Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling

Benjamin Peherstorfer

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents a model reduction approach for problems with coherent structures that propagate over time, such as convection-dominated fows and wave-type phenomena. Traditional model reduction methods have difculties with these transport-dominated problems because propagating coherent structures typically introduce high-dimensional features that require high-dimensional approximation spaces. The approach proposed in this work exploits the locality in space and time of propagating coherent structures to derive efcient reduced models. Full-model solutions are approximated locally in time via local reduced spaces that are adapted with basis updates during time stepping. The basis updates are derived from querying the full model at a few selected spatial coordinates. A core contribution of this work is an adaptive sampling scheme for selecting at which components to query the full model to compute basis updates. The presented analysis shows that, in probability, the more local the coherent structure is in space, the fewer full-model samples are required to adapt the reduced basis with the proposed adaptive sampling scheme. Numerical results on benchmark examples with interacting wave-type structures and time-varying transport speeds and on a model combustor of a single-element rocket engine demonstrate the wide applicability of the proposed approach and runtime speedups of up to one order of magnitude compared to full models and traditional reduced models.

Original languageEnglish (US)
Article number19M1257275
Pages (from-to)A2803-A2836
JournalSIAM Journal on Scientific Computing
Volume42
Issue number5
DOIs
StatePublished - 2020

Keywords

  • Empirical interpolation
  • Model reduction
  • Online adaptive model reduction
  • Proper orthogonal decomposition
  • Sparse sampling
  • Transport-dominated problems

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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