Multidimensional and multivariate empirical mode decomposition

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Aerospace Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Thirumalaisamy, Mruthun R.

Contributors dc:contributor

• Ansell, Phillip J.

• empirical mode decomposition
• modal analysis

Statement dc:rights

• Copyright 2019 Mruthun Thirumalaisamy

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104911

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104911