Repository: Freie Universit├Ąt Berlin, Math Department

On identification of self-similar characteristics using the Tensor Train decomposition method with application to channel turbulence flow

von Larcher, T. and Klein, R. (2017) On identification of self-similar characteristics using the Tensor Train decomposition method with application to channel turbulence flow. Theoretical and Computational Fluid Dynamics . pp. 1-22. ISSN 0935-4964 (Print) 1432-2250 (Online) (Submitted)

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Official URL: https://arxiv.org/abs/1708.07780

Abstract

A study on the application of the Tensor Train decomposition method to 3D direct numerical simulation data of channel turbulence flow is presented. The approach is validated with respect to compression rate and storage requirement. In tests with synthetic data, it is found that grid-aligned self-similar patterns are well captured, and also the application to non grid-aligned self-similarity yields satisfying results. It is observed that the shape of the input Tensor significantly affects the compression rate. Applied to data of channel turbulent flow, the Tensor Train format allows for surprisingly high compression rates whilst ensuring low relative errors.

Item Type:Article
Additional Information:SFB 1114 Preprint 08/2017: arXiv:1708.07780
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics > Geophysical Fluid Dynamics Group
ID Code:2100
Deposited By: Silvia Hoemke
Deposited On:28 Aug 2017 10:23
Last Modified:24 Oct 2017 14:54

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