Low-Grid-Resolution-RANS-Based Data Assimilation of Time-Averaged Separated Flow Obtained by LES

Masamichi Nakamura, Yuta Ozawa, Taku Nonomura

Research output: Contribution to journalArticlepeer-review


The objective of this study is to obtain accurate flow field analysis results in a short computational time by using data assimilation, which increases the accuracy of Reynolds averaged Navier-Stokes (RANS) simulations with low grid resolution. The large-eddy simulation (LES) results are assimilated into RANS simulations. In those simulations, the turbulence-model parameters are optimised by an ensemble Kalman filter with a proposed method for adaptive hyperparameter optimisation. The target of calculations is the flow field around a square cylinder of the Reynolds number of approximately (Formula presented.). Only the surface pressure of the square cylinder is used as an observation variable. For this shape, the assimilated RANS flow field is similar to that given by the LES analysis, and the drag coefficient reproducibility is improved by (Formula presented.). The turbulence-model parameters are also used in the analyses of different cross-sectional shape and are found to improve the reproducibility of the flow field.

Original languageEnglish
Pages (from-to)167-185
Number of pages19
JournalInternational Journal of Computational Fluid Dynamics
Issue number2
Publication statusPublished - 2022


  • computational acceleration
  • Computational fluid analysis
  • data assimilation
  • LES
  • RANS
  • training data creation

ASJC Scopus subject areas

  • Computational Mechanics
  • Aerospace Engineering
  • Condensed Matter Physics
  • Energy Engineering and Power Technology
  • Mechanics of Materials
  • Mechanical Engineering


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