与深度卷积神经网络耦合的不可压缩流体求解器的性能和精度评估

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-09-20 DOI:10.1017/dce.2022.2
Ekhi Ajuria Illarramendi, M. Bauerheim, B. Cuenot
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引用次数: 9

摘要

泊松方程的解算通常是不可压缩流体解算中计算量最大的步骤之一。最近,深度学习,特别是卷积神经网络(cnn)被引入来解决这个方程,导致推理时间显著减少,但代价是缺乏对解的准确性的保证。这个缺点可能会导致不准确,潜在的不稳定模拟,并阻止对不同网络架构的CNN加速进行公平评估。为了规避这一问题,开发了一种混合策略,将CNN与传统的迭代求解器耦合在一起,以确保用户自定义的精度水平。CNN混合方法在两种流动情况下进行了测试:(a)二维圆柱体周围的流动和(b)有障碍物和无障碍物的变密度羽流(2D和3D),证明了显著的泛化能力,保证了模拟的准确性和稳定性。在羽流测试用例中,进一步研究了使用几种网络架构的预测的误差分布。引入的混合策略允许对不同网络架构在相同精度水平下的CNN性能进行系统评估。特别是,在网络架构中加入多个尺度的重要性得到了证明,因为与前馈CNN架构相比,它提高了准确性和推理性能。因此,除了纯网络的性能评估之外,本研究还为如何构建神经网络和计算策略来预测非定常流动提供了许多指南和结果,同时满足精度和稳定性要求。
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Performance and accuracy assessments of an incompressible fluid solver coupled with a deep convolutional neural network
Abstract The resolution of the Poisson equation is usually one of the most computationally intensive steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional neural networks (CNNs), has been introduced to solve this equation, leading to significant inference time reduction at the cost of a lack of guarantee on the accuracy of the solution.This drawback might lead to inaccuracies, potentially unstable simulations and prevent performing fair assessments of the CNN speedup for different network architectures. To circumvent this issue, a hybrid strategy is developed, which couples a CNN with a traditional iterative solver to ensure a user-defined accuracy level. The CNN hybrid method is tested on two flow cases: (a) the flow around a 2D cylinder and (b) the variable-density plumes with and without obstacles (both 2D and 3D), demonstrating remarkable generalization capabilities, ensuring both the accuracy and stability of the simulations. The error distribution of the predictions using several network architectures is further investigated in the plume test case. The introduced hybrid strategy allows a systematic evaluation of the CNN performance at the same accuracy level for various network architectures. In particular, the importance of incorporating multiple scales in the network architecture is demonstrated, since improving both the accuracy and the inference performance compared with feedforward CNN architectures. Thus, in addition to the pure networks’ performance evaluation, this study has also led to numerous guidelines and results on how to build neural networks and computational strategies to predict unsteady flows with both accuracy and stability requirements.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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