论文标题
基于图神经网络的流体模拟系统
Fluid Simulation System Based on Graph Neural Network
论文作者
论文摘要
传统的计算流体动力学通过求解部分微分方程来计算流场的物理信息，该方程式需要很长时间才能计算和消耗大量计算资源。我们基于图神经网络体系结构构建流体模拟模拟器。模拟器具有快速的计算速度和计算资源的低消耗。我们将计算域视为结构图，而结构图中的计算节点通过自适应采样确定邻居节点。通过注意力图神经网络构建深度学习体系结构。根据不同雷诺数的圆柱周围流场的模拟结果，对流体模拟模拟器进行了训练。训练有素的流体模拟模拟器不仅可以在训练集中预测流场的准确性很高，而且还可以推断训练集之外的流场。与传统的CFD求解器相比，流体模拟模拟器达到了2-3个数量级的加速。流体模拟模拟器为流体力学模型的快速优化和设计提供了新的想法，以及对智能流体机制的实时控制。
Traditional computational fluid dynamics calculates the physical information of the flow field by solving partial differential equations, which takes a long time to calculate and consumes a lot of computational resources. We build a fluid simulation simulator based on the graph neural network architecture. The simulator has fast computing speed and low consumption of computing resources. We regard the computational domain as a structural graph, and the computational nodes in the structural graph determine neighbor nodes through adaptive sampling. Building deep learning architectures with attention graph neural networks. The fluid simulation simulator is trained according to the simulation results of the flow field around the cylinder with different Reynolds numbers. The trained fluid simulation simulator not only has a very high accuracy for the prediction of the flow field in the training set, but also can extrapolate the flow field outside the training set. Compared to traditional CFD solvers, the fluid simulation simulator achieves a speedup of 2-3 orders of magnitude. The fluid simulation simulator provides new ideas for the rapid optimization and design of fluid mechanics models and the real-time control of intelligent fluid mechanisms.
