本研究中利用粒子影像測速儀 ( Particle Image Velocimetry ) 於圓柱和方柱之條件下之量測速度的結果，探討動力特徵分析 ( Dynamic Mode Decomposition, DMD )、正交特徵分解 ( Proper Orthogonal Decomposition, POD ) 和Period - averaged processing的方法於近尾流區之紊態數據之應用。

動力特徵分析利於分辨出變化率高之流場特徵，在圓柱流場和方柱流場的條件之下，與正交特徵分解互相對照，可清楚分析出約在30 Hz至50 Hz之Secondary vortex頻率。應用動力特徵分析的高敏感性，只使用0.05秒的粒子影像張數，便可得具有意義之分析結果；但使用過多的資料量進行動力特徵分析，亦可能使分析的結果失真。使用Period - averaged processing處理方法移除圓柱流場中的類週期頻率 ( quasi - periodic motion ) 和實驗系統雜訊後，可獲得與動力特徵分析所得的相同Secondary vortex頻率。使用正交特徵分解法分析Period - averaged processing處理後的紊態數據和使用在流場不同位置的數據進行統計平均，亦可清楚得知Secondary vortex的存在及其產生之頻率。

The description of coherent features of fluid flow is essential for understanding fluid-dynamical and transport processes. With the help of particle image velocimetry (PIV), the velocity profiles of the circular cylinder flow field and the square cylinder flow field can be obtained. Dynamic mode decomposition (DMD), proper orthogonal decomposition (POD), and period-averaged processing (PAP) are then introduced to turbulence to extract dynamic information from different kinds of flow fields. The results of dynamic mode decomposition clearly show the features of the Kármán vortex street in the circular cylinder and the square cylinder flow fields. The DMD analysis clearly shows that there are secondary vortex signals between 30 Hz to 50 Hz. Due to high sensitivity, DMD could analyze data only in 0.5 seconds to acquire valid results. On the other hand, DMD will be intoxicated if we import an inappropriate amount of data. In addition, the period-averaged processing method is applied to filter quasi-periodic motion and obtain secondary organized motion in the circular cylinder flow. Furthermore, to understand the secondary vortex and its features, POD is also applied to analyze PAP data.

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