Optimization of multi-structural parameters in metamaterials based on the DGN co-simulation method
Shangyang Jin, Fuxing Chen, Jie Bai, Bingfei Liu
TL;DR
This paper introduces a new optimization method for metamaterials that improves sound insulation performance by combining global and local algorithms.
Contribution
The DGN co-simulation method combines DOE, GA, and NLPQL to optimize multiple structural parameters in metamaterials more effectively.
Findings
The DGN method achieves a 44.8% improvement in peak frequency position for sound insulation.
The method improves the bandwidth of sound insulation by nearly 116.7% compared to the original structure.
Compared to NSGA-II, the DGN method enhances acoustic isolation bandwidth by 36.8%.
Abstract
The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. The quality of optimization of local optimization algorithms is often limited by the initial data. Moreover, the influence of structural parameters on the performance is difficult to be reflected in the optimization process of traditional algorithms. Thus, a combination algorithm optimization strategy for metamaterials in terms of multiple structural parameters is proposed in this paper based on a co-simulation approach. This strategy combines the design of experiments (DOE), genetic algorithm (GA), and NLPQL algorithm, which is referred to as the DGN method. For the optimization problem of complex structures, firstly, the relationship between the structural parameters on acoustic performance can be obtained by fitting the relationship…
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Taxonomy
TopicsAcoustic Wave Phenomena Research · Noise Effects and Management · Hearing Loss and Rehabilitation