| 张博文,吴光强,黄焕军.基于迭代更新近似模型的车内噪声优化[J].计算力学学报,2016,33(1):33~38 |
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| 基于迭代更新近似模型的车内噪声优化 |
| Optimization of interior noise in vehicle based on iteratively updating approximation model |
| 投稿时间：2015-02-04 修订日期：2015-05-23 |
| DOI：10.7511/jslx201601006 |
| 中文关键词: 声固耦合 迭代更新近似模型 K均值聚类 车内噪声 多目标优化 |
| 英文关键词:acoustic-structure coupling analysis iteratively updating approximation model K-mean cluster vehicle interior noise optimization |
| 基金项目:国家自然科学基金(51175379)资助项目. |
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| 中文摘要: |
| 基于声固耦合分析与迭代更新近似模型对车身板件进行了优化。首先通过板件声学贡献量分析,确定影响车内噪声的关键板件,将其厚度作为设计变量,在减少计算量的同时去除了无关变量的干扰。然后建立不同激励下驾驶员右耳处最大声压与关键板件厚度之间的组合近似模型,对其进行多目标优化并采用序列二次规划和K均值聚类算法对近似模型进行更新,使得采样样本逐渐靠近Pareto前沿,从而提高近似模型在Pareto前沿上的精度。优化结果表明,迭代更新近似模型能够有效优化车身结构,在保证轻量化的同时,降低了车内噪声。 |
| 英文摘要: |
| The vehicle interior noise was optimized based on acoustic-structure coupling analysis and the iteratively updating approximation model.Firstly, to reduce computation complexity, panel contribution analysis method was proposed to determine the design variables.Unit excitation was applied at the joint of vehicle body and left rear suspension, vehicle body and right front suspension seperately.The maximum sound pressure at the driver's right ear at aforementioned excitations were chosen as responses.Then the ensemble approximation model between the design variables and responses was established.The sequence quadratic programming algorithm and K-mean cluster was introduced to enhance the precision at the Pareto front.The results indicate that:iteratively updating approximation model can optimize the body structure to minimize the noise while ensure the lightweight. |
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