finding the location of the global optimum. Since the optimum attained by GAs may not be exact, Sequential Quadratic Programming is employed afterwards to improve the solution. An academic test problem is used to demonstrate the strategy