本研究分別以粒子影像測速儀及熱線測速法量測圓柱尾流與格點紊流以探討兩種測量方法的數據差距。利用圓柱尾流的明顯流場特徵來凸顯PIV計算上所受到的空間解析限制：當空間解析不足時會造成特徵頻率上的低估，因此與熱線測速儀比較上頻譜會有一個頻率衰弱的現象；而格點紊流因為沒有強烈的低頻效應，因此則可以觀察PIV計算所造成的底噪範圍。藉由人造影像的模擬在不考慮速度空間相關性的條件下討論誤差所造成的頻譜樣貌，結果顯示誤差頻譜主要由兩種誤差貢獻，偏差量誤差會隨著不同的流場機制誘發出特徵頻率，而隨機誤差則會產生白躁雜訊。
圓柱尾流所產生的渦流會造成顆粒濃度產生變化，因此間接造成PIV所計算的交互相關性變化，本研究指出其相關性係數與流場的機制會有關連，因此不能將整體的壞點判定設定相同，要針對不同的流場特徵去做壞點的認定閥值以免將特定的流場資訊剔除。
進光量的增加會造成灰階值的利用率上升，使得PIV識別微小擾動的能力增加，因此適當的增加曝光時間能夠降低其高頻雜訊;但是當曝光時間過長使得粒子產生殘影會等效於影像積分尺度的變長，因此誤差會上升，由頻譜也看得出高頻雜訊隨著曝光時間變長明顯的抬升，所以實驗時曝光時間建議以影像殘影不超過1 pixel為基準。
利用人造影像模擬出真實粒子影像上的結塊粒子與區域性均勻粒子兩大特徵，並利用不同的參數如粒子大小或數量來討論誤差且說明影像積分尺度的確可以當做一個影像品質的定量參數，最後由模擬得知實驗上PIV所計算的速度主要貢獻為結塊粒子並非原本設定能跟隨上流場的均勻粒子，且使用中值濾波器在模擬上雖然能夠減少結塊粒子的效應，但在真實實驗上無法完全消去結塊粒子的影像且會增加計算的不確定性。

This thesis investigates the velocity distribution of turbulent flow over cylinder and grid turbulence by hot-wire anemometry (HWA) and particle image velocimetry (PIV). Using the characteristics of the flow over cylinder to highlight the limitation of spatial resolution in PIV： the characteristic frequency would be underestimated when the spatial resolution is insufficient； therefore， when we compare the spectrum with that measured with HWA，it shows a decay of the frequency. In contrast，the grid turbulence doesn’t have such a magnificent low frequency effect， the range of background noise arose from the PIV can be thus observed. The error spectrum by the synthetic PIV without considering the spatial correlation of the speed is consisted of bias error and random error. The bias error results in different characteristics with respect to the flow field， on the other hand， the random noise would lead to white noise.
The change of local number density of the particles arose from the wake flow will affect the cross correlation in the PIV result. This research points out a fact that the correlation coefficient is related to the flow field. As a result ,it is not true to extend acceptable data from some regions of flow field to a criterion justifying the whole domain. Instead, it is necessary to set the threshold according to the characteristics of each flow region to avoid over-detecting the flow field information.
Increasing the incoming light intensity increases the resolution of gray level， which can improve the ability of disturbance detecting；hence increasing the exposure time properly can lower down the high frequency noise. However， overexposing results in image blur；so does the error by increasing the integral scale. From the spectrum view， it shows that the high frequency noise increases apparently with the increasing exposure time. Thus, the exposure time should not exceed 1 pixel for the image blur.
Synthetic PIV shows the two characters of real particle image: gathering and local uniformity. Using different parameters such as particle size or particle number to discuss the error and the integral scale of the image can be regarded as the quantitative parameters for the image quality. And from the simulation results， we can know that the gathering particles have major contribution to determination of particle velocity. Although the media filter can reduce the effect of the gathering， it still can’t eliminate the image of gathering particle and it may result in the uncertainty in calculation of particle velocity.

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