Forces prediction of a hydrofoil by combined machine learning and cfd approach

Hongjie Cao, Decheng Wan, Changhong Hu

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Computational fluid dynamics (CFD) methods, as an efficient tool, have simulated and solved many engineering problems. However, the process is time-consuming and generates numerous underutilized data. Machine learning (ML) provides a way to extract more information from the data and then analyze it for further study. To efficiently handle non-linear problems and analyze numerous data, in this paper, a novel method combining of machine learning and CFD to solve the problems in the field of fluid mechanics is proposed. Two typical algorithms, “the back propagation (BP) algorithm and convolutional neural network (CNN) algorithm”, are chosen to predict the lift and drag coefficients on hydrofoil NACA0012, respectively. The angle of attack (AOA) is considered as a variable. Two different forms are adopted to describe the variable. One is the degree itself and the other is a matrix based on the computational domain in the CFD method. Compared with the target values, the predicted results of both algorithms show great agreement, and the CNN algorithm is more applicable to handle massive data. Furthermore, the cost of time per prediction is about 0.3s, demonstrating great superiority in comparison with that of 147s cost by CFD methods.

本文言語英語
ホスト出版物のタイトルProceedings of the 31st International Ocean and Polar Engineering Conference, ISOPE 2021
出版社International Society of Offshore and Polar Engineers
ページ1786-1791
ページ数6
ISBN(印刷版)9781880653821
出版ステータス出版済み - 2021
イベント31st International Ocean and Polar Engineering Conference, ISOPE 2021 - Virtual, Online
継続期間: 6 20 20216 25 2021

出版物シリーズ

名前Proceedings of the International Offshore and Polar Engineering Conference
ISSN(印刷版)1098-6189
ISSN(電子版)1555-1792

会議

会議31st International Ocean and Polar Engineering Conference, ISOPE 2021
CityVirtual, Online
Period6/20/216/25/21

All Science Journal Classification (ASJC) codes

  • エネルギー工学および電力技術
  • 海洋工学
  • 機械工学

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