Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information
Improvements to two widely used particle-image velocimetry (PIV) algorithms, e.g., multi-grid and iterative image deformation cross-correlations, are proposed here to reduce the computational costs associated with time-resolved PIV (TR-PIV) data-processing. TR-PIV typically involves capturing signif...
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sg-ntu-dr.10356-1010652023-03-04T17:19:52Z Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information New, T. H. Shi, Shengxian. Liu, Yingzheng. School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Improvements to two widely used particle-image velocimetry (PIV) algorithms, e.g., multi-grid and iterative image deformation cross-correlations, are proposed here to reduce the computational costs associated with time-resolved PIV (TR-PIV) data-processing. TR-PIV typically involves capturing significant time-series particle-image datasets across to allow statistically meaningful temporal and spectral analyses; hence considerable computational cost-savings can be realised. The improvements involve using the historical particle displacement field and its variation to determine the required window offsets and image deformations in the above-mentioned algorithms, respectively. In this case, cross-correlation based on the smallest interrogation window size can be used directly instead of multi-pass cross-correlations based on decreasing interrogation window sizes. To evaluate their efficacy, the proposed improvements were implemented and evaluated using synthetic PIV images of a Rankine vortex flow, numerical solutions for a square cylinder wake flow, as well as actual experimental time-series TR-PIV measurements. Comparisons show that the proposed improvements save up to 50% computational time while maintaining relatively similar measurement accuracy levels as conventional algorithms. In particular, the new algorithms successfully resolve unsteady flow fields where particle displacements vary by more than 20% between successive particle-images, where error propagations associated with large displacement variations are mitigated by employing suitable recalculation thresholds. Accepted version 2013-10-23T05:14:34Z 2019-12-06T20:32:56Z 2013-10-23T05:14:34Z 2019-12-06T20:32:56Z 2013 2013 Journal Article Shi, S. X., New, T. H., & Liu, Y. Z. (2013). Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information. Flow measurement and instrumentation, 29, 67-79. 0955-5986 https://hdl.handle.net/10356/101065 http://hdl.handle.net/10220/16698 10.1016/j.flowmeasinst.2012.10.011 174846 en Flow measurement and instrumentation © 2012 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Flow Measurement and Instrumentation, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.flowmeasinst.2012.10.011]. application/pdf |
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DRNTU::Engineering::Mechanical engineering New, T. H. Shi, Shengxian. Liu, Yingzheng. Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information |
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Improvements to two widely used particle-image velocimetry (PIV) algorithms, e.g., multi-grid and iterative image deformation cross-correlations, are proposed here to reduce the computational costs associated with time-resolved PIV (TR-PIV) data-processing. TR-PIV typically involves capturing significant time-series particle-image datasets across to allow statistically meaningful temporal and spectral analyses; hence considerable computational cost-savings can be realised. The improvements involve using the historical particle displacement field and its variation to determine the required window offsets and image deformations in the above-mentioned algorithms, respectively. In this case, cross-correlation based on the smallest interrogation window size can be used directly instead of multi-pass cross-correlations based on decreasing interrogation window sizes. To evaluate their efficacy, the proposed improvements were implemented and evaluated using synthetic PIV images of a Rankine vortex flow, numerical solutions for a square cylinder wake flow, as well as actual experimental time-series TR-PIV measurements. Comparisons show that the proposed improvements save up to 50% computational time while maintaining relatively similar measurement accuracy levels as conventional algorithms. In particular, the new algorithms successfully resolve unsteady flow fields where particle displacements vary by more than 20% between successive particle-images, where error propagations associated with large displacement variations are mitigated by employing suitable recalculation thresholds. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering New, T. H. Shi, Shengxian. Liu, Yingzheng. |
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Article |
author |
New, T. H. Shi, Shengxian. Liu, Yingzheng. |
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New, T. H. |
title |
Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information |
title_short |
Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information |
title_full |
Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information |
title_fullStr |
Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information |
title_full_unstemmed |
Improvements to time-series TR-PIV algorithms using historical displacement and displacement variation information |
title_sort |
improvements to time-series tr-piv algorithms using historical displacement and displacement variation information |
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2013 |
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https://hdl.handle.net/10356/101065 http://hdl.handle.net/10220/16698 |
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1759855852196462592 |