Abstract
The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.
Highlights
Airfoil design is the most important part of flight vehicle design and requires considerable attention
The optimized configuration showed an increase of 6.14% in endurance ratio in comparison with the base design
Santos et al [25] presented an multi-layer perceptron (MLP) network to predict the aerodynamic coefficients of airfoils
Summary
Airfoil design is the most important part of flight vehicle design and requires considerable attention. CFD solvers compute the flow field around the airfoil using Navier–Stokes equations but require considerable time. Saakaar et al [20] used a convolutional network to predict the flow field around airfoils. Vinothkumar et al [21] used a multi-layer perceptron (MLP) to predict the flow field around 110 NACA airfoils at various angles of attack. The trained network showed good generalization performance in predicting test samples. Santos et al [25] presented an MLP network to predict the aerodynamic coefficients of airfoils. The trained network was used to optimize the airfoil for the maximum lift-to-drag ratio using a genetic algorithm. A t 1562 airfoils were considered, and aerodynamic data were generated using an in panel method code.
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