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SYNTHETIC JET FLOW CONTROL OPTIMIZATION ON SD7003 AIRFOIL AT LOW REYNOLDS NUMBER

机译:低雷诺数下SD7003机翼的合成射流控制优化

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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--a> 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.
机译:跨越边界层的合成射流已广泛应用于机翼的顶表面,以控制流场。这项研究中引入了遗传算法与人工神经网络(ANN)结合,以找到设计参数的最佳值。雷诺数为60,000,迎角为13°和16°的SD7003机翼进行了优化。使用URANS方程求解流场,并使用k--a> SST作为湍流模型。合成射流与边界层(TBL)相切。已经发现,在最佳设计参数值下,通过增加升力和减小阻力,可以显着改善空气动力学系数。通过减小后失速时的压力阻力和分离区的显着减小来实现阻力的减小。

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