A data assimilation methodology for reconstructing turbulent flows around aircraft

Hiroshi Kato, Akira Yoshizawa, Genta Ueno, Shigeru Obayashi

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)


This paper proposes a new approach for the study of complex turbulent flows of aeronautics that integrates experimental fluid dynamics (EFD), employing methods such as wind tunnel experiments, and computational fluid dynamics (CFD) by using a data assimilation technique. The approach aims at representing complex turbulent flows more properly than conventional EFD and CFD approaches by estimating the proper angle of attack, the proper Mach number, and the proper turbulent viscosity, which are the three uncertainty factors in EFD and CFD. To this end, the ensemble transform Kalman filter (ETKF), a sequential advanced data assimilation method, is employed for the estimation and applied to transonic flows around the RAE 2822 airfoil (two-dimensional flow) and transonic flows around the ONERA M6 wing (three-dimensional flow). The results computed using the angles of attack, Mach numbers, and turbulent viscosities estimated by the ETKF diminish the discrepancies between the results of standard computations and experiments. These findings show the effectiveness of this approach, which combines EFD and CFD using data assimilation to represent complex turbulent flows.

Original languageEnglish
Pages (from-to)559-581
Number of pages23
JournalJournal of Computational Physics
Publication statusPublished - 2015 Feb 5


  • Data assimilation
  • Optimization
  • Turbulent flows
  • Uncertainty


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