Assessment of probability density function based on POD reduced-order model for ensemble-based data assimilation

Ryota Kikuchi, Takashi Misaka, Shigeru Obayashi

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


An integrated method of a proper orthogonal decomposition based reduced-order model (ROM) and data assimilation is proposed for the real-time prediction of an unsteady flow field. In this paper, a particle filter (PF) and an ensemble Kalman filter (EnKF) are compared for data assimilation and the difference in the predicted flow fields is evaluated focusing on the probability density function (PDF) of the model variables. The proposed method is demonstrated using identical twin experiments of an unsteady flow field around a circular cylinder at the Reynolds number of 1000. The PF and EnKF are employed to estimate temporal coefficients of the ROM based on the observed velocity components in the wake of the circular cylinder. The prediction accuracy of ROM-PF is significantly better than that of ROM-EnKF due to the flexibility of PF for representing a PDF compared to EnKF. Furthermore, the proposed method reproduces the unsteady flow field several orders faster than the reference numerical simulation based on the Navier-Stokes equations.

Original languageEnglish
Article number051403
JournalFluid Dynamics Research
Issue number5
Publication statusPublished - 2015 Sep 23


  • data assimilation
  • proper orthogonal decomposition
  • reduced-order model
  • von Kármán vortex streets

ASJC Scopus subject areas

  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Fluid Flow and Transfer Processes


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