  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.
