在圓管內流動的流體通過孔口板時，在孔口板後方會因管內的截面積突然變大而造成邊界層分離，此一區域包含不穩定的剪切層、渦流、強烈回流等，此區域的壓力波動甚大，是造成誘導聲音的主要來源。本研究是利用流體通過孔口板時會因壓力波動而誘發出聲音，利用麥克風擷取聲音的壓力訊號，然後對聲壓訊號進行頻譜分析，建立流體的流動速度與聲音之間的關係，期能由量測管流內的聲壓訊號即對應出管內平均流體的流動速度。
首先，利用實驗進行初步驗證。設備包含空氣幫浦、平直圓管、麥克風、孔口板及其相關儀器，實驗中的平均入口流體速度由0.88 m/s增加至1.35 m/s、實驗環境在溫度攝氏25度、大氣壓力條件下進行。孔口板是放置於水平的圓管內並完全貼合圓管的內徑，且麥克風裝置在孔口板下游的管壁上。因為流體於管內的平均流動速度很低，可視為不可壓縮流。實驗以空氣幫浦為其主要氣源，讓空氣沿平直圓管流動，俟其通過孔口板時，再由麥克風擷取其聲壓訊號，然後利用快傅利葉轉換(FFT)來進行頻譜及回歸分析，由初步的實驗結果發現其聲壓訊號的功率譜密度(PSD)會隨流體的速度增加而上升，並呈現正比的關係。
其次，穩定的氣源，不僅可產生方向一致的入口流體速度，也可減少因供氣設備所造成的噪音。因此，選擇安全、經濟、有效的壓縮空氣為主要實驗的供氣來源，其實驗設備包括儲氣瓶、緩衝罐、電磁閥、平直圓管、孔口板和麥克風等。實驗中的平均入口流體速度由9.89 m/s增加至33.25 m/s、雷諾數從6.6×103 增加到 2.3×104、實驗環境在溫度攝氏25度及大氣壓力條件下進行。此實驗先將壓縮空氣由儲氣瓶降壓至緩衝罐，以達到實驗所設定的壓力值，再利用電磁閥控制其出氣量(關啟約1秒)，使空氣沿平直圓管流動，俟其通過孔口板時由麥克風擷取其聲壓訊號，然後利用快傅利葉轉換(FFT)來進行頻譜及其回歸分析，實驗結果證明了其聲壓訊號的PSD與流體速度的正相關的關係，並建立聲壓與氣流速度的關係曲線，而且可利用此一曲線來預測流場速度。為了再次檢驗實驗分析的趨勢，將利用訊號分析的伯格法(the Burg method)來進行頻譜分析及找出其特徵頻率，仍然獲得一致的趨勢。整體而言，聲壓訊號的PSD與雷諾數(以氣流速度基礎)有強烈的正相關的關係。
利用計算流體力學(CFD)的模擬工具FLUENT中的非穩態平均雷諾數法(URANS)來預測氣流在圓管內的流場行為與聲壓機制。氣流速度由9.89增加至33.25 m/s，其誘導聲音由81.9增加至126.7分貝，模擬結果指出誘導聲音的峰值與其雷諾數成正比，此結果與實驗量測趨勢一致。使用麥克風量測聲壓訊號來推測其管流的氣流速度經實驗的快速傅利葉轉換與伯格法的分析，都呈現一致的正向比例趨勢，而且CFD模擬結果也指出氣流經由孔口板的干擾後，誘導出聲音且隨氣流速度增加而放大，根據以上結果，可證明管流的流速與其產生的聲音有很好的正向比例關係。

This study is conducted to analyze the characteristic sound signals and to reveal the relationship between averaged incoming air velocity and the flow-induced sound characteristics behind an obstacle in a pipe with various Reynolds numbers. A verifiable experiment of the quantitative analysis of sound pressure signals correlated with averaged air velocity in a pipe has been conducted using an apparatus that includes an air pump in conjunction with a pipe, a microphone, and an orifice plate, among other instruments. The analysis of the results using the fast Fourier transform (FFT) and statistical regression show that the pressure fluctuation of sound spectra can be correlated to the averaged incoming air velocity of a pipe and the approach for measuring the averaged incoming air velocity using a microphone can be justified. To ensure that the sound signals be positively identified from experimental data, the Burg method with autoregressive (AR) model is performed the analysis of the measured signals to find the characteristic frequency. Overall, it is existed a good trend between the power spectral density (PSD) of the sound pressure and the Reynolds number based on the incoming air velocity can be obtained. The experimental results using the FFT show that the pressure fluctuation of sound spectra is related to the averaged incoming air velocity in the pipe from the regression analysis, which can form the relation curve. The aerodynamic data and complicated flow structures are visualized using commercial computational fluid dynamics (CFD) package FLUENT, with Unsteady Reynolds-averaged Navier-Stokes (URANS) modelling and to use a compressible pressure-based solver simulations of turbulence flow. Simulation results of URANS indicate a similar trend with the experimental results which be pointed out that sound amplitudes are proportional to the Reynolds number as shown on comparison with experimental results, it can be shown that the approach for measuring the averaged incoming air velocity using a microphone can be validated.
From the CFD simulations the flow field mechanism and the sound amplitude of air through the orifice in the pipe can be revealed. It has been found that the peak amplitude of sound is located at the edge of orifice plate, i.e. the loudest sound at this position is generated by pressure fluctuations of unsteady flow. It concludes that a microphone can be used to measure the sound pressure fluctuation of downstream of the orifice and this results can be correlated to the incoming air velocity.

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