Rapidly predicting airborne pollutant dispersion in urban is vital for ventilation design and evacuation planning. Computational fluid dynamics (CFD) simulations are commonly used to provide accurate predictions, but the computational cost is too high. Although graph neural networks (GNNs) provide fast predictions of flow fields by manipulating unstructured mesh on GPU, they suffer from high memory usage and accuracy decreases when applied to large-scale urban scenes. Moreover, it is difficult for GNNs to learn the coupled relationship between wind field and pollutant concentration field. We propose a multi-objective GNN model as CFD surrogate to rapidly predict the transient dispersion of airborne pollutant under the influence of complex wind field patterns in urban environment. Based on random urban layouts generated by a 2D bin packing algorithm, we employ a validated CFD model to construct a sample dataset of wind fields and concentration fields. We leverage graph pooling and multi-scale feature fusion to improve prediction accuracy, and subgraph partitioning of both wind field and concentration field to reduce GPU memory usage. The results show that our GNN model at its best runs 1–2 orders of magnitude faster than CFD simulation with accuracy evaluation metrics , and achieves 70 % GPU memory reduction.

中文翻译：

---

基于图神经网络的城市环境空气污染物泄漏瞬态扩散快速预测


快速预测城市空气污染物的扩散对于通风设计和疏散规划至关重要。计算流体动力学（CFD）模拟通常用于提供准确的预测，但计算成本太高。尽管图神经网络 (GNN) 通过在 GPU 上操作非结构化网格来提供流场的快速预测，但在应用于大规模城市场景时，它们会遇到内存使用率高且精度下降的问题。此外，GNN 很难学习风场和污染物浓度场之间的耦合关系。我们提出了一种多目标 GNN 模型作为 CFD 替代品，用于快速预测城市环境中复杂风场模式影响下空气污染物的瞬态扩散。基于二维装箱算法生成的随机城市布局，我们采用经过验证的 CFD 模型来构建风场和浓度场的样本数据集。我们利用图池化和多尺度特征融合来提高预测精度，并利用风场和浓度场的子图分区来减少 GPU 内存使用。结果表明，我们的 GNN 模型在最佳状态下的运行速度比具有精度评估指标的 CFD 模拟快 1-2 个数量级，并且 GPU 内存减少了 70%。