The Genetic Algorithm-Based Optimization Method for the Geometric and Material Parameters of Underwater Anechoic Coating

Recent studies show that the shape of the internal cavity with periodic distribution has a significant impact on the characteristics of the anechoic coating. In this study, we proposed a genetic algorithm-based method for optimizing the design parameters of anechoic coating. By transforming the 3D finite element model (FEM) of the anechoic coating to a 2D axisymmetric finite element model (AFEM), and COMSOL with MATLAB co-simulation combined with the genetic algorithm applied to obtain the optimal parameters. Lots of simulation results demonstrate that as the number of generated segments on the rotating surface in the internal cavity increases, the sound absorption performance of the anechoic coating greatly improves. The expected optimization effect is achieved by substituting the optimized parameters into 3D FEM. Moreover, the reflection coefficient of an anechoic coating gradually increases with the increasing of incident angles. In the middle-high frequency range, when synthetical optimizing the material properties and cavity shape, better sound absorption performance can be obtained in comparison to the case where only the cavity shape is optimized.