어파인변환 기반 하이브리드 PIV

Abstract

Key Words: PIV(입자영상유속계), PTV(입자추적유속계), Virtual Image(가상영상), Cross-Correlation PIV(상호상관PIV), 2F-Match Probability Method(2프레임확률일치법), Affine Transformation(어파인변환)초록:입자추적유속계(PTV)는나노및바이오분야의유체유동장에서는각입자들을추적하여속도측정을하는관계로많은강점이있다. 그러나측정원리상보간에의한속도장측정오차를피할수없는관계로PTV기술을사용함에있어서제한적이었다. 본연구에서는어파인변환알고리듬을PIV 및PTV측정에도입함으로써보간에의한오차를줄일수있는어파인변환기반하이브리드PIV알고리듬을구축하였다. 구축된알고리듬에대한성능평가를위하여Green-Taylor와유동의수치적데이터를이용한가상영상에대한시험을실시하였으며,이로부터입자수가2000개이상일때최적의측정성능임을확인하였으며상호상관PIV법및확률일치PTV법보다우수한측정성능임을확인하였다. 나아가길이비2:1(6cm x 3cm)인장방형물체후류(Re=5,300)에대한실험영상에대한실제계산을통하여구축된알고리듬에대한측정성능의우수성을확인하였다.Abstract: Since PTV (particle tracking velocimetry) provides velocity vectors by tracking each particle in afluid flow, it has significant benefits when used for nano- and bio-fluid flows. However, PTV has only beenused for limited flow fields because interpolation data loss is inevitable in PTV in principle. In this paper, ahybrid particle image velocimetry (PIV) algorithm that eliminates interpolation data loss was constructed byusing an affine transformation. For the evaluation of the performance of the constructed hybrid PIV algorithm,an artificial image test was performed using Green–Taylor vortex data. The constructed algorithm was testedon experimental images of the wake flow (Re = 5,300) of a rectangular body (6 cm × 3 cm), and wasdemonstrated to provide excellent results.