Fluid mechanics simulation of steady state flow in complex geometries has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geothermal) and beyond. Direct simulation of steady state flow in such porous media requires significant computational resources to solve within reasonable timeframes. This study outlines an integrated method combining predictions of fluid flow (fast, limited accuracy) with direct flow simulation (slow, high accuracy) is outlined that reduces computation time by an order of magnitude without loss of accuracy. A convolutional neural network (CNNs) is trained with various configurations on simulations in 2D and 3D porous media to estimate steady state velocity fields. Permeability estimation (as an average of the field) is accurate, but the velocity fields themselves are error prone, unsuitable for further transport studies. This estimate can either be used as an indicative prediction, or as initial conditions in direct simulation to reach a fully accurate result in a fraction of the compute time. Using Deep Learning predictions (or potentially any other approximation method) to accelerate flow simulation to steady state in complex structures shows promise as a technique to push the boundaries fluid flow modelling.

复杂几何形状中稳态流动的流体力学模拟具有许多应用，从微观尺度（细胞膜，过滤器，岩石）到宏观尺度（地下水，碳氢化合物储层和地热）乃至更多。在这种多孔介质中直接模拟稳态流动需要大量的计算资源才能在合理的时间内解决。这项研究概述了一种将流体流量预测（快速，有限精度）与直接流量模拟（缓慢，高精度）相结合的集成方法，该方法可将计算时间减少一个数量级而不会降低精度。卷积神经网络（CNN）在2D和3D多孔介质的模拟中以各种配置进行训练，以估计稳态速度场。渗透率估算（作为油田的平均值）是准确的，但是速度场本身容易出错，不适合进行进一步的运输研究。该估计值既可以用作指示性预测，也可以用作直接仿真的初始条件，以在不到一分钟的计算时间内达到完全准确的结果。使用深度学习预测（或可能使用任何其他近似方法）将复杂结构中的流动模拟加速至稳态时，显示出有望突破流体流动建模的一种技术。