Aerodynamic shape optimization by continually moving ROM

Li Zhou Li

doi:10.1016/j.ast.2020.105729

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Aerodynamic shape optimization by continually moving ROM

2020-04-01

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DOI：10.1016/j.ast.2020.105729

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Abstract

En 中文

This study presents a multi-round shape optimization method based on the aerodynamic reduced order model. In this method, each optimization round employs the first-order Taylor reduced order model to provide aerodynamic characteristics in the local design space around the baseline point. At the end of each optimization round, the baseline point moves toward the optima and a new reduced order model is built at the new baseline point for the next optimization round. In this way, the provided method explores the whole design space. The NACA0012 airfoils in subsonic and transonic flows are used to verify the method with the lift-drag ratio as optimization objective. The design parameters are the shape control parameters of the airfoil. The optimization algorithm is a genetic algorithm. After a total of 20 simulations by two rounds of optimizations, the lift-drag ratios increase from 0 to 25 and from 0 to 8 in the subsonic case and transonic case, respectively. An additional trick was suggested for addressing shockwave effect in the transonic case, in which the influence of the airfoil shape on the aerodynamic forces is divided into the following two parts: the smoothed part to address the influence of the shape on the smooth aerodynamic distribution, and the shockwave part to address the influence of the shape on shockwave strength, shockwave position, and their effect on the aerodynamic distribution. The optimizations of the airfoils in subsonic and transonic flows yield different trends. The results of the subsonic case tend to increase the airfoil curvature to increase the lift-drag ratio. The results of the transonic case tend to move the upper surface shockwave to the trailing edge, while moving the lower surface shockwave to the leading edge. Independent of the case, the provided optimization method can properly address the resulting trends and increase the efficiency of the optimization. (C) 2020 Elsevier Masson SAS. All rights reserved.

Keywords:

Aerodynamic

Shape optimization

Reduced order model

Moving search

Taylor expansion

Parameterization

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