用图神经网络快速预测复杂鳍片流场与温度分布。
Multiscale Graph Neural Network for Turbulent Flow-Thermal Prediction Around a Complex-Shaped Pin-Fin
- 构建多尺度图网络，融合几何特征与边界信息进行流热预测。
- 预测精度高，捕捉边界层、回流区等关键结构，速度提升100~1000倍。
- 适合需要快速仿真复杂流体场景的研究者或工程应用。
本研究提出一种面向特定领域的边缘感知多尺度图神经网络，用于预测二维通道内任意复杂形状鳍片周围的稳态湍流流动与热行为。训练数据通过集成几何生成、网格划分与ANSYS Fluent求解的自动化框架构建，采用分段三次样条参数化鳍片几何，通过拉丁超立方采样生成1,000种多样化配置。每项模拟结果转换为图结构，节点包含空间坐标、归一化流向位置、独热边界指示符及到最近边界的有符号距离。该图结构作为输入，训练网络以预测各节点处的温度、速度大小和压力。模型在保持高精度的同时，准确捕捉了边界层、回流区及鳍片上游滞止区域，并将计算耗时降低2至3个数量级。结果表明，该新型图神经网络可作为复杂流场仿真的高效可靠替代方案。
原文摘要 · Abstract (English)
This study presents the development of a domain-responsive edge-aware multiscale Graph Neural Network for predicting steady, turbulent flow and thermal behavior in a two-dimensional channel containing arbitrarily shaped complex pin-fin geometries. The training dataset was constructed through an automated framework that integrated geometry generation, meshing, and flow-field solutions in ANSYS Fluent. The pin-fin geometry was parameterized using piecewise cubic splines, producing 1,000 diverse configurations through Latin Hypercube Sampling. Each simulation was converted into a graph structure, where nodes carried a feature vector containing spatial coordinates, a normalized streamwise position, one-hot boundary indicators, and a signed distance to the nearest boundary such as wall. This graph structure served as input to the newly developed Graph Neural Network, which was trained to predict temperature, velocity magnitude, and pressure at each node using data from ANSYS. The network predicted fields with outstanding accuracy, capturing boundary layers, recirculation, and the stagnation region upstream of the pin-fins while reducing wall time by 2-3 orders of magnitude. In conclusion, the novel graph neural network offered a fast and reliable surrogate for simulations in complex flow configurations.
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