Aircraft Propeller Design Technology Based on CST Parameterization, Deep Learning Models, and Genetic Algorithm

This article presents aircraft propeller optimal design technology; including an algorithm and OpenVINT 5 code. To achieve greater geometric flexibility, the proposed technique implements Class-Shape Transformation (CST) parameterization combined with Bézier curves, replacing the previous fully Bézier-based system. Performance improvements in the optimization process are accomplished through deep learning models and a genetic algorithm, which substitute XFOIL and Differential Evolution-based approaches, respectively. The scientific novelty of the article lies in the application of a neural network to predict the aerodynamic characteristics of profiles in the form of contour diagrams, rather than scalar values, which execute the neural network repeatedly per ISM algorithm iteration and speed up the design time of propeller blades by 32 times as much. A propeller for an aircraft-type UAV was designed using the proposed methodology and OpenVINT 5. A comparison was made with the results to solve a similar problem using numerical mathematical models and experimental studies in a wind tunnel.