Dynamic mode decomposition for non-uniformly sampled data

Dynamic mode decomposition for non-uniformly sampled data We propose an original approach to estimate dynamic mode decomposition (DMD) modes from non-uniformly sampled data. The proposed strategy processes a time-resolved sequence of flow snapshots in three steps. First, a reduced-order modeling of the non-missing data is made by proper orthogonal decomposition to obtain a low-order description of the state space. Second, the missing data are determined with maximum likelihood by coupling a linear dynamical state-space model with the Expectation-Maximization algorithm. Third, the DMD modes are finally estimated on the reconstructed data with a multiple linear regression method called orthonormalized partial least squares regression. This methodology is assessed for the flow past a NACA0012 airfoil at 20° of angle of attack and a Reynolds number of 103. The flow measurements are obtained with time-resolved particle image velocimetry and artificially subsampled at different ratios of missing data. The results show that the proposed method can reproduce the dominant DMD modes and the main structures of the flow fields for 50 and 75 % of missing data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Dynamic mode decomposition for non-uniformly sampled data

Loading next page...
 
/lp/springer_journal/dynamic-mode-decomposition-for-non-uniformly-sampled-data-KHElaOB2tH
Publisher
Springer Berlin Heidelberg
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
D.O.I.
10.1007/s00348-016-2165-1
Publisher site
See Article on Publisher Site

Abstract

We propose an original approach to estimate dynamic mode decomposition (DMD) modes from non-uniformly sampled data. The proposed strategy processes a time-resolved sequence of flow snapshots in three steps. First, a reduced-order modeling of the non-missing data is made by proper orthogonal decomposition to obtain a low-order description of the state space. Second, the missing data are determined with maximum likelihood by coupling a linear dynamical state-space model with the Expectation-Maximization algorithm. Third, the DMD modes are finally estimated on the reconstructed data with a multiple linear regression method called orthonormalized partial least squares regression. This methodology is assessed for the flow past a NACA0012 airfoil at 20° of angle of attack and a Reynolds number of 103. The flow measurements are obtained with time-resolved particle image velocimetry and artificially subsampled at different ratios of missing data. The results show that the proposed method can reproduce the dominant DMD modes and the main structures of the flow fields for 50 and 75 % of missing data.

Journal

Experiments in FluidsSpringer Journals

Published: May 14, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off