# Low-Frequency Sound Absorption Mechanism and Bidirectional Prediction of a Viscoelastic Rubber-Based Underwater Acoustic Coating Using Multimodal Deep Ensemble Learning

## TL;DR

This paper introduces a new model for underwater acoustic coatings that improves low-frequency sound absorption and uses deep learning to predict and optimize their performance.

## Contribution

A novel MPPACL model and ELDNN framework for bidirectional prediction of underwater acoustic coating performance.

## Key findings

- The MPPACL model significantly enhances sound absorption between 50 and 2000 Hz.
- The ELDNN model's frequency prediction accuracy is 3.7 times higher than traditional DNN models.
- Underwater experiments validated the improved sound absorption performance of the proposed model.

## Abstract

Underwater acoustic coatings are widely used to suppress low-frequency noise radiation and sonar reflection in underwater vehicles. In this study, an underwater acoustic coating model consisting of viscoelastic rubber layers and micro-perforated panel (MPP) structures is investigated, with particular emphasis on the low-frequency sound absorption mechanism and predictive modeling. Based on an improved transfer function method, a novel Micro-Perforated Panel Acoustic Coating Layer (MPPACL) model is developed to describe the coupled acoustic behavior of multilayer coatings under underwater conditions. The low-frequency sound absorption performance is primarily governed by the viscoelastic characteristics of the rubber layer, including material damping and complex modulus, while the incorporation of the MPP further enhances absorption through resonance effects. To efficiently explore the relationship between structural parameters and acoustic response, an ensemble learning-based deep neural network (ELDNN) is constructed using analytically generated data, enabling both forward prediction of sound absorption performance and inverse prediction of structural design parameters. The results show that the frequency prediction accuracy of the IDNN model is 3.7 times that of the DNN model. Furthermore, the proposed MPPACL model has achieved a significantly enhanced sound absorption effect within the frequency range of 50 to 2000 hertz. This effect has also been further verified through underwater experiments. The proposed framework provides an efficient and reliable approach for the design and optimization of underwater acoustic coatings.