基于U−Net的格子玻尔兹曼方法

Lattice Boltzmann Method based on U-Net

格子玻尔兹曼方法(LBM)是一类广泛应用的介观尺度下的流体数值模拟方法。其缺陷在于,它对于计算资源的要求较高,一般情况下难以实现即时模拟。文章构造了一种新的基于U−Net的卷积神经网络(CNN,convolutional neural network)以对LBM进行加速,以一次卷积神经网络模型的运算代替原本需要进行多次的时间步迭代。对一系列层流绕柱流动的数值模拟进行试验,发现:该方法能够在保证计算精度较高的同时,相较于串行的LBM程序有约250倍加速,验证了该方法的有效性。

Lattice Boltzmann Method(LBM)is a kind of widely used mesoscopic fluid numerical sim‐ulation method.The drawback of LBM is the high computational cost,which causes difficulties in real time simulation.In this work,we created a convolutional neural network(CNN)based on U-Net to accelerate LBM calculation.The purpose is to replace multiple LBM steps with one single operation of the CNN model.According to the result of our numerical experiment on a laminar flow around three obstacles in different geometries,the generated model can maintain the calculation at a high accuracy and accelerate the LBM calculation by over 250 times.