Rana Pratip, Weigand Timothy M, Pilkiewicz Kevin R, Mayo Michael L
Bennett Aerospace, Vicksburg, 39180, USA.
Oak Ridge Institute for Science and Education, Oak Ridge, 37830, USA.
Sci Rep. 2024 Oct 4;14(1):23080. doi: 10.1038/s41598-024-73529-y.
We evaluate the capability of convolutional neural networks (CNNs) to predict a velocity field as it relates to fluid flow around various arrangements of obstacles within a two-dimensional, rectangular channel. We base our network architecture on a gated residual U-Net template and train it on velocity fields generated from computational fluid dynamics (CFD) simulations. We then assess the extent to which our model can accurately and efficiently predict steady flows in terms of velocity fields associated with inlet speeds and obstacle configurations not included in our training set. Real-world applications often require fluid-flow predictions in larger and more complex domains that contain more obstacles than used in model training. To address this problem, we propose a method that decomposes a domain into subdomains for which our model can individually and accurately predict the fluid flow, after which we apply smoothness and continuity constraints to reconstruct velocity fields across the whole of the original domain. This piecewise, semicontinuous approach is computationally more efficient than the alternative, which involves generation of CFD datasets required to retrain the model on larger and more spatially complex domains. We introduce a local orientational vector field entropy (LOVE) metric, which quantifies a decorrelation scale for velocity fields in geometric domains with one or more obstacles, and use it to devise a strategy for decomposing complex domains into weakly interacting subsets suitable for application of our modeling approach. We end with an assessment of error propagation across modeled domains of increasing size.
我们评估卷积神经网络（CNN）预测速度场的能力，该速度场与二维矩形通道内各种障碍物排列周围的流体流动有关。我们的网络架构基于门控残差U-Net模板，并在由计算流体动力学（CFD）模拟生成的速度场上进行训练。然后，我们评估模型在与训练集中未包含的入口速度和障碍物配置相关的速度场方面，能够准确有效地预测稳定流的程度。实际应用通常需要在比模型训练中使用的包含更多障碍物的更大、更复杂的域中进行流体流动预测。为了解决这个问题，我们提出了一种方法，将一个域分解为子域，我们的模型可以对这些子域分别准确地预测流体流动，之后我们应用平滑性和连续性约束来重建整个原始域上的速度场。这种分段、半连续的方法在计算上比另一种方法更有效，后者涉及生成在更大、空间更复杂的域上重新训练模型所需的CFD数据集。我们引入了一种局部方向向量场熵（LOVE）度量，它量化了具有一个或多个障碍物的几何域中速度场的去相关尺度，并使用它来设计一种策略，将复杂域分解为适合应用我们建模方法的弱相互作用子集。最后，我们评估了跨大小不断增加的建模域的误差传播情况。
