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

Ryota Kikuchi, Takashi Misaka, Shigeru Obayashi

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルFUSION 2014 - 17th International Conference on Information Fusion
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9788490123553
出版ステータスPublished - 2014 10 3
イベント17th International Conference on Information Fusion, FUSION 2014 - Salamanca, Spain
継続期間: 2014 7 72014 7 10

出版物シリーズ

名前FUSION 2014 - 17th International Conference on Information Fusion

Other

Other17th International Conference on Information Fusion, FUSION 2014
国/地域Spain
CitySalamanca
Period14/7/714/7/10

ASJC Scopus subject areas

  • 情報システム

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