Experiments in Fluids

, 47:553

An efficient simultaneous reconstruction technique for tomographic particle image velocimetry

Research Article

DOI: 10.1007/s00348-009-0728-0

Cite this article as:
Atkinson, C. & Soria, J. Exp Fluids (2009) 47: 553. doi:10.1007/s00348-009-0728-0

Abstract

To date, Tomo-PIV has involved the use of the multiplicative algebraic reconstruction technique (MART), where the intensity of each 3D voxel is iteratively corrected to satisfy one recorded projection, or pixel intensity, at a time. This results in reconstruction times of multiple hours for each velocity field and requires considerable computer memory in order to store the associated weighting coefficients and intensity values for each point in the volume. In this paper, a rapid and less memory intensive reconstruction algorithm is presented based on a multiplicative line-of-sight (MLOS) estimation that determines possible particle locations in the volume, followed by simultaneous iterative correction. Reconstructions of simulated images are presented for two simultaneous algorithms (SART and SMART) as well as the now standard MART algorithm, which indicate that the same accuracy as MART can be achieved 5.5 times faster or 77 times faster with 15 times less memory if the processing and storage of the weighting matrix is considered. Application of MLOS-SMART and MART to a turbulent boundary layer at Reθ = 2200 using a 4 camera Tomo-PIV system with a volume of 1,000 × 1,000 × 160 voxels is discussed. Results indicate improvements in reconstruction speed of 15 times that of MART with precalculated weighting matrix, or 65 times if calculation of the weighting matrix is considered. Furthermore the memory needed to store a large weighting matrix and volume intensity is reduced by almost 40 times in this case.

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Laboratory for Turbulence Research in Aerospace and Combustion, Department of Mechanical and Aerospace EngineeringMonash UniversityMelbourneAustralia

Personalised recommendations