应用深度学习模型预测复杂平面房间内的火灾温度场

Deep-learning prediction on fire-induced temperature field in complex room layouts

目前火灾探测系统的设计和评估主要依赖于经验模型,虽然这些模型简化了顶棚射流的特性,却未考虑建筑结构对火灾烟气行为的影响。因此,本研究采用了一种基于UNet架构的深度学习模型,以实现对复杂平面房间内顶棚下火灾温度场的快速而准确的预测。模型的训练数据包括136种不同火灾工况的数值模拟结果,其中包含各种房间平面布局、火源位置和房间高度的变化。研究结果表明,该模型能够在数秒内准确预测任何给定房间平面设计中的火灾温度场,准确率高达88%。该研究可为复杂建筑的消防系统设计和优化提供人工智能视角的参考。

The current design and analysis of fire detection system are mainly based on the simplified unconfined ceiling jet model,which does not consider the effect of building structures on the smoke flow behavior.This work proposed a deep learning model based on conventional neural network(CNN)with UNet architec⁃ture which aims to provide quick and accurate prediction of the fire-induced temperature field in rooms with complex layouts.A numerical database with 136 fire scenarios was first established by considering different room layouts,fire locations and room heights.The result shows that the model can provide temperature field for a given building in seconds with an accuracy of up to 88%.This work can contribute to the safety design for buildings with complex architectural plans.