Aerodynamic lift coefficient prediction of supercritical airfoils at transonic flow regime using convolutional neural networks (CNNs) and multi-layer perceptions (MLPs)

Adebayo Olayemi, Olalekan; Isaac Salako, Oluwadolapo; Jinadu, Abdulbaqi; Obalalu, Adebowale Martins; Elochukwu Anyaegbuna, Benjamin

doi:10.30772/qjes.v16i2.955

	Aerodynamic lift coefficient prediction of supercritical airfoils at transonic flow regime using convolutional neural networks (CNNs) and multi-layer perceptions (MLPs)
Al-Qadisiyah Journal for Engineering Sciences
Volume 16, Issue 2, June 2023, Pages 108-115 PDF (707.91 K)
Document Type: Research Paper
DOI: 10.30772/qjes.v16i2.955
Authors
Olalekan Adebayo Olayemi^* ¹; Oluwadolapo Isaac Salako¹; Abdulbaqi Jinadu¹; Adebowale Martins Obalalu²; Benjamin Elochukwu Anyaegbuna³
¹Department of Aeronautics and Astronautics, Faculty of Engineering and Technology, Kwara State University, Malete, Nigeria
²Department of Mathematical Science, Augustine University, Illara-Epe, Nigeria
³Department of Mechanical Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin 240003, Nigeria
Abstract
Designing an aircraft involves a lot of stages, however, airfoil selection remains one of the most crucial aspects of the design process. The type of airfoil chosen determines the lift on the aircraft wing and the drag on the aircraft fuselage. When a potential airfoil is identified, one of the first steps in deciding its optimality for the aircraft design requirements is to obtain its aerodynamic lift and drag coefficients. In the early stages of trying to select a candidate airfoil, which a whole part of the design process rests on, the conventional method for acquiring the aerodynamic coefficients is through Computational Fluid Dynamics Simulations (CFDs). However, CFD simulation is usually a computationally expensive, memory-demanding, and time-consuming iterative process; to circumvent this challenge, a data-driven model is proposed for the prediction of the lift coefficient of an airfoil in a transonic flow regime. Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) were used to develop a suitable model which can learn a set of usable patterns from an aerodynamic data corpus for the prediction of the lift coefficients of airfoils. Findings from the training revealed that the models (MLPs and CNNs) were able to accurately predict the lift coefficients of the airfoil.
Keywords
Aerodynamics; Lift Coefficient; Supercritical Airfoil; Transonic; Neural-network

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