Aerodynamic Optimization Method for Propeller Airfoil Based on DBO-BP and NSWOA

• Published in: Aerospace 2024-11, Vol.11 (11), p.931
• Main Authors: Guo, Changjing, Xu, Zhiling, Yang, Xiaoyan, Li, Hao
• Format: Article
• Language: English
• Subjects: Accuracy; Aerodynamic coefficients; aerodynamic optimization; Aerodynamics; Aerofoils; airfoil optimization; Airfoils; Algorithms; Angle of attack; Back propagation; Back propagation networks; Computational fluid dynamics; DBO-BP agent model; Design optimization; Drag; Drag coefficients; Efficiency; Fluid dynamics; Genetic algorithms; Hydrodynamics; Hypercubes; Latin hypercube sampling; Mathematical optimization; Methods; Multiple objective analysis; Neural networks; NSWOA optimization algorithm; Optimization algorithms; Parameterization; Production capacity; Shape functions; Software; Sorting algorithms; Turbulence models
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace11110931

To address the issues of tedious optimization processes, insufficient fitting accuracy of surrogate models, and low optimization efficiency in drone propeller airfoil design, this paper proposes an aerodynamic optimization method for propeller airfoils based on DBO-BP (Dum Beetle Optimizer-Back-Propagation) and NSWOA (Non-Dominated Sorting Whale Optimization Algorithm). The NACA4412 airfoil is selected as the research subject, optimizing the original airfoil at three angles of attack (2°, 5° and 10°). The CST (Class Function/Shape Function Transformation) airfoil parametrization method is used to parameterize the original airfoil, and Latin hypercube sampling is employed to perturb the original airfoil within a certain range to generate a sample space. CFD (Computational Fluid Dynamics) software (2024.1) is used to perform aerodynamic analysis on the airfoil shapes within the sample space to construct a sample dataset. Subsequently, the DBO algorithm optimizes the initial weights and thresholds of the BP neural network surrogate model to establish the DBO-BP neural network surrogate model. Finally, the NSWOA algorithm is utilized for multi-objective optimization, and CFD software verifies and analyzes the optimization results. The results show that at the angles of attack of 2°, 5° and 10°, the test accuracy of the lift coefficient is increased by 45.35%, 13.4% and 49.3%, and the test accuracy of the drag coefficient is increased by 12.5%, 39.1% and 13.7%. This significantly enhances the prediction accuracy of the BP neural network surrogate model for aerodynamic analysis results, making the optimization outcomes more reliable. The lift coefficient of the airfoil is increased by 0.04342, 0.01156 and 0.03603, the drag coefficient is reduced by 0.00018, 0.00038 and 0.00027, respectively, and the lift-to-drag ratio is improved by 2.95892, 2.96548 and 2.55199, enhancing the convenience of airfoil aerodynamic optimization and improving the aerodynamic performance of the original airfoil.