Abstract:

The research on 2-Dimensional (2D) obstacle flow fields primarily employs numerical computations to simulate fluid motion. Factors such as mesh granularity in the model, solver algorithms, and boundary conditions significantly affect the accuracy of the computational results, as well as the computational resources and time required. Deep learning models trained on flow field data can be utilized for rapid prediction and inference tasks in real-time or large-scale flow fields, thereby substantially reducing computational costs. However, when predicting 2D flow fields, existing deep learning models require the resampling of flow field data to satisfy convolutional operation requirements, transforming originally unevenly distributed meshes into uniformly distributed meshes. This process results in feature loss, which significantly affects prediction accuracy. To address this issue, a convolutional neural network model called CAM-UNet with dynamic geometric adaptability is proposed. A Content-Aware Mixer (CAMixer) module is employed to enhance the dynamic adaptability of the U-Net model to geometric shapes and improve the flow field prediction accuracy in mesh point regions affected by resampling. Additionally, a module with gating units capable of effectively integrating the spatial and channel features of the data is designed. Based on this module, the skip connection structure of the U-Net model is reconstructed to further enhance its feature representation capability. Experiments reveal that this model shows improvement in terms of learning the nonlinear representations between the input data of 2D obstacle flow fields and the fluid velocities in the x and y directions, significantly reducing prediction errors in the horizontal velocity for regions with complex flow phenomena.

Key words: Computational Fluid Dynamics (CFD), deep learning, U-Net, channel attention, spatial attention

摘要：

二维(2D)障碍物流场研究主要利用数值计算模拟流体运动, 模型的网格划分粒度、求解器算法、边界条件等因素对计算结果精度、计算资源和时间有着重要影响。由流场数据训练得到的深度学习模型, 能够用于实时或大规模流场的快速预测和推断任务, 并明显降低计算成本。但现有的深度学习模型在预测2D流场时, 为满足卷积操作, 需要对流场数据进行重采样, 将原本分布不均匀的网格变成均匀分布的网格, 造成特征丢失, 从而显著影响预测精度。针对这一问题, 提出一种对几何形状具有动态自适应能力的卷积神经网络模型CAM-UNet。采用内容感知混合器(CAMixer)模块增强U-Net模型对几何形状的动态适应能力, 提高受重采样影响的网格点区域的流场预测精度。设计一种带门控单元的、能够有效融合数据空间特征和通道特征的模块, 基于此模块重构U-Net模型的跳跃连接结构, 进一步增强模型的特征表达能力。实验结果表明, 该模型能够更好地学习2D障碍物流场输入数据与流体x、y方向速度的非线性表示, 能够明显降低具有复杂流动现象区域的水平方向速度预测误差。

关键词: 计算流体动力学, 深度学习, U-Net, 通道注意力, 空间注意力