具矩形截面延伸型消音系統性能分析與最佳化設計

Analysis and Optimization of Rectangular Expansion Chamber with Multiple Extended tubes

陳瑱沅(Tien-Yuan Chen)

指導教授 ： 張英俊

大同大學/工程學院/機械工程學系所/碩士(2012年)

摘要

摘 要
本論文使用聲學分析軟體SYSNOISE之邊界元素(Boundary element method, BEM)法分析，其主要目的在於研究分析消音箱放置延伸開口薄阻板及加入雙邊延伸管下之尺寸設計。
設計消音箱模型與規劃出模型組數，以軟體I-DEAS建立出消音箱模組，再將建立好之模組匯入聲學分析軟體SYSNOISE分析消音箱放置開口薄阻板、延伸型開口薄阻板與加入雙邊延伸管及貼附吸音棉下之消音性能（Transmission Loss）。
論文主要有兩大主軸：(1) 於消音箱內部放置開口薄阻板、延伸型開口薄阻板及加入雙邊延伸管，探討不同開口下不同長度之延伸段與延伸管對於消音箱消音性能之影響。(2) 加入類神經網路系統與遺傳演算法兩者結合之最佳化方法，與搭配聲學分析軟體SYSNOISE設計出消音箱延伸開口薄阻板及加入延伸管之最佳化設計。
透過理論數值分析的驗證，與本文模擬結果相當吻合下，對於日常及工業界之應用，能有效的減少噪音及開發設計時間和生產成本，進而提升商業上產品競爭力。

關鍵字:SYSNOISE、延伸開口薄阻板、類神經網路系統

關鍵字

SYSNOISE ； 延伸開口薄阻板 ； 類神經網路系統

並列摘要

Abstract
A software (SYSNOISE), one kind of the boundary element method (BEM) used in acoustic analysis, has been adopted in this paper. The main purpose for this paper is to study the acoustical-performance of the plenums equipped with multiple thin extended baffles.
First, the model and testing set of the plenum will be planned. Thereafter, the outline model of the plenum is established t using the I-DEAS software and then imported to the SYSNOISE. The related acoustical performance（Transmission Loss） of the plenums equipped with multiple thin extended baffles and internally lined with sound absorbing material will be proceeded.
Two primary issues presented in this paper include (1) To explore the influences of the acoustical performance by varying the lengths of extended baffles and tubes for the plenums equipped with open, extended baffles, and extended tubes; (2) To search for optimally shaped plenum by using the SYSNOISE, Neural Network, and Genetic Algorithm.
Here, the accuracy of mathematical model has been verified using the experimental data and found to be in good agreement. Consequently, the optimization applied in industry can effectively shorten the design time, lower the manufacturing cost, and enhance the product competitiveness.

Keywords: SYSNOISE、Extended thin baffle、Neural Network System

並列關鍵字

Extended thin baffle ； SYSNOISE ； Neural NetworkS

參考文獻

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