A Study on Predicting Turbulence Drag Reduction Using Multiple Machine Learning Algorithms
Turbulence drag reduction holds significant importance in engineering applications,as it can markedly enhance the efficiency and performance of fluid systems.This paper proposes a method based on three machine learning algorithms to predict the effects of turbulence drag reduction:Multilayer Perceptron(MLP),Particle Swarm Optimization(PSO)enhanced MLP,and Least Squares Support Vector Regression(LS-SVR).Firstly,turbulence data was collected and processed to extract key features.Subsequently,the three algorithms were trained and optimized separately,and their performance in predicting turbulence drag reduction was compared.Experimental results indicate that the PSO-optimized MLP outperforms the other methods in terms of prediction accuracy and computational efficiency.This study provides new insights and methods for optimizing turbulence drag reduction using machine learning techniques.
万方数据
维普
