Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.668898
Title: An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications
Author: Zimoń, Małgorzata Jadwiga
ISNI:       0000 0004 5367 8456
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2015
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Abstract:
Particle-based fluid simulations can be utilised to study phenomena ranging from galaxyscale, using smoothed particle hydrodynamics, plasma physics with particle-in-cell methods, aerospace re-entry problems, using direct simulation Monte Carlo, down to chemical, biological and fluid properties at the nanoscale with molecular dynamics and dissipative particle dynamics. The information generated by particle methods, such as molecular dynamics, is converted to macroscopic observables by means of statistical averaging. A significant drawback of nano- or micro-scale modelling is the substantial noise associated with particle techniques, which disturbs the analysis of the results. The uncertainty in the mean of the ensemble is due to fluctuations caused e.g. by additional forcing terms (thermostats). Extracting the genuine information from indirect, noisy measurements is analogous to solving the ill-posed statistical inverse problem, where the object of interest is not easily accessible. The presence of noise in the data can be reduced by averaging over a large number of samples, but the computational intensity of the simulations would then be substantially increased. In order to improve the efficiency of estimating the unknown structure from the disturbed observations, a number of decomposition techniques have been applied, including: proper orthogonal decomposition, singular spectrum analysis, random QR de-noising, wavelet transform, and empirical mode decomposition. In the present work, the strengths and weaknesses of each approach, and their extensions to solving statistical inverse problems for particle simulations, are evaluated. Furthermore, we propose several novel combinations of these methods, that have the capability to improve the signal-to-noise ratio and reduce the computational cost.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.668898  DOI: Not available
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