大域的な空力トポロジー最適設計における次元削減手法の検討

抄録

In recent years, topology optimizations have attracted attention as advanced optimization methods with higher degree of freedom to represent various topologies. These methods can automatically propose innovative/optimal topologies. Therefore, the topology optimization can be an efficient tool to extract innovative design insights. In general, the topology optimizations become high-dimensional optimization problems. This increases the possibility to represent optimal topologies, while requires a large number of performance evaluations to solve the optimization problems. In this study, we investigate the introduction of dimensionality reduction methods that can replace a high-dimensional design space with a low-dimensional design space in order to reduce the computational cost to solve the topology optimization problems. Proper orthogonal decomposition and active subspace method are considered as the dimensionality reduction methods in this study. As a result, it is confirmed that the computational cost can be reduced by using the dimensionality reduction methods.