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基于量子行为粒子群算法的舱室噪声监测点优化布置

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针对舱室噪声在线监测及声场预报问题，本文提出了一种基于量子行为粒子群算法的舱室内部声监测点优化布置方法。根据研究频段范围确定所需声腔模态阶数，计算全部备选监测点位置处各阶声腔模态的声场分布，采用模态置信矩阵作为目标函数，基于量子行为粒子群算法对监测点位置进行优化，获得优化布置方案。从声腔模态采样的正交性及内外声场响应的角度与其他测点布置方案进行了性能比较。研究表明:本文方法优化得到的测点布置方案采集声腔模态信息更全面，可有效提升舱室内声场的重建精度和基于舱室内声场监测的水下辐射噪声预报精度。
Optimal sensor placement at cabin-inner-sound monitoring points based on the quantum-behaved particle swarm optimization algorithm
This paper proposes a method for optimizing cabin inner-sound monitoring points using quantum-behaved particle swarm optimization(QPSO)to address the challenges of online monitoring of cabin noise and prediction of sound fields.The proposed method first determines the desired mode order of the acoustic cavity based on the fre-quency range.Subsequently,it calculates the sound field distribution for various mode orders of the acoustic cavity at all potential monitoring points.Utilizing the modal confidence matrix as the objective function,the positions of the monitoring points are optimized based on the QPSO algorithm.Herein,the quadrature of acoustic mode sam-pling and the responses of internal and external acoustic fields are compared with other arrangement schemes.The results show that the optimized measuring-point arrangement scheme proposed herein is more effective in collecting acoustic-cavity-mode information.This effectively improves the accuracy of sound field reconstruction within a cabin and the prediction accuracy of underwater radiation noise.

optimal arrangement of measuring pointsonline monitoring of cabin noisequantum-behaved particle swarm optimizationacoustic cavity modalitymodal confidence matrixunderwater radiation noise predictionsound field predictionacoustic excitation

郭强、时胜国、何辉辉

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哈尔滨工程大学 水声技术全国重点实验室,黑龙江 哈尔滨 150001

哈尔滨工程大学 海洋信息获取与安全工业和信息化部重点实验室,黑龙江 哈尔滨 150001

哈尔滨工程大学 水声工程学院,黑龙江 哈尔滨 150001

测点优化布置 舱室噪声在线监测 量子行为粒子群算法 声腔模态 模态置信矩阵 水下辐射噪声预报 声场预报 声激励

国家自然科学基金项目

52327901

2024

10.11990/jheu.202403002

哈尔滨工程大学学报
哈尔滨工程大学

哈尔滨工程大学学报

CSTPCD北大核心
影响因子：0.655
ISSN：1006-7043
年,卷(期)：2024.45(8)