| 张建,罗建国,张国顺,傅爱军,尧潇雪.汽车排气系统尾管噪声声品质研究与优化*[J].,2024,43(4):865-873 |
| 汽车排气系统尾管噪声声品质研究与优化* |
| Research and optimization of noise and sound quality of tailpipe in automobile exhaust system |
| 投稿时间：2023-02-21 修订日期：2024-07-02 |
| 中文摘要: |
| 针对某型汽车排气系统尾管噪声声品质问题，提出一种以径向基函数（RBF）神经网络建立排气系统结构—尾管噪声声品质预测模型，并通过自适应模拟退火算法对排气系统噪声声品质进行优化的方法，来提高排气系统的声品质。以某型汽车排气系统为研究对象，建立包含结构参数的样本数据库，并用GT-POWER进行仿真计算得到得到样本数据的噪声数据，对仿真得到的声音数据进行声品质的主观评价打分，通过径向基函数（RBF）神经网络建立排气系统结构—尾管噪声声品质预测模型，对作为模型自变量的排气系统结构参数对尾管噪声声品质的贡献量和灵敏度进行了分析，最后采用自适应模拟退火算法对排气系统结构参数进行了优化，结果表明排气系统声品质显著提升。 |
| 英文摘要: |
| Aiming at the problem of tailpipe noise and sound quality of a certain type of automobile exhaust system, a radial basis function (RBF) neural network is proposed to establish an exhaust system structure-tailpipe noise quality prediction model, and optimize the noise and sound quality of exhaust system through adaptive simulated annealing algorithm to improve the sound quality of exhaust system.Taking a certain type of automobile exhaust system as the research object, a sample database containing structural parameters was established, and the noise data of the sample data obtained was obtained by simulation calculation with GT-POWER, and the subjective evaluation of the sound quality of the simulated sound data was scored, and the exhaust system structure-tailpipe noise quality prediction model was established by the radial basis function (RBF) neural network, and the contribution and main effects of the exhaust system structural parameters as model independent variables to the tailpipe noise quality were analyzed. Finally, the adaptive simulated annealing algorithm is used to optimize the structural parameters of the exhaust system, and the results show that the sound quality of the exhaust system is significantly improved. |
| DOI：10.11684/j.issn.1000-310X.2024.04.019 |
| 中文关键词: 尾管噪声声品质 径向基函数（RBF）神经网络 GT-POWER 主观评价 自适应模拟退火算法 |
| 英文关键词: Tailpipe noise sound quality Radial basis function (RBF) neural networks Subjective evaluation Adaptive simulated annealing algorithm |
| 基金项目:广西科技重大专项（桂科AA22068055） ;广西科技重大专项(桂科AA22372) |
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