Data-driven learning algorithm to predict full-field aerodynamics of large structures subject to crosswinds | |
Chen XJ(陈贤佳); Yin B(银波); Yuan Z(袁征); Yang GW(杨国伟); Li, Qiang; Sun, Shouguang; Wei YJ(魏宇杰) | |
刊名 | PHYSICS OF FLUIDS |
2024-05-01 | |
卷号 | 36期号:5页码:19 |
ISSN号 | 1070-6631 |
DOI | 10.1063/5.0197178 |
英文摘要 | Quick and high-fidelity updates about aerodynamic loads of large-scale structures, from trains, planes, and automobiles to many civil infrastructures, serving under the influence of a broad range of crosswinds are of practical significance for their design and in-use safety assessment. Herein, we demonstrate that data-driven machine learning (ML) modeling, in combination with conventional computational methods, can fulfill the goal of fast yet faithful aerodynamic prediction for moving objects subject to crosswinds. Taking a full-scale high-speed train, we illustrate that our data-driven model, trained with a small amount of data from simulations, can readily predict with high fidelity pressure and viscous stress distributions on the train surface in a wide span of operating speed and crosswind velocity. By exploring the dependence of aerodynamic coefficients on yaw angles from ML-based predictions, a rapid update of aerodynamic forces is realized, which can be effectively generalized to trains operating at higher speed levels and subject to harsher crosswinds. The method introduced here paves the way for high-fidelity yet efficient predictions to capture the aerodynamics of engineering structures and facilitates their safety assessment with enormous economic and social significance. |
分类号 | 一类/力学重要期刊 |
WOS关键词 | HIGH-SPEED TRAIN ; DYNAMIC-RESPONSE ; PERFORMANCE ; WIND ; TOWER ; LOADS ; CFD |
WOS研究方向 | Mechanics ; Physics |
语种 | 英语 |
WOS记录号 | WOS:001225917600021 |
其他责任者 | Wei, Yujie |
内容类型 | 期刊论文 |
源URL | [http://dspace.imech.ac.cn/handle/311007/95611] |
专题 | 力学研究所_非线性力学国家重点实验室 |
通讯作者 | Wei YJ(魏宇杰) |
推荐引用方式 GB/T 7714 | Chen XJ,Yin B,Yuan Z,et al. Data-driven learning algorithm to predict full-field aerodynamics of large structures subject to crosswinds[J]. PHYSICS OF FLUIDS,2024,36(5):19. |
APA | Chen XJ.,Yin B.,Yuan Z.,Yang GW.,Li, Qiang.,...&Wei YJ.(2024).Data-driven learning algorithm to predict full-field aerodynamics of large structures subject to crosswinds.PHYSICS OF FLUIDS,36(5),19. |
MLA | Chen XJ,et al."Data-driven learning algorithm to predict full-field aerodynamics of large structures subject to crosswinds".PHYSICS OF FLUIDS 36.5(2024):19. |
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