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Title: A novel coupling of noise reduction algorithms for particle flow simulations

Abstract

Proper orthogonal decomposition (POD) and its extension based on time-windows have been shown to greatly improve the effectiveness of recovering smooth ensemble solutions from noisy particle data. However, to successfully de-noise any molecular system, a large number of measurements still need to be provided. In order to achieve a better efficiency in processing time-dependent fields, we have combined POD with a well-established signal processing technique, wavelet-based thresholding. In this novel hybrid procedure, the wavelet filtering is applied within the POD domain and referred to as WAVinPOD. The algorithm exhibits promising results when applied to both synthetically generated signals and particle data. In this work, the simulations compare the performance of our new approach with standard POD or wavelet analysis in extracting smooth profiles from noisy velocity and density fields. Numerical examples include molecular dynamics and dissipative particle dynamics simulations of unsteady force- and shear-driven liquid flows, as well as phase separation phenomenon. Simulation results confirm that WAVinPOD preserves the dimensionality reduction obtained using POD, while improving its filtering properties through the sparse representation of data in wavelet basis. This paper shows that WAVinPOD outperforms the other estimators for both synthetically generated signals and particle-based measurements, achieving a higher signal-to-noise ratiomore » from a smaller number of samples. The new filtering methodology offers significant computational savings, particularly for multi-scale applications seeking to couple continuum informations with atomistic models. It is the first time that a rigorous analysis has compared de-noising techniques for particle-based fluid simulations.« less

Authors:
 [1];  [2];  [3];  [4]
  1. School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL (United Kingdom)
  2. (United Kingdom)
  3. School of Engineering, The University of Edinburgh, Edinburgh EH9 3JL (United Kingdom)
  4. Scientific Computing Department, STFC Daresbury Laboratory, Warrington WA4 4AD (United Kingdom)
Publication Date:
OSTI Identifier:
22572345
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Computational Physics; Journal Volume: 321; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; EFFICIENCY; LIQUID FLOW; LIQUIDS; MATHEMATICAL SOLUTIONS; MOLECULAR DYNAMICS METHOD; NOISE; PERFORMANCE; PROCESSING; SIGNALS; SIGNAL-TO-NOISE RATIO; SIMULATION; TIME DEPENDENCE

Citation Formats

Zimoń, M.J., E-mail: malgorzata.zimon@stfc.ac.uk, James Weir Fluids Lab, Mechanical and Aerospace Engineering Department, The University of Strathclyde, Glasgow G1 1XJ, Reese, J.M., and Emerson, D.R. A novel coupling of noise reduction algorithms for particle flow simulations. United States: N. p., 2016. Web. doi:10.1016/J.JCP.2016.05.049.
Zimoń, M.J., E-mail: malgorzata.zimon@stfc.ac.uk, James Weir Fluids Lab, Mechanical and Aerospace Engineering Department, The University of Strathclyde, Glasgow G1 1XJ, Reese, J.M., & Emerson, D.R. A novel coupling of noise reduction algorithms for particle flow simulations. United States. doi:10.1016/J.JCP.2016.05.049.
Zimoń, M.J., E-mail: malgorzata.zimon@stfc.ac.uk, James Weir Fluids Lab, Mechanical and Aerospace Engineering Department, The University of Strathclyde, Glasgow G1 1XJ, Reese, J.M., and Emerson, D.R. 2016. "A novel coupling of noise reduction algorithms for particle flow simulations". United States. doi:10.1016/J.JCP.2016.05.049.
@article{osti_22572345,
title = {A novel coupling of noise reduction algorithms for particle flow simulations},
author = {Zimoń, M.J., E-mail: malgorzata.zimon@stfc.ac.uk and James Weir Fluids Lab, Mechanical and Aerospace Engineering Department, The University of Strathclyde, Glasgow G1 1XJ and Reese, J.M. and Emerson, D.R.},
abstractNote = {Proper orthogonal decomposition (POD) and its extension based on time-windows have been shown to greatly improve the effectiveness of recovering smooth ensemble solutions from noisy particle data. However, to successfully de-noise any molecular system, a large number of measurements still need to be provided. In order to achieve a better efficiency in processing time-dependent fields, we have combined POD with a well-established signal processing technique, wavelet-based thresholding. In this novel hybrid procedure, the wavelet filtering is applied within the POD domain and referred to as WAVinPOD. The algorithm exhibits promising results when applied to both synthetically generated signals and particle data. In this work, the simulations compare the performance of our new approach with standard POD or wavelet analysis in extracting smooth profiles from noisy velocity and density fields. Numerical examples include molecular dynamics and dissipative particle dynamics simulations of unsteady force- and shear-driven liquid flows, as well as phase separation phenomenon. Simulation results confirm that WAVinPOD preserves the dimensionality reduction obtained using POD, while improving its filtering properties through the sparse representation of data in wavelet basis. This paper shows that WAVinPOD outperforms the other estimators for both synthetically generated signals and particle-based measurements, achieving a higher signal-to-noise ratio from a smaller number of samples. The new filtering methodology offers significant computational savings, particularly for multi-scale applications seeking to couple continuum informations with atomistic models. It is the first time that a rigorous analysis has compared de-noising techniques for particle-based fluid simulations.},
doi = {10.1016/J.JCP.2016.05.049},
journal = {Journal of Computational Physics},
number = ,
volume = 321,
place = {United States},
year = 2016,
month = 9
}
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