Particle tracking velocimetry with an ant colony optimization algorithm

Relaxation algorithm-based PTV with dual calculation method and its application in addressing particle saltation

“…Compared with the 2D particle distribution, the 3D particle coordinates provide naturally more exploitable information in particle matching. That is, provided 3D particle coordinates are precisely given, 3D particle matching is easier for any PTV algorithms (Ohmi et al 2010). Therefore, to conduct a more rigorous test, random erasing is applied to frame t ?…”

“…The test is conducted using synthetic particle images of PIV Standard Image project (Okamoto et al 2000a, b), which has been extensively used to verify PTV/PIV algorithms (Brevis et al 2011;Cardwell et al 2011;Mikheev and Zubtsov 2008;Ohmi and Li 2000;Ohmi and Panday 2009;Ohmi et al 2010). Three sets of 2D image series, numbered as 01, 04 and 23, are used here.…”

An ant colony optimization based stereoscopic particle pairing algorithm for three-dimensional particle tracking velocimetry

“…This is a kind of algorithm for optimal solution problems and seems attractive for the particle tracking in the sense that the method uses a concept of group intelligence. In this regard, the ACO algorithm has already been applied by Takagi [20] and two of the present authors [21] with successful results for the time-differential particle images. Although the epipolar line particle pairing between spatiodifferential particle images is not based on the same type of image disparity as in the time-differential images, the ACO algorithm can also be an effective particle pairing strategy for the former case, because the group intelligence principle of ACO could work well for minimizing the mismatch of the particle projection point and the relevant epipolar line.…”

A particle tracking velocimetry algorithm based on the Voronoi diagram

“…Yang Zhang 1 , Yuan Wang 1 , Bin Yang 2 and Wenbo He 1 neural-network PTV (Knaak et al 1997, Labonte 1999, the self-organizing map (SOM) PTV (Ohmi 2003), the geneticalgorithm-based PTV (Doh et al 2002, Ohmi andPanday 2009) and the PTV based on ant-colony optimization (Ohmi et al 2010). For example, the Hopfield neural-network PTV (Knaak et al 1997) is designed to minimize certain Lyapunov energy function, but appropriate Lyapunov energy functions are difficult to define.…”