The objective of this research is to construct an efficient global topology optimization method using machine learning technologies. In the conventional design process of mechanical design, the conceptual design is the earliest stage of the design process; and it is carried out based on the designer’s own idea/experience. Therefore, it is difficult to obtain innovative and high-quality concepts overwhelming the designer’s knowledge since the earliest stage of the design process has the largest impact. In this research, therefore, the black-box function aerodynamic topology optimization algorithm via machine learning technologies (FANTOM) is developed to overcome the problem. In the FANTOM approach, topology optimization problems are solved using/combining two efficient global optimization methods developed by the authors: the efficient global optimization method for discontinuous optimization problems with infeasible regions using classification method, and the efficient global optimization method via clustering/classification methods and exploration strategy. In the present approach, topological optimal designs can be obtained only by setting an objective function and constraint conditions. The validity of the FANTOM approach is demonstrated in an inviscid drag minimization problem at a two-dimensional supersonic flow condition, which provides an optimal topology as the Busemann biplane airfoil. Executing topology optimizations with the variation in freestream Mach number, it is also demonstrated that the FANTOM approach can explore topological optimal designs robustly.

本研究的目的是利用机器学习技术构建一种有效的全局拓扑优化方法。在机械设计的常规设计过程中，概念设计是设计过程的最早阶段；它是根据设计师自己的想法/经验进行的。因此，由于设计过程的最早阶段影响最大，因此很难获得压倒设计师知识的创新和高质量概念。因此，在本研究中，开发了基于机器学习技术（FANTOM）的黑盒函数气动拓扑优化算法来克服该问题。在 FANTOM 方法中，使用/组合作者开发的两种有效的全局优化方法来解决拓扑优化问题：使用分类方法对具有不可行区域的不连续优化问题的有效全局优化方法，以及通过聚类/分类方法和探索策略的有效全局优化方法。在目前的方法中，拓扑优化设计只能通过设置目标函数和约束条件来获得。FANTOM 方法的有效性在二维超音速流动条件下的无粘性阻力最小化问题中得到证明，该问题提供了作为 Busemann 双翼翼型的最佳拓扑。随着自由流马赫数的变化执行拓扑优化，还证明了 FANTOM 方法可以稳健地探索拓扑优化设计。