Rapid airfoil design based on ellipse direct method and prediction model

The rapid, efficient, and rational design of airfoils is crucial for enhancing the flight efficiency of aircraft. At present, traditional two-dimensional airfoil design methods are mostly based on the indirect transformation of the features of existing airfoil libraries, which makes it challenging to develop new airfoil characteristics to meet the increasingly stringent flight index requirements. Therefore, a direct airfoil design path with less time consumption and high efficiency and a new method integrating direct airfoil generation with predicting aerodynamic performance parameters were put forward. First, an in-depth theoretical analysis of the turbulent characteristics of airfoils was conducted, which can more accurately predict performance parameters, such as the lift-drag ratio, lift coefficient, and drag coefficient of airfoils. Second, an airfoil generation strategy based on the “direct ellipse method” was proposed. Taking the ellipse as the geometric carrier, the upper curve of the airfoil is rapidly generated by combining the random three-point descent curve and the parameter calculation theory, and the overall design of the airfoil is efficiently accomplished through coordinate transformation. Finally, based on the airfoil coordinate points and a large amount of lift–drag ratio data calculated by Python programs and computational fluid dynamics programs, an airfoil lift–drag ratio prediction model based on deep learning multilayer perceptron theory was constructed. Compared with the traditional finite element calculation method, this model significantly accelerates the calculation speed of the lift–drag ratio, and the error rate is only 2.783%. Combining the new airfoil generation method and the lift–drag ratio prediction model can complete the overall airfoil design in a diverse, rapid, and accurate way, providing a reliable approach and valuable reference for efficient and rapid airfoil design.