Machine Learning Optimization Design of Acoustic Metamaterial Band Gaps for Cylindrical Shells
Cylindrical shell structures are widely used in engineering,and their vibration and noise levels have a great impact on engineering design.According to the requirements of band gap design of cylindrical shell acoustic metamaterials,a method of band gap optimization based on machine learning theory is proposed.Firstly,the finite element method is used to analyze the band gaps of 3 000 cylindrical shell metamaterial cell structures,and the calculated band gap results are input into different machine learning models for training.Then,the prediction model is constructed and the best model is searched according to the indicators such as the coefficient of determination(R2),Mean Square Error(MSE)and Explained Variance Score(EVS).Finally,the optimal machine learning model is used to broaden the band gap of cylindrical shell acoustic meta-materials.The research results show that this method can effectively predict and broaden the band gap of cylindrical shell acoustic metamaterials.
万方数据
维普
