An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications

Malgorzata J. Zimon, Robert Prosser, David R. Emerson, Matthew Borg, Bray David, Leopold Grinberg, Jason Reese

Research output: Contribution to journalArticlepeer-review

Abstract

Filtering of particle-based simulation data can lead to reduced computational costs and enable more efficient information transfer in multi-scale modelling. This paper compares the effectiveness of various signal processing methods to reduce numerical noise and capture the structures of nano-flow systems. In addition, a novel combination of these algorithms is introduced, showing the potential of hybrid strategies to improve further the de-noising performance for time-dependent measurements. The methods were tested on velocity and density fields, obtained from simulations performed with molecular dynamics and dissipative particle dynamics. Comparisons between the algorithms are given in terms of performance, quality of the results and sensitivity to the choice of input parameters. The results provide useful insights on strategies for the analysis of particle-based data and the reduction of computational costs in obtaining ensemble solutions.
Original languageEnglish
Pages (from-to)380–394
JournalJournal of Computational Physics
Volume325
Early online date26 Aug 2016
DOIs
Publication statusPublished - 2016

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