seeding concentration in the PSV method. In the PIV method, particles moving within the light sheet may be recorded photographically as pairs of particle image. The local fluid velocity was determined statistically from a small interrogating window on the developed photograph by sequentially measuring the displacement of the particle images within each window. The particle displacement within an interrogating window can be found by a variety of methods such as the Young’s fringe method (Meynart 1980 & 1982, Reynolds, et. al. 1985) or the 2-D auto-correlation analysis (He et. al. 1984, Vogel and Lauterborn 1988). One of the difficulties in implementing the PIV method is that there exists no characteristic to distinguish first image from second image on the double-exposure PIV photograph. As a result, measurement of the particle image displacement can not determine the direction of the fluid velocity, and the velocity was ambiguous in direction. To resolve the directional ambiguity, imaging shift (Adrian 1986 & 1988), and thus an introduced velocity, of a certain amount can be applied during the time interval between successive exposures. Subtracting the introduced velocity from the measured fluid velocity, flow direction can be determined from the sign, positive or negative, of the velocity vector. However, this technique tends to complicate the experimental setup and the subsequent analysis. In addition, the multi-exposure particle image should not overlap imposes restrictions on the spatial resolution and the dynamic range of the velocity measurement.
3. Digital Particle Image Velocimetry (DPIV) Method: In order to eliminate the limitations of the PIV method, another technique, called the digital particle image velocimetry method, was proposed in various manners since the late 1980s. Instead of taking multi-exposure images, the DPIV method analyzed a series of time sequential images captured either from a high-speed camera or from a video camera, with each frame containing a single-exposure image. The captured digital images were stored directly to computer memory through A/D converter without conventional picture taking and film development. The major advantage of DPIV method is that both the magnitude and direction of the velocity vector can be determined from the cross-correlation analysis as a series of single-exposure images were available (Willert and Gharib 1991). The cross correlation analysis of two sequential images is also better than the multi-exposure, auto-correlation analysis of the PIV method. The cross-correlation, i.e. the convolution of two sequential images, set restrictions on the particle paring from image one to two, which reduces the opportunity of miscalculating the magnitude and direction of particle displacement. A major disadvantage of the DPIV method is that the time interval between sequential images are limited by the frame speed, usually 30 frames per second, of the recording device. Since velocity is determined from dividing the particle moving displacement with the time interval of sequential images, the dynamic range of the velocity measurement is thus limited to tens of centimeter per second for typical applications. Higher speed recording device is available at the present time, which moderately increases the dynamic range of velocity measurement at the expense of high cost and sacrificing image resolution. Light chopper was also devised (Dabiri and Gharib 1996) to control the time interval between sequential images, which complicates the operation of image capturing, however.
From the descriptions stated above, it is found that each flow visualization method has its advantages and also drawbacks. What’s interesting is that the advantages of one method happen to be the limitations of other methods. For instances, by analyzing multi-exposure particle image, the dynamic range of the velocity measurement of the PIV method is highly increased while the determination of flow direction is ambiguous and requires additional equipment such as the image shifting devices. The DPIV method determines the flow direction easily from a series of sequential images, allows denser seeding particles, but the dynamic range of velocity measurement is limited by the frame speed of the recording device. One way to preserve the advantages of both the PIV and DPIV methods is to analyze the flow field from a multi-exposure particle image using sequential image files. This is accomplished in the proposed alternating color image anemometry (ACIA) method by separating a multi-exposure particle image into sequential images of different color.
In the proposed ACIA method, the flow image was recorded by a three-chip, color CCD camera. The blue laser and green laser, deflected alternately from a 4 watt, Argon-Ion laser, were adopted as the light source. The laser beam was guided to a multi-facet, rotating mirror to generate alternating color, blue and green laser sheets. The particles seeded in the fluid was then illuminated by the two different, alternating color laser sheets. A single frame, even times exposure, alternating color particle image was then recorded by the red, blue, and green CCD-chip of a 3-CCD camera. Knowing that different color CCD chip has different sensitivity to the color of the incoming