A novel coupling of noise reduction algorithms for particle flow simulations

JOURNAL OF COMPUTATIONAL PHYSICS

卷:321

Article

Zimon, M. J.^1,2  Reese, J. M.³  Emerson, D. R.⁴ 

[1] Univ Manchester, Sch Mech Aerosp & Civil Engn, Manchester M13 9PL, Lancs, England

[2] Univ Strathclyde, Dept Mech & Aerosp Engn, James Weir Fluids Lab, Glasgow G1 1XJ, Lanark, Scotland

[3] Univ Edinburgh, Sch Engn, Edinburgh EH9 3JL, Midlothian, Scotland

[4] STFC Daresbury Lab, Dept Comp Sci, Warrington WA4 4AD, Cheshire, England

关键词: Noise reduction;    Particle-based simulations;    Molecular dynamics;    Dissipative particle dynamics;    Windowed proper orthogonal;    decomposition;    Wavelet thresholding;   

DOI  :  10.1016/j.jcp.2016.05.049

来源: Elsevier

【 摘 要 】

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. (C) 2016 Published by Elsevier Inc.

【 授权许可】

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