Gas leak accident has been troublesome issues in the chemical industries. Predicting dispersion boundaries are important to make rapid and proper actions. Currently, computational fluid dynamics (CFD) are used to predict the dispersion boundaries. However, when the facility-layout of a workplace is often modified, using CFD is not desirable since it requires large computational expenses. This study proposes an encoding-prediction neural network to learn representations between dispersion of leak gas, velocity field, and facility-layouts. This network predict volume fraction field of leak gas in t + kΔt timestep by observing that data in t ∼ t + (k-1)Δt timestep. Training and test losses are decreased to 1.04 × 10^-5 and 1.46 × 10^-5, respectively. The network predicts dispersion of leak gas through recursive prediction scheme, the predicted results shows good agreement with ground truth. Methodology to generated various facility-layouts, and preprocessing methods to deal with skewed data are suggested. The methodology and results proposed in this study would be useful for developing the CFD surrogate model.

煤气泄漏事故一直是化学工业中的麻烦问题。预测色散边界对于快速采取正确的措施很重要。当前，计算流体动力学（CFD）用于预测弥散边界。但是，当经常修改工作场所的设施布局时，使用CFD是不理想的，因为它需要大量的计算费用。这项研究提出了一种编码预测神经网络，以学习泄漏气体的扩散，速度场和设施布局之间的表示形式。该网络通过观察t〜t +（k-1）Δt时间步长中的数据来预测t +kΔt时间步长中泄漏气体的体积分数场。训练和测试损失减少到1.04×10 ^-5和1.46×10 ^-5， 分别。该网络通过递归预测方案预测泄漏气体的扩散，预测结果与地面真实情况吻合良好。提出了用于生成各种设施布局的方法，以及用于处理偏斜数据的预处理方法。这项研究中提出的方法和结果将有助于开发CFD替代模型。