A novel coupling of noise reduction algorithms for particle flow simulations

M.J. Zimoń, J.M. Reese, D.R. Emerson

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 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.

LanguageEnglish
Pages169-190
Number of pages22
JournalJournal of Computational Physics
Volume321
Early online date27 May 2016
DOIs
Publication statusPublished - 15 Sep 2016

Fingerprint

Flow simulation
Noise abatement
noise reduction
Decomposition
decomposition
simulation
flow separation
Wavelet analysis
liquid flow
estimators
Phase separation
wavelet analysis
Molecular dynamics
signal processing
Signal to noise ratio
Signal processing
liquid phases
signal to noise ratios
velocity distribution
methodology

Keywords

  • dissipative particle dynamics
  • molecular dynamics
  • noise reduction
  • particle-based simulations
  • wavelet thresholding
  • windowed proper orthogonal decomposition

Cite this

Zimoń, M.J. ; Reese, J.M. ; Emerson, D.R. / A novel coupling of noise reduction algorithms for particle flow simulations. In: Journal of Computational Physics. 2016 ; Vol. 321. pp. 169-190.
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A novel coupling of noise reduction algorithms for particle flow simulations. / Zimoń, M.J.; Reese, J.M.; Emerson, D.R.

In: Journal of Computational Physics, Vol. 321, 15.09.2016, p. 169-190.

Research output: Contribution to journalArticle

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AB - 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.

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