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基于高斯过程回归和遗传算法的翼型优化设计 被引量:6

Airfoil optimization design based on Gaussian process regression and genetic algorithm
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摘要 针对高升阻比风力机翼型前缘曲率半径较大的问题,传统的翼型参数化方法前缘控制能力不足,且基于面元法XFOIL预测精度差的问题,利用增强类函数/形函数转换(CST)参数化方法控制翼型的形状变化、拉丁超立方实验设计、计算流体力学(CFD)流场计算模块、高斯过程回归模型和遗传算法,提出了基于高可信度Reynolds average Navier-Stocks(RANS)和高斯回归模型辅助遗传算法的翼型优化设计方法。结果表明:基于高斯回归模型的翼型优化方法,可以将优化所用CFD计算次数降低一阶,从而大幅度提升优化设计效率。由标准算例超临界翼型RAE2822的降阻设计表明,在百次量级的CFD次数阻力降低43.16%,激波被削弱且升力、力矩和面积严格满足约束。由风力机翼型NACA64618的最大化升阻比优化设计表明,所设计翼型不仅在设计攻角和副设计攻角处升阻比大大增加,在整个小攻角范围内其气动性能都得到了提升,且两个主设计点,无不良阻力的产生。 In view of the problems that the leading edge curvature of the wind turbine airfoil with high lift-to-drag ratio has large radius,the traditional airfoil parameterization method has in-sufficient leading edge control ability,and there exists poor prediction accuracy based on the pan-el method XFOIL,an enhanced class function/shape function transformation(CST)parameter-ized method was used to control the shape change of airfoil, Latin hypercube experimental de-sign, computational fluid dynamics(CFD)flow field calculation module, Gaussian process re-gression model and genetic algorithm, based on high - confidence Reynolds average Navier -Stocks(RANS)and Gaussian regression model-assisted genetic algorithm for airfoil optimization design method. The results showed that the airfoil optimization method based on the Gaussian re-gression model can reduce the number of CFD calculations for optimization by one order,there-by greatly improving the optimization design efficiency. The resistance reduction design of the su-percritical airfoil RAE2822 of the standard calculation example showed that the resistance of the CFD frequency in the order of hundreds of times was reduced by 43. 16%,the shock wave was weakened and the lift,moment and area could strictly meet the constraints. The maximum lift-to-drag ratio of the wind turbine airfoil NACA64618 showed that the designed airfoil not only great-ly increased the lift-to-drag ratio at the design angle of attack and the secondary design angle of at-tack,but also improved its aerodynamic performance in the entire range of the small angle of at-tack. And there were two main design points,without bad resistance.
作者 常林森 张倩莹 郭雪岩 CHANG Linsen;ZHANG Qianying;GUO Xueyan(School of Energy and Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处 《航空动力学报》 EI CAS CSCD 北大核心 2021年第11期2306-2316,共11页 Journal of Aerospace Power
关键词 高斯过程回归(GPR) CST参数化方法 遗传算法 贝叶斯优化 翼型设计 Gaussian process regression(GPR) CST parameterization method genetic algorithm Bayesian optimization airfoil design
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