Towards model order reduction for fluid-thermal analysis

Kento Kaneko, Ping Hsuan Tsai, Paul Fischer

Research output: Contribution to journalArticlepeer-review


The authors develop basic components of a parametric model-order reduction (pMOR) procedure targeting high Rayleigh-number buoyancy-driven flows. The pMOR is based on Galerkin formulation of the governing Boussinesq equations using eigenfunction bases derived from proper orthogonal decomposition. The advantages of pMOR over parametric interpolation are demonstrated for quantities of interest (QOIs) with linear and nonlinear dependencies on the solution in low Rayleigh-number cases. Constraint-base and Leray-type regularizations are shown to offer significant advantages over the standard reduced-order Galerkin formulation in a variety of examples including 3D Rayleigh–Bénard flow at Rayleigh number 107 and turbulent flow in a half-pipe at Reynolds number 5300.

Original languageEnglish (US)
Article number110866
JournalNuclear Engineering and Design
StatePublished - Dec 15 2020


  • Boussinesq
  • Model order reduction
  • Parametric model order reduction
  • Proper orthogonal decomposition (POD)
  • Reduced-order models
  • Spectral element method

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Materials Science(all)
  • Safety, Risk, Reliability and Quality
  • Waste Management and Disposal
  • Mechanical Engineering


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