SD7003低雷诺数翼型综合射流控制优化

D. Kamari, M. Tadjfar
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引用次数: 1

摘要

为了控制流场,在翼型顶面上广泛应用了跨越边界层的合成射流。引入遗传算法与人工神经网络(ANN)相结合的方法,求解设计参数的最优值。对SD7003翼型在雷诺数为60000、迎角为13°和16°时进行了优化。流场采用URANS方程求解,湍流模型采用k -ω海表温度。合成射流是切向边界层(TBL)。结果表明,在设计参数的最优值下,通过增加升力和减小阻力可以显著改善气动系数。通过减小失速后的压力阻力和显著减小分离区,实现了阻力的减小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthetic Jet Flow Control Optimization on SD7003 Airfoil at Low Reynolds Number
Synthetic jet crossing the boundary layer has been widely implemented on the airfoil’s top surface to control the flow field. Introducing a genetic algorithm coupled with artificial neural network (ANN) was used in this study to find optimum values for design parameters. Optimization was done for SD7003 airfoil at Reynolds number of 60,000 and angles of attack of 13° and 16°. URANS equations were employed to solve the flow field and k–ω SST was used as the turbulence model. The synthetic jets were implemented tangential to boundary layer (TBL). It was found that at optimum values of design parameters a significant improvement in aerodynamic coefficients by increasing lift and reducing drag can be achieved. Drag force reduction was achieved by reducing pressure drag at post stall and a significant reduction of separation zone.
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