首页|An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions
An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions
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Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the com-plexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape de-formation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics sim-ulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of nu-merical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.
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