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On-line Access: 2022-02-28

Received: 2020-08-28

Revision Accepted: 2022-04-22

Crosschecked: 2021-05-04

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Xiang GAO


Chuanfu XU


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.2 P.207-219


FlowDNN: a physics-informed deep neural network for fast and accurate flow prediction

Author(s):  Donglin CHEN, Xiang GAO, Chuanfu XU, Siqi WANG, Shizhao CHEN, Jianbin FANG, Zheng WANG

Affiliation(s):  College of Computer, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   chendonglin14@nudt.edu.cn, gaoxiang12@nudt.edu.cn, xuchuanfu@nudt.edu.cn

Key Words:  Deep neural network, Flow prediction, Attention mechanism, Physics-informed loss

Donglin CHEN, Xiang GAO, Chuanfu XU, Siqi WANG, Shizhao CHEN, Jianbin FANG, Zheng WANG. FlowDNN: a physics-informed deep neural network for fast and accurate flow prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 207-219.

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journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T FlowDNN: a physics-informed deep neural network for fast and accurate flow prediction
%A Donglin CHEN
%A Xiang GAO
%A Chuanfu XU
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A1 - Donglin CHEN
A1 - Xiang GAO
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A1 - Siqi WANG
A1 - Shizhao CHEN
A1 - Jianbin FANG
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For flow-related design optimization problems, e.g., aircraft and automobile aerodynamic design, computational fluid dynamics (CFD) simulations are commonly used to predict flow fields and analyze performance. While important, CFD simulations are a resource-demanding and time-consuming iterative process. The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design. In this paper, we propose FlowDNN, a novel deep neural network (DNN) to efficiently learn flow representations from CFD results. FlowDNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes. FlowDNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction. This approach not only improves the prediction accuracy, but also preserves the physical consistency of the predicted flow fields, which is essential for CFD. Various metrics are derived to evaluate FlowDNN with respect to the whole flow fields or regions of interest (RoIs) (e.g., boundary layers where flow quantities change rapidly). Experiments show that FlowDNN significantly outperforms alternative methods with faster inference and more accurate results. It speeds up a graphics processing unit (GPU) accelerated CFD solver by more than 14 000×, while keeping the prediction error under 5%.


摘要:对于与流场相关的设计优化问题,例如飞机和汽车空气动力学设计,计算流体力学(CFD)模拟通常用于预测流场并分析性能。虽然CFD模拟十分重要,但它的迭代计算非常需要计算资源且极其耗时。昂贵的模拟开销限制了大范围设计空间的探索,并阻碍了实时的交互式设计。在本文中,我们提出FlowDNN模型,它是一种新颖的深度神经网络,可从CFD结果中高效地学习流场表示。FlowDNN根据给定的流动条件和几何形状可以直接预测预期的流场结果,从而极大地节省计算时间。FlowDNN首次结合了流体力学的基本守恒定律和注意力机制进行定常流场预测。这样做不仅可以提高预测准确性,而且可以维持预测流场的物理一致性,这对于CFD模拟至关重要。本文设计了多种指标以评估FlowDNN预测的整体流场和关键区域的结果(如流场快速变化的边界层)。实验结果表明,FlowDNN明显优于其他方法且具有更短的推理时间和更准确的结果。它与最新的GPU并行求解器相比,生成流场的速度提升14 000倍以上,同时保持预测误差在5%以内。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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