Open Access

ARTICLE

Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model

Yiping Sun^1,2, Qiang Bai¹, Xuefeng Zhao¹, Meng Tao^1,*

1 School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China
2 Aviation Academy, Guizhou Open University, Guiyang, 550003, China

* Corresponding Author: Meng Tao. Email:

(This article belongs to the Special Issue: Data-Driven Model and Deep Learning for Advanced Smart Materials and Structures)

Computer Modeling in Engineering & Sciences 2022, 130(2), 1149-1170. https://doi.org/10.32604/cmes.2022.017760

Received 04 June 2021; Accepted 31 August 2021; Issue published 13 December 2021

Abstract

A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures. The acoustic performance field has become a key research direction worldwide. Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings, this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network (ANN). First, the mapping relationship between the input characteristics and reflection coefficient was analysed. When the elastic modulus and loss factor value were smaller, the characteristics of the reflection coefficient curve were more complicated. These key parameters affected the acoustic performance of the viscoelastic coating. Second, a dataset of the acoustic performance of the viscoelastic coating containing a cylindrical cavity was generated based on the finite element method (FEM), which avoided a large number of repeated experiments. The minmax normalization method was used to preprocess the input characteristics of the viscoelastic coating, and the reflection coefficient was used as the dataset label. The grid search method was used to fine-tune the ANN parameters, and the prediction error was studied based on a 10-fold cross-validation. Finally, the error distributions were analysed. The average root means square error (RMSE) and the mean absolute percentage error (MAPE) predicted by the improved ANN model were 0.298% and 1.711%, respectively, and the Pearson correlation coefficient (PCC) was 0.995, indicating that the improved ANN model accurately predicted the acoustic performance of the viscoelastic coating containing a cylindrical cavity. In practical engineering applications, by expanding the database of the material range, cavity size and backing of the coating, the reflection coefficient of more sound-absorbing layers was evaluated, which is useful for efficiently predicting the acoustic performance of coatings in a specific frequency range and has great application value.

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