Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows
Name
KGBBN17_AirflowSensing_paper.pdf
Description
Main article
Size
2.53 MB
Format
Adobe PDF
Checksum (MD5)
aa21c950135aaf9344c4f6f88f3f3205
Author(s)
Kramer, Boris
Grover, Piyush
Boufounos, Petros
Nabi, Saleh
Benosman, Mouhacine
Date Issued
June 2017
Journal
SIAM Journal on Applied Dynamical Systems
Publisher
Society for Industrial and Applied Mathematics
Citation
Kramer, Boris et al. “Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows.” SIAM Journal on Applied Dynamical Systems 16, 2 (January 2017): 1164–1196 © 2017 Society for Industrial and Applied Mathematics
Version
Final published version
Abstract
We present a sparse sensing framework based on dynamic mode decomposition (DMD) to identify flow regimes and bifurcations in large-scale thermofluid systems. Motivated by real-time sensing and control of thermal-fluid flows in buildings and equipment, we apply this method to a direct numerical simulation (DNS) data set of a two-dimensional laterally heated cavity. The resulting flow solutions can be divided into several regimes, ranging from steady to chaotic flow. The DMD modes and eigenvalues capture the main temporal and spatial scales in the dynamics belonging to different regimes. Our proposed classification method is data driven, robust w.r.t. measurement noise, and exploits the dynamics extracted from the DMD method. Namely, we construct an augmented DMD basis, with “built-in” dynamics, given by the DMD eigenvalues. This allows us to employ a short time series of data from sensors, to more robustly classify flow regimes, particularly in the presence of measurement noise. We also exploit the incoherence exhibited among the data generated by different regimes, which persists even if the number of measurements is small compared to the dimension of the DNS data. The data-driven regime identification algorithm can enable robust low-order modeling of flows for state estimation and control.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1137/15M104565X
