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Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations

机译:多目标粘性翼型优化的进化(遗传)算法和伴随方法的比较

摘要

A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.
机译:提出了一种进化算法(EA)和一种用于二维翼型设计的Navier-Stokes代码的伴随梯度(AG)方法之间的比较。两种方法都使用通用函数评估代码,即代码的稳态显式部分ARC2D。设计空间的参数化是机翼表面的常见B样条曲线方法,再加上常见的网格化方法,将AG和EA限制在同一设计空间中。给出了一类粘性跨音速翼型的结果,其中在阻力最小化作为一个目标与升力最大化在另一个目标之间的优化折衷产生了多目标设计空间。比较效率,准确性和设计一致性。

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