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首页> 外文学位 >Multi-objective optimization of transonic airfoils using variable-fidelity models, co-kriging surrogates, and design space reduction.
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Multi-objective optimization of transonic airfoils using variable-fidelity models, co-kriging surrogates, and design space reduction.

机译：使用可变保真度模型，共同克里格代理和设计空间缩减来优化跨音速翼型的多目标。

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摘要

Computationally efficient constrained multi-objective design optimization of transonic airfoils is considered. The proposed methodology focuses on fixed-lift design aimed at finding the best possible trade-offs between the conflicting objectives. The algorithm exploits the surrogate-based optimization principle, variable-fidelity computational fluid dynamics (CFD) models, as well as auxiliary approximation surrogates (here, using kriging). The kriging models constructed within a reduced design space. The optimization process has three major stages: (i) design space reduction which involves the identification of the extreme points of the Pareto front through single-objective optimization, (ii) construction of the kriging model and an initial Pareto front generation using multi-objective evolutionary algorithm, and (iii) Pareto front refinement using co-kriging models. For the sake of computational efficiency, stages (i) and (ii) are realized at the level of low-fidelity CFD models. The proposed algorithm is applied to the multi-objective optimization of a transonic airfoil at a Mach number of 0.734 and a fixed lift coefficient of 0.824. The shape is parameterized with eight B-spline control points. The fluid flow is taken to be inviscid. The high-fidelity model solves the compressible Euler equations. The low-fidelity model is the same as the high-fidelity one, but with a coarser description and is much faster to execute. With the proposed approach, the entire Pareto front of the drag coefficient and the pitching moment coefficient is obtained using 100 low-fidelity samples and 3 high-fidelity model samples. This cost is not only considerably lower (up to two orders of magnitude) than the cost of direct high-fidelity mode optimization using metaheuristics without design space reduction, but, more importantly, renders multi-objective optimization of transonic airfoil shapes computationally tractable, even at the level of accurate CFD models.
机译：考虑跨音速翼型的计算有效约束多目标设计优化。拟议的方法侧重于固定举升设计，旨在在相互矛盾的目标之间找到最佳的折衷方案。该算法利用了基于代理的优化原理，可变保真度计算流体动力学（CFD）模型以及辅助近似代理（此处使用kriging）。克里金模型在缩小的设计空间内构建。优化过程包括三个主要阶段：（i）设计空间缩减，其中涉及通过单目标优化确定帕累托锋的极端点；（ii）构造克里金模型和使用多目标初始帕累托锋生成进化算法，以及（iii）使用协同克里格模型进行Pareto前沿优化。为了计算效率，阶段（i）和（ii）在低保真CFD模型级别上实现。该算法应用于马赫数为0.734，固定升力系数为0.824的跨音速翼型的多目标优化。通过八个B样条曲线控制点对形状进行参数化。认为流体流动不粘稠。高保真模型求解可压缩的Euler方程。低保真模型与高保真模型相同，但是描述较粗糙，执行起来也快得多。通过提出的方法，使用100个低保真样本和3个高保真模型样本获得了阻力系数和俯仰力矩系数的整个Pareto前沿。这种成本不仅比不使用设计启发式空间而使用元启发法进行直接高保真模式优化的成本低得多（最多两个数量级），而且更重要的是，跨音速翼型形状的多目标优化在计算上易于处理，甚至在准确的CFD模型水平上。

著录项

  • 作者

    Amrit, Anand.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Aerospace engineering.;Computer engineering.
  • 学位 M.S.
  • 年度 2016
  • 页码 71 p.
  • 总页数 71
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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