基于人工智能的外立面开口火溢流温度场预测

Artificial intelligence based facade spilled flame temperature field prediction

为了明确外立面构造形式对开口火溢流的影响,对不同外立面构造条件下的开口火溢流进行实验研究,并对外立面火灾温度场的实时预测进行探索。通过不同翼墙间距和进深条件下的外立面火灾实验构建外立面火灾预测数据库,并将其应用于人工智能方法—卷积神经网络的训练和验证,建立不同外立面构造形式下开口溢流火焰温度的实时预测模型,为外立面构造形式的设计和外立面火灾防控提供支撑。

In order to clarify the influence of facade wing walls on spilled flame,an experimental study on the spilled flame under different facade wing wall conditions was conducted and the real-time prediction of the fire temperature field of the facade was predicted.The facade fire prediction database was constructed through facade fire experiments under different wingwall spacing and depth conditions,and applied to the training and validation of convolutional neural network(CNN),so as to establish a real-time prediction model for the flame temperature of spilled flame under different facade wing-wall conditions,and provide support for the design of facade construction forms and facade fire protection.