Enhancing Airfoil Performance through Artificial Neural Networks and Genetic Algorithm Optimization

Abstract

As airfoil design plays a crucial role in achieving superior aerodynamic performances, optimization has become an essential part in various engineering applications, including aeronautics and wind energy production. Airfoil optimization using high-fidelity CFD, although highly effective, has proven itself to be time-consuming and computationally expensive. This paper proposes an alternative approach to airfoil performance assessment, through the integration of a deep learning algorithm and a stochastic optimization method. NACA 4-digit parametrization was used for airfoil geometry generation, to ensure feasibility and to reduce the number of input variables. An extensive dataset of airfoil performance parameters has been obtained using an automated CFD solver, laying the foundation for the training of an accurate and robust Artificial Neural Network, capable of accurately predicting aerodynamic coefficients and significantly reducing computational time. Due to the ANN’s predictive capabilities of efficiently navigating vast search spaces, it has been employed as the fitness evaluation method of a multi-objective Genetic Algorithm. Following the optimization process, the resulting airfoils demonstrate significant enhancements in aerodynamic performance and notable improvements in stall behavior. To validate their increased capabilities, a high-fidelity Computational Fluid Dynamics (CFD) validation was conducted. Simulation results demonstrate the approach’s efficacy in finding the optimum airfoil shape for the given conditions and respecting the imposed constraints.

Keywords

• database
• artificial neural network
• computational fluid dynamics
• hyperbolic mesh
• automation
• genetic algorithm
• optimization