Low dimensional models from large datasets of complex flows using full orthogonalization Arnoldi dynamic mode decomposition

Sreevatsa Anantharamu, Praveen Kumar, Krishnan Mahesh

Research output: Contribution to conferencePaper

Abstract

Dynamic Mode Decomposition (DMD) is being used in recent years to analyze and derive low order models of complex systems including fluid flows. Anantharamu & Mahesh (2019) proposed a novel DMD algorithm suitable for analysis of large datasets, which is used here to analyze the complex flow field of a reverse rotating propeller attached to hull. The datasets employed in the present work are obtained from the large eddy simulation results of Verma et al. (2012). The employed DMD algorithm is well-suited for such large datasets due to its memory and computational efficiency, and better accuracy, compared to other popular streaming DMD algorithms. The DMD spectra for both datasets show dominant peaks consistent with the previously reported force spectra. The corresponding DMD modes are analyzed for their relevence to overall dynamics.

Original languageEnglish (US)
StatePublished - Jan 1 2019
Event11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019 - Southampton, United Kingdom
Duration: Jul 30 2019Aug 2 2019

Conference

Conference11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019
CountryUnited Kingdom
CitySouthampton
Period7/30/198/2/19

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Anantharamu, S., Kumar, P., & Mahesh, K. (2019). Low dimensional models from large datasets of complex flows using full orthogonalization Arnoldi dynamic mode decomposition. Paper presented at 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019, Southampton, United Kingdom.