An airfoil inverse design method is proposed by using the pressure gradient distribution as the design target. The adjoint method is used to compute the derivatives of the design target. A combination of the weighted drag coefficient and the target dimensionless pressure gradient is applied as the optimization objective, while the lift coefficient is considered as a constraint. The advantage of this method is that the designer can sketch a rough expectation of the pressure distribution pattern rather than a precise pressure coefficient under a certain lift coefficient and Mach number, which can greatly reduce the design iteration in the initial stage of the design process. Multiple solutions can be obtained under different objective weights. The feasibility of the method is validated by a supercritical airfoil and a supercritical natural laminar flow airfoil, which are designed based on the target pressure gradients on the airfoils. Eight supercritical airfoils are designed under different upper surface pressure gradients. The drag creep and drag divergence characteristics of the airfoils are numerically tested. The shockfree airfoil demonstrates poor performance because of a high suction peak and the double-shock phenomenon. The adverse pressure gradient on the upper surface before the shockwave needs to be less than 0.2 to maintain both good drag creep and drag divergence characteristics.
Field inversion and machine learning are implemented in this study to describe three-dimensional (3-D) separation flow around an axisymmetric hill and augment the Spart-Allmaras (SA) model. The discrete adjoint method is used to solve the field inversion problem, and an artificial neural network is used as the machine learning model. A validation process for field inversion is proposed to adjust the hyperparameters and obtain a physically acceptable solution. The field inversion result shows that the non-equilibrium turbulence effects in the boundary layer upstream of the mean separation line and in the separating shear layer dominate the flow structure in the 3-D separating flow, which agrees with prior physical knowledge. However, the effect of turbulence anisotropy on the mean flow appears to be limited. Two approaches are proposed and implemented in the machine learning stage to overcome the problem of sample imbalance while reducing the computational cost during training. The results are all satisfactory, which proves the effectiveness of the proposed approaches.
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