基于层级分解的前围声学包多目标优化

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (3) : 267-277.

声学研究与应用

基于层级分解的前围声学包多目标优化

杨帅1,2，吴宪*1，薛顺达2

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Multi-objective optimization of front wall acoustic package based on hierarchical decomposition

YANG Shuai1,2, WU Xian*1, XUE Shunda2

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摘要

搭建了前围声学包多层级目标分解架构，提出GAPSO-RBFNN（Genetic Algorithm Particle Swarm Optimization-Radial basis function neural network，GAPSO-RBFNN）预测模型，并将其应用于多层级目标分解架构。将材料数据库、覆盖率、泄漏量作为优化的变量范围，以PBNR均值作为约束，以重量和成本作为优化目标，采用NSGA-II（Nondominated Sorting Genetic Algorithm II，NSGA-II）算法进行多目标优化，得到Pareto多目标解集。并从中选取满足设计目标的最佳组合方案（材料组合、覆盖率、前围过孔密封方案选型）。结果显示，该模型最终的优化结果与实测结果接近，误差分别为0.35%，1.47%，1.82%，相较于初始声学包方案，优化后的结果显示，PBNR（Power Based Noise Reduction，PBNR）均值提升3.05%，其重量降低52.38%，成本降低15.15%，验证了所提方法的有效性和准确性。

Abstract

A Genetic Algorithm Particle Swarm Optimization-Radial basis function neural network (GAPSO-RBFNN) prediction model is proposed and applied to the multi-level target decomposition architecture. Material database, coverage rate and leakage amount were taken as the optimized variable range, PBNR mean value was taken as constraint, taking weight and cost as optimization objectives, NSGA-II (Nondominated Sorting Genetic Algorithm II) algorithm is used to optimize the multi-objective, and Pareto multi-objective solution set is obtained. And the best combination scheme (material combination, coverage ratio, sealing scheme selection of dash pass-throughs) is selected to meet the design objectives. The results show that the final optimized results of the model are close to the measured results, and the errors are 0.35%, 1.47% and 1.82% respectively. Compared with the initial acoustic package scheme, the optimized results show that the PBNR（Power Based Noise Reduction，PBNR） average increases by 3.05%, its weight decreases by 52.38%, and its cost decreases by 15.15%. The effectiveness and accuracy of the proposed method are verified.

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杨帅1, 2, 吴宪1, 薛顺达2. 基于层级分解的前围声学包多目标优化[J]. 振动与冲击, 2025, 44(3): 267-277

YANG Shuai1, 2, WU Xian1, XUE Shunda2. Multi-objective optimization of front wall acoustic package based on hierarchical decomposition[J]. Journal of Vibration and Shock, 2025, 44(3): 267-277

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