干涉型消音器最佳化及其黏附吸音材料位置最佳化設計

Optimization of Noise Cancellation Mufflers and Optimal Placement of Absorption Materials

吳立偉(Li-Wei Wu)

指導教授 ： 張英俊

大同大學/工程學院/機械工程學系所/碩士(2009年)

摘要

本論文整合類神經網路系統與遺傳演算法，塔配聲學分析軟體SYSNOISE使用邊界元素法於消音器最佳化設計。
本論文主要分三部份：
(1)干涉型消音器之消音性能：消音器的通路分開為二或更多通路，最後再使其總路會集為一，來減低噪音。並探討尺寸變化，對干涉型消音性能之影響。(2)結合類神經網路系統與遺傳演算法之最佳化方法，並塔配聲學分析軟體SYSNOISE於干涉型消音器之尺寸最佳化設計：利用類神經網路建立其網路模式，再結合遺傳演算法搜尋消音器之最佳化尺寸及聲音傳遞損失值。(3)結合遺傳演算法之最佳化方法，和聲學分析軟體SYSNOISE於干涉型消音器最佳化設計：利用遺傳演算法規劃吸音材料位置，來達到消音性能最佳化。

對於工業界的應用，可有效地減少開發時間和降低生產成本，提升產品競爭力。

關鍵字

聲音傳遞損失值 ； 類神經網路系統 ； 遺傳演算法 ； 邊界元素法 ； SYSNOISE ； I-DEAS

並列摘要

The thesis combines Neural Network System and Genetic Algorithm with acoustic analysis software SYSNOISE and which uses Boundary Element Method (BEM) to perform the muffler optimum design.

The thesis is composed of three parts: (1) The performance of noise cancellation muffler: the tube of muffler is separated into two or more, and converge on one tube to reducing noise. Discussing the influence of variation of dimensions on the performance of noise cancellation muffler; (2) Combining Neural Network System and Genetic Algorithm with acoustic analysis software SYSNOISE to the optimum design of dimension of noise cancellation muffler: using Neural Network System to build network system and combining Genetic Algorithm to search the optimum of dimension and transmission loss(TL) of the muffer; (3) Combining Genetic Algorithm with acoustic analysis software SYSNOISE to the optimum design of noise cancellation muffler: using Genetic Algorithm to layout the distribution of absorptive materials to optimized the performance of the muffle.
For the industrial applications, it can save time of design, decrease the production cost, and promote the production competition.

並列關鍵字

noise cancellation muffler ； Transmission Loss(TL) ； Boundary Element Method ； Neural Network System ； Genetic Algorithm ； SYSNOISE

參考文獻

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