Flow Field Analysis of a Racing Car based on Dimensionality Reduction and Clustering
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20225270
- 文献・情報種別
- 学術講演会予稿集(春)
No.61-22
- 掲載ページ
- 1-8(Total 8 p)
- 発行年月
- 2022年 5月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- 2022年春季大会
書誌事項
著者(英) | 1) Michaela Reck, 2) René Hilhorst, 3) Marc Hilbert, 4) Thomas Indinger |
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勤務先(英) | 1) Technical University of Munich, 2) Toyota GAZOO Racing Europe, 3) Leiden University, 4) Technical University of Munich |
抄録(英) | The aerodynamic development process of a racing car involves the generation of a vast amount of data from numerical investigations. Hence, machine learning techniques are used in order to optimize the aerodynamic analysis workflow. In this study, flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations serve as input for dimensionality reduction and clustering methods. The objective is to relate variations in flow topology to changes of corresponding performance metrics, aiming for an improved understanding of predominant fluidic phenomena. Consequently, inferences of aerodynamically relevant zones around the vehicle provide meaningful insights for future aerodynamic development. 翻訳 |