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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science >Shape optimization of mufflers hybridized with multiple connected tubes using the boundary element method, neural networks, and genetic algorithm
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Shape optimization of mufflers hybridized with multiple connected tubes using the boundary element method, neural networks, and genetic algorithm

机译：使用边界元方法，神经网络和遗传算法的多管混合消声器的形状优化

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摘要

Recently, research on new mufflers hybridized with connected curved tubes using phase cancellation techniques has been well addressed in the industrial field. Most researchers have explored noise reduction effects based on the transfer matrix method and the stiffness matrix method. However, the maximum noise reduction of a silencer within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of mufflers becomes an essential issue. In this article, two kinds of phase-cancellation mufflers (a two-connected tube and a three-connected tube) within a fixed length are assessed. In order to speed up the assessment of optimal mufflers hybridized with multiple connected curved tubes, a simplified objective function (OBJ) is established by linking the boundary element model (BEM; developed by the commercialized software SYSNOISE) with a polynomial neural network fitted with a series of real data: input design data (muffler dimensions) and output data approximated by BEM data in advance. To assess the optimal mufflers, a genetic algorithm is applied. Optimal results reveal that the maximum value of the sound transmission loss can be improved at the desired frequencies. Consequently, the optimum algorithm proposed in this study can provide an efficient way to develop optimal silencers for industry.
机译：最近，在工业领域中已经很好地研究了使用相抵消技术与连接的弯曲管混合的新型消声器的研究。大多数研究人员已经研究了基于传递矩阵法和刚度矩阵法的降噪效果。然而，在工程问题中经常发生的在有限空间内消音器的最大降噪被忽略了。因此，消声器的优化设计成为必不可少的问题。在本文中，评估了固定长度内的两种相消消声器（两个连接的管和一个三个连接的管）。为了加快评估与多个连接的弯管混合的最佳消声器的效率，通过将边界元模型（BEM；由商业化软件SYSNOISE开发）与多项式神经网络相链接，建立了简化的目标函数（OBJ）。一系列实际数据：输入设计数据（消声器尺寸）和预先由BEM数据近似的输出数据。为了评估最佳消声器，应用了遗传算法。最佳结果表明，在所需频率下可以改善声音传输损耗的最大值。因此，本研究中提出的最佳算法可以为开发用于工业的最佳消音器提供有效的方法。

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