| 周玉甲,胡峰松.考虑压力损失的汽车排气系统尾管噪声优化*[J].,2025,44(5):1288-1296 |
| 考虑压力损失的汽车排气系统尾管噪声优化* |
| Tailpipe noise optimization of automotive exhaust systems considering pressure loss |
| 投稿时间：2024-12-26 修订日期：2025-08-28 |
| 中文摘要: |
| 针对某型汽车排气系统尾管2阶噪声以及冷端背压超限问题，提出一种在保证排气系统冷端背压的前提下对排气系统尾管2阶噪声进行优化的方法。首先把消声器的结构参数作为优化变量，采用最优拉定超立方方法进行取样，通过径向基函数神经网络建立数学模型，然后通过具有精英保留策略的非支配排序遗传算法进行多目标优化得到最优解，并对理论方法进行试验验证。结果表明：通过数学模型优化后尾管2阶噪声和排气系统冷端背压分别降低了1.25 dB(A)和1.86 kPa，且通过试验与仿真进行对比得到尾管2阶噪声和冷端背压最大误差均小于5%，说明优化结果可靠，可以实现最小化背压和最大化降噪的这一个双重目标。 |
| 英文摘要: |
| Aiming at the 2nd order noise of the tailpipe of an exhaust system of a certain type of automobile and the problem of cold-end backpressure exceeding the limit, this paper proposes a method to optimize the 2nd order noise of the tailpipe of the exhaust system under the premise of guaranteeing the cold-end backpressure of the exhaust system. Firstly, the structural parameters of the muffler are taken as the optimization variables, the optimal Radin hypercube method is used for sampling, the mathematical model is established by radial basis function (RBF) neural network, and then the optimal solution is obtained by multi-objective optimization through the non-dominated sequential genetic algorithm with elite retention strategy (NSGA-II), and the theoretical method is verified experimentally. The results show that the 2nd order noise of the tailpipe and the backpressure at the cold end of the exhaust system are reduced by 1.25 dB(A) and 1.86 kPa after the optimization of the mathematical model, and the maximal error of the 2nd order noise of the tailpipe and the backpressure at the cold end is less than 5% by comparing the experiment with the simulation, which indicates that the optimization results are reliable and can achieve the dual objectives of minimizing the backpressure and maximizing the noise reduction. |
| DOI：10.11684/j.issn.1000-310X.2025.05.019 |
| 中文关键词: 排气系统冷端背压 尾管2阶噪声 径向基函数（RBF）神经网络 NSGA-Ⅱ |
| 英文关键词: Cold end back pressure of the exhaust system Tailpipe second-order noise Radial basis function (RBF) neural networks NSGA-Ⅱ |
| 基金项目:湖南省教育厅科学研究项目 |
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