It is well-known that the acoustic stealth of an underwater vehicle composed of a non-watertight structure has been facing severe challenges. The origins of this effect are associated with the fact that the coupling between the water and the mechanical structure is not negligible because both sides are in the water. Along these lines, the idea of forward absorption and backward reflection was proposed in this work to address this issue. More specifically, a composite underwater acoustic metamaterial (AM) was designed based on different layers, namely a sound absorption layer and a sound insulation layer from the outside to the inside. The sound absorption layer was made of a soft rubber matrix with embedded steel scatterers (ESs) to enrich the coupled resonance effects, while the sound insulation layer was composed of hard rubber with a built-in cavity to improve the impedance mismatching between the AM and the water. The impact of the number and thickness of the embedded ESs on the acoustic performance of the AM was also thoroughly investigated via a finite element method (FEM). A fast non-dominated genetic algorithm (NAGA-II) with elite strategy was used to optimize the position and the size of the ESs. The optimization results revealed the high absorption at the forward incidence and the high reflection at the backward incidence. Thus, our work provides a novel and effective approach for improving the acoustic stealth of underwater vehicles composed of non-watertight structures.

This paper establishes a many-objective MATLAB with a COMSOL joint parallel simulation optimization method in order to solve the current situation of low efficiency, single objective, and poor effect in acoustic optimization design research for a sound-absorbing structure. Our proposed method combines the means for population partitioning, monitoring, and adaptive normalization, within the framework of the NSGA-III algorithm, which takes the hyperplane deployment scheme into account in its entirety. Compared to the traditional genetic algorithm toolbox of the joint COMSOL optimization scheme, it is shown that the joint parallel simulation optimization method that is constructed in this paper achieves a higher optimization efficiency and a better experimental performance, thereby aiding in the identification of the optimal solution to multiple objectives. The optimization efficiency can increase linearly as the number of available cores on the computer increases. This method is then used to construct a parallel, low-frequency, broadband, highly-sound-absorbing structure. Without any constraints on the optimization objective, the diversity of the optimization results is evident within the parameter optimization range of this paper. The optimization results are stable and substantial, with constrained optimization objectives that have some reference value. In addition, the proposed method can solve acoustic vibration optimization problems and can be applied to other finite element optimization problems.