Data assimilation for POD reduced-order model - Comparison of PF and EnKF

Ryota Kikuchi, Takashi Misaka, Shigeru Obayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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

Original languageEnglish
Title of host publicationFUSION 2014 - 17th International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788490123553
Publication statusPublished - 2014 Oct 3
Event17th International Conference on Information Fusion, FUSION 2014 - Salamanca, Spain
Duration: 2014 Jul 72014 Jul 10

Publication series

NameFUSION 2014 - 17th International Conference on Information Fusion

Other

Other17th International Conference on Information Fusion, FUSION 2014
CountrySpain
CitySalamanca
Period14/7/714/7/10

Keywords

  • Circular Cylinder
  • Data Assimilation
  • Proper Orthogonal Decomposition
  • Reduced-Order Model

ASJC Scopus subject areas

  • Information Systems

Fingerprint Dive into the research topics of 'Data assimilation for POD reduced-order model - Comparison of PF and EnKF'. Together they form a unique fingerprint.

  • Cite this

    Kikuchi, R., Misaka, T., & Obayashi, S. (2014). Data assimilation for POD reduced-order model - Comparison of PF and EnKF. In FUSION 2014 - 17th International Conference on Information Fusion [6916175] (FUSION 2014 - 17th International Conference on Information Fusion). Institute of Electrical and Electronics Engineers Inc..