Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers

Taraneh Sayadi, Peter J. Schmid

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Many fluid flows of engineering interest, though very complex in appearance, can be approximated by low-order models governed by a few modes, able to capture the dominant behavior (dynamics) of the system. This feature has fueled the development of various methodologies aimed at extracting dominant coherent structures from the flow. Some of the more general techniques are based on data-driven decompositions, most of which rely on performing a singular value decomposition (SVD) on a formulated snapshot (data) matrix. The amount of experimentally or numerically generated data expands as more detailed experimental measurements and increased computational resources become readily available. Consequently, the data matrix to be processed will consist of far more rows than columns, resulting in a so-called tall-and-skinny (TS) matrix. Ultimately, the SVD of such a TS data matrix can no longer be performed on a single processor, and parallel algorithms are necessary. The present study employs the parallel TSQR algorithm of (Demmel et al. in SIAM J Sci Comput 34(1):206–239, 2012), which is further used as a basis of the underlying parallel SVD. This algorithm is shown to scale well on machines with a large number of processors and, therefore, allows the decomposition of very large datasets. In addition, the simplicity of its implementation and the minimum required communication makes it suitable for integration in existing numerical solvers and data decomposition techniques. Examples that demonstrate the capabilities of highly parallel data decomposition algorithms include transitional processes in compressible boundary layers without and with induced flow separation.
Original languageEnglish (US)
Pages (from-to)415-428
Number of pages14
JournalTheoretical and Computational Fluid Dynamics
Volume30
Issue number5
DOIs
StatePublished - Oct 1 2016
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

ASJC Scopus subject areas

  • Computational Mechanics
  • General Engineering
  • Fluid Flow and Transfer Processes
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers'. Together they form a unique fingerprint.

Cite this