Bias in particle tracking acceleration measurement
Bias in particle tracking acceleration measurement
Abstract: We investigate sources of systematic error (bias) in acceleration statistics derived from Lagrangian particle tracking data and demonstrate techniques to eliminate or minimise these bias errors introduced during processing. Numerical simulations of particle tracking experiments in isotropic turbulence show that the main sources of bias error arise from noise due to random position errors and selection biases introduced during numerical differentiation. We outline the use of independent measurements and filtering schemes to eliminate these biases. Moreover, we test the validity of our approach in estimating the statistical moments and probability densities of the Lagrangian acceleration. Finally, we apply these techniques to experimental particle tracking data and demonstrate their validity in practice with comparisons to available data from the literature. The general approach, which is not limited to acceleration statistics, can be applied with as few as two cameras and permits a substantial reduction in the position accuracy and sampling rate required to adequately measure the statistics of Lagrangian acceleration. Graphical abstract: Sources of bias error in Lagrangian Particle Tracking measurements are explored. Methods are presented and validated to correct acceleration statistics for the main sources of systematic errors introduced by random position error and filtering, allowing for a substantial improvement in the effective temporal resolution of particle tracking measurements. [Figure not available: see fulltext.].
Lawson, John M.
4e0b1895-51c5-41e6-9322-7f79e76e0e4c
Bodenschatz, Eberhard
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Lalescu, Cristian C.
1745485e-3bd0-4ef2-91d0-718607afc6c2
Wilczek, Michael
42f0aded-3a8d-458c-be29-34c893a52156
1 November 2018
Lawson, John M.
4e0b1895-51c5-41e6-9322-7f79e76e0e4c
Bodenschatz, Eberhard
e8658e96-6c7b-4385-94fa-a23fb68edeb5
Lalescu, Cristian C.
1745485e-3bd0-4ef2-91d0-718607afc6c2
Wilczek, Michael
42f0aded-3a8d-458c-be29-34c893a52156
Lawson, John M., Bodenschatz, Eberhard, Lalescu, Cristian C. and Wilczek, Michael
(2018)
Bias in particle tracking acceleration measurement.
Experiments in Fluids, 59 (11), [172].
(doi:10.1007/s00348-018-2622-0).
Abstract
Abstract: We investigate sources of systematic error (bias) in acceleration statistics derived from Lagrangian particle tracking data and demonstrate techniques to eliminate or minimise these bias errors introduced during processing. Numerical simulations of particle tracking experiments in isotropic turbulence show that the main sources of bias error arise from noise due to random position errors and selection biases introduced during numerical differentiation. We outline the use of independent measurements and filtering schemes to eliminate these biases. Moreover, we test the validity of our approach in estimating the statistical moments and probability densities of the Lagrangian acceleration. Finally, we apply these techniques to experimental particle tracking data and demonstrate their validity in practice with comparisons to available data from the literature. The general approach, which is not limited to acceleration statistics, can be applied with as few as two cameras and permits a substantial reduction in the position accuracy and sampling rate required to adequately measure the statistics of Lagrangian acceleration. Graphical abstract: Sources of bias error in Lagrangian Particle Tracking measurements are explored. Methods are presented and validated to correct acceleration statistics for the main sources of systematic errors introduced by random position error and filtering, allowing for a substantial improvement in the effective temporal resolution of particle tracking measurements. [Figure not available: see fulltext.].
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Published date: 1 November 2018
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Open access funding provided by the Max Planck Society. The authors gratefully acknowledge the financial support of the Max Planck Society and EuHIT: European High-Performance Infrastructures in Turbulence, which is funded by the European Commission Framework Program 7 (Grant no. 312778). Part of the computations were performed on the clusters of the Max Planck Computing and Data Facility. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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© 2018, The Author(s).
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Copyright 2018 Elsevier B.V., All rights reserved.
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Local EPrints ID: 455437
URI: http://eprints.soton.ac.uk/id/eprint/455437
ISSN: 0723-4864
PURE UUID: d8536a53-7c5c-432e-9179-323f51c43dd7
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Date deposited: 21 Mar 2022 17:55
Last modified: 18 Mar 2024 03:49
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Author:
Eberhard Bodenschatz
Author:
Cristian C. Lalescu
Author:
Michael Wilczek
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