Imperial College London
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DrAndrewWynn

Faculty of Engineering, Department of Aeronautics

Reader in Control and Optimization
 
 
 
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Contact

 

+44 (0)20 7594 5047a.wynn Website

 
 
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Location

 

340City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Krol:2017:10.1017/jfm.2017.340,
author = {Krol, J and Wynn, A},
doi = {10.1017/jfm.2017.340},
journal = {Journal of Fluid Mechanics},
pages = {133--166},
title = {Dynamic reconstruction and data reconstruction for subsampled or irregularly sampled data},
url = {http://dx.doi.org/10.1017/jfm.2017.340},
volume = {825},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The Nyquist–Shannon criterion indicates the sample rate necessary to identify information with particular frequency content from a dynamical system. However, in experimental applications such as the interrogation of a flow field using particle image velocimetry (PIV), it may be impracticable or expensive to obtain data at the desired temporal resolution. To address this problem, we propose a new approach to identify temporal information from undersampled data, using ideas from modal decomposition algorithms such as dynamic mode decomposition (DMD) and optimal mode decomposition (OMD). The novel method takes a vector-valued signal, such as an ensemble of PIV snapshots, sampled at random time instances (but at sub-Nyquist rate) and projects onto a low-order subspace. Subsequently, dynamical characteristics, such as frequencies and growth rates, are approximated by iteratively approximating the flow evolution by a low-order model and solving a certain convex optimisation problem. The methodology is demonstrated on three dynamical systems, a synthetic sinusoid, the cylinder wake at Reynolds number and turbulent flow past the axisymmetric bullet-shaped body. In all cases the algorithm correctly identifies the characteristic frequencies and oscillatory structures present in the flow.
AU  - Krol,J
AU  - Wynn,A
DO  - 10.1017/jfm.2017.340
EP  - 166
PY  - 2017///
SN  - 0022-1120
SP  - 133
TI  - Dynamic reconstruction and data reconstruction for subsampled or irregularly sampled data
T2  - Journal of Fluid Mechanics
UR  - http://dx.doi.org/10.1017/jfm.2017.340
UR  - http://hdl.handle.net/10044/1/49467
VL  - 825
ER  -
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