Optimization Design of Airfoil Hydrodynamic Performance Based on Machine Learning
Machine learning-based airfoil geometry optimization design methods can effectively avoid complex numerical solution processes and have high efficiency.By parameterizing the airfoil geometry and establishing machine learning models together with optimization algorithms for both prediction and learning,the time required for optimizing airfoil design can be greatly reduced.This paper researches machine learning-based airfoil hydrodynamic performance prediction and optimization design.The CST method is used to parameterize the airfoil,and the ensemble learning method XGBoost is used to establish a rapid forecasting model of airfoil hydrodynamic characteristics.Combined with machine learning methods and genetic algorithms,an optimization model is established.The results show that the proposed airfoil optimization and design method can efficiently obtain optimal airfoil geometries.It has important implications for the sectional geometry design optimization of marine propeller blades.
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