Prediction of Safe-Zone Evolution in Poisonous Environment Based on Deep Learning

Precise online identification of safe-zone evolution has been long desired in the context of indoor chemical attack events. Computational fluid dynamics (CFD) technology can provide great accuracy but is incapable of applying on online predictions of a large-scale fluid system due to the enormous computational costs to date. In this paper, we propose a Multi-Step Spatial-Temporal Situational-Awareness Network (MSSTP-SA Net) based on deep learning algorithms for rapid online estimation of concentration field. The dataset is firstly created by CFD simulation considering different poisonous gas release and decontamination scenarios in pre-specific domain with various combinations of airflow conditions and source parameters. Corresponding experiments are also provided for validations. The test experiments of the trained network suggest that the evolution of concentration field's distribution can be predicted faithfully in millisecond computation time costs. We hope this approach to be highly useful in most chemical attack scenarios to reduce casualties.