Multidimensional and multivariate empirical mode decomposition
Thirumalaisamy, Mruthun R.
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https://hdl.handle.net/2142/104911
Description
Title
Multidimensional and multivariate empirical mode decomposition
Author(s)
Thirumalaisamy, Mruthun R.
Issue Date
2019-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Ansell, Phillip J.
Department of Study
Aerospace Engineering
Discipline
Aerospace Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2019-08-23T20:01:15Z
Keyword(s)
empirical mode decomposition
modal analysis
Abstract
Over the last decade, Empirical Mode Decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational demands of multidimensional signal processing. The current work introduces an alternative approach to multivariate EMD, and by combining it with existing fast and adaptive algorithms, paves the way for performing multivariate EMD on multidimensional signals. The application of the algorithm developed through the current study, when applied to the Direct Numerical Simulation (DNS) of a flat-plate boundary layer (a large dataset), revealed the desired scale separation behaviour across multiple data channels. This proves that the algorithm could be useful for a broad range of future problems.
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