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Analysis of the filtering effect of the stochastic estimation and accuracy improvement by sensor location optimization

Analysis of the filtering effect of the stochastic estimation and accuracy improvement by sensor... The reconstruction of the flow behind a backward-facing step at a Reynolds number of 60,000 using linear stochastic estimation (LSE) and modified LSE (with or without multi-time-delay) is investigated. In particular, the turbulent spatial integral length scales estimated for several sensor configurations are studied. The estimation of the proper orthogonal decomposition (POD) modes is also performed in order to show the limitations of the SE for complex flows, for which taking into account only a few POD modes may not be enough to represent the flow dynamics. The importance of the sensor locations on the estimation is also emphasized, and the opportunity to use a sensor location optimization algorithm is investigated. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Analysis of the filtering effect of the stochastic estimation and accuracy improvement by sensor location optimization

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References (35)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Engineering Fluid Dynamics; Fluid- and Aerodynamics; Engineering Thermodynamics, Heat and Mass Transfer
ISSN
0723-4864
eISSN
1432-1114
DOI
10.1007/s00348-016-2276-8
Publisher site
See Article on Publisher Site

Abstract

The reconstruction of the flow behind a backward-facing step at a Reynolds number of 60,000 using linear stochastic estimation (LSE) and modified LSE (with or without multi-time-delay) is investigated. In particular, the turbulent spatial integral length scales estimated for several sensor configurations are studied. The estimation of the proper orthogonal decomposition (POD) modes is also performed in order to show the limitations of the SE for complex flows, for which taking into account only a few POD modes may not be enough to represent the flow dynamics. The importance of the sensor locations on the estimation is also emphasized, and the opportunity to use a sensor location optimization algorithm is investigated.

Journal

Experiments in FluidsSpringer Journals

Published: Nov 19, 2016

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