大規模非定常データに対する特徴構造抽出法の開発と大気突入カプセル後流解析への適用

書誌事項

タイトル別名
  • Feature extraction technique for large time-series data and its application to wake flow analysis of a re-entry capsule

抄録

第50回流体力学講演会/第36回航空宇宙数値シミュレーション技術シンポジウム (2018年7月4日-6日. 宮崎市民プラザ), 宮崎市, 宮崎

50th Fluid Dynamics Conference /the 36th Aerospace Numerical Simulation Symposium (July 4-6, 2018. Miyazaki Citizen's Plaza), Miyazaki, Japan

The feature extraction technique based on dynamic mode decomposition (DMD) and mode selection methods for large CFD datasets was proposed. The incremental proper orthogonal decomposition (POD) was introduced as a preconditioning step. By performing the preconditioning step, the DMD and mode selection can be applied to large datasets with low memory consumption. The proposed algorithms were applied to a subsonic flowfield around a re-entry capsule. We found that the flowfield has four dominant fluid phenomena and they have the frequency of St approx. 0.2 and St = 0.0159. Furthermore, the contribution of these fluid phenomena on the aerodynamic coefficient fluctuations of the capsule was clarified. The result showed that the lift and drag coefficient fluctuations are dominated by vortex shedding phenomena (of St approx. 0.2) and pressure oscillation phenomena in the recirculation region (of St = 0.0159), respectively. This pressure oscillation phenomenon of St = 0.0159 has not been reported so far and seems to be related to dynamic instability phenomena of the capsule because its time scale is close to that of the capsule's motion reported previously.

形態: カラー図版あり

Physical characteristics: Original contains color illustrations

資料番号: AA1830029004

レポート番号: JAXA-SP-18-005

収録刊行物

詳細情報 詳細情報について

  • CRID
    1050574036210684672
  • NII論文ID
    120006827184
  • ISSN
    24332232
  • Web Site
    http://id.nii.ac.jp/1696/00002927/
  • 本文言語コード
    ja
  • 資料種別
    conference paper
  • データソース種別
    • IRDB
    • CiNii Articles

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