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Reduced order model assisted evolutionary algorithms for multi-objective flow design optimization

机译：用于多目标流设计优化的降阶模型辅助进化算法

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摘要

Evolutionary algorithms (EAs) have been widely used for flow design optimization problems for their well-known robustness and derivative-free property as well as their advantages in dealing with multi-objective optimization problems and providing global optimal solutions. However, EAs usually involve a large number of function evaluations that are sometimes quite time consuming. In this article a reduced order modelling technique that combines proper orthogonal decomposition and radial basis function interpolation is developed to reduce the computational cost. These models provide an efficient way to simulate the whole flow region with varied geometry parameters instead of solving partial differential equations. As a test case, the design optimization of a heat exchanger is considered. Shape variation is conducted through a free form deformation technique, which deforms the computational grid employed by the flow solver. A comparison between the optimization results when using reduced order models and the exact flow solver is presented.
机译：进化算法（EA）因其众所周知的鲁棒性和无导数特性以及在处理多目标优化问题和提供全局最优解方面的优势而被广泛用于流程设计优化问题。但是，EA通常涉及大量的功能评估，有时这很耗时。在本文中，开发了一种结合了适当的正交分解和径向基函数插值的降阶建模技术，以降低计算成本。这些模型提供了一种有效的方法来模拟具有变化的几何参数的整个流动区域，而不是求解偏微分方程。作为测试案例，考虑了热交换器的设计优化。形状变化是通过自由形式变形技术进行的，该技术使流求解器采用的计算网格变形。给出了使用降阶模型时的优化结果与精确的流量求解器之间的比较。

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