CFDNet：基于深度学习的流体模拟加速器
CFDNet: a deep learning-based accelerator for fluid simulations
摘要：
计算流体动力学被广泛用于物理系统设计和优化，它被用来预测感兴趣的工程量，如飞机机翼上的升力或汽车上的阻力。然而，由于计算流体动力学模拟评估的费用，许多感兴趣的系统对于设计优化来说过于昂贵。为了使计算易于处理，降阶或代理模型用于加速模拟，同时尊重由高保真度解决方案提供的收敛约束。本文介绍了CFDNet --一个物理模拟和深度学习耦合的框架，用于加速雷诺平均Navier-Stokes模拟的收敛。CFDNet旨在预测流体的主要物理特性，包括速度，压力和涡流粘度，其核心是单个卷积神经网络。我们在各种用例上评估CFDNet，包括外推和插值，其中在训练期间观察/不观察测试几何。我们的研究结果表明，CFDNet满足特定领域的物理求解器的收敛约束，同时在稳定层流和湍流上的性能优于它1.9 - 7.4倍。此外，我们通过测试CFDNet对训练过程中看不到的新几何形状的预测来证明CFDNet的泛化能力。在这种情况下，该方法满足计算流体动力学收敛标准，同时仍然提供了显着的加速比传统的域只模型。
英文摘要：
CFD is widely used in physical system design and optimization, where it is used to predict engineering quantities of interest, such as the lift on a plane wing or the drag on a motor vehicle. However, many systems of interest are prohibitively expensive for design optimization, due to the expense of evaluating CFD simulations. To render the computation tractable, reduced-order or surrogate models are used to accelerate simulations while respecting the convergence constraints provided by the higher-fidelity solution. This paper introduces CFDNet -- a physical simulation and deep learning coupled framework, for accelerating the convergence of Reynolds Averaged Navier-Stokes simulations. CFDNet is designed to predict the primary physical properties of the fluid including velocity, pressure, and eddy viscosity using a single convolutional neural network at its core. We evaluate CFDNet on a variety of use-cases, both extrapolative and interpolative, where test geometries are observed/not-observed during training. Our results show that CFDNet meets the convergence constraints of the domain-specific physics solver while outperforming it by 1.9 - 7.4x on both steady laminar and turbulent flows. Moreover, we demonstrate the generalization capacity of CFDNet by testing its prediction on new geometries unseen during training. In this case, the approach meets the CFD convergence criterion while still providing significant speedups over traditional domain-only models.
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