基于代理的湍流边界层流动主动减阻优化

Fabian Hübenthal, Marian Albers, Matthias Meinke, Wolfgang Schröder
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引用次数: 0

摘要

摘要以支持向量回归(SVR)和高斯过程回归(GPR)为代表,研究了两种基于代理的优化策略,以指导民航飞机巡航飞行和高速列车湍流边界层中主动减阻技术的驱动参数设计。作为一种近似方法,用壁面分辨大涡模拟(LESs)模拟了受横向正弦波作用的平板上的湍流。利用这些仿真数据建立了目标减阻对驱动参数(即优化变量)的依赖关系模型。在这项工作中,之前基于SVR的山脊线优化的纯粹利用方法被扩展到基于GPR的贝叶斯优化,以进一步自动化仿真驱动的驱动参数调整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surrogate‐based optimization for active drag reduction of turbulent boundary layer flows
Abstract Two surrogate‐based optimization strategies using support vector regression (SVR) and Gaussian process regression (GPR) as surrogates are investigated to guide the design of actuation parameters for active drag reduction techniques in turbulent boundary layer flows encountered at civil airplanes in cruise flight and high‐speed trains. As an approximation, the turbulent flow over a flat plate subjected to spanwise traveling transversal sinusoidal surface waves is simulated by wall‐resolved large‐eddy simulations (LESs). These simulation data are used to model the dependence of the objective drag reduction on the actuation parameters, that is, the optimization variables. In this work, the previous purely exploitative approach of SVR‐based ridgeline optimization is extended to GPR‐based Bayesian optimization to further automate the simulation‐driven tuning of the actuation parameters.
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