横通道排烟作用下隧道火灾深度学习研究

Research on Deep Learning of Tunnel Fire under the Effect of Cross Passage Exhaust

  • 摘要: 针对采用横通道排烟的单洞双向公路隧道,利用FDS数值模拟软件对不同横通道夹角和火源纵向距离下的火灾工况进行分析,确定最佳排烟工况。基于横通道排烟的隧道火灾场景,建立基于Transformer架构的火灾烟气参数快速预测模型。结果表明:横通道夹角为60°、火源纵向距离为0 m时,烟气蔓延的控制效果最佳;基于FDS数值模拟结果构建的Transformer-CNN与Transformer-GRU火灾烟气参数预测模型可以对隧道内纵向温度、能见度及烟气层高度实现快速、准确预测。

     

    Abstract: For a single-hole bidirectional highway tunnel with a cross passage for smoke exhaust, FDS numerical simulation software was utilized to analyze fire scenarios under different angles of cross passage and longitudinal position of the fire source, to determine the optimal smoke extraction conditions. Based on tunnel fire scenarios involving smoke exhaust with cross passage, a rapid prediction model for fire smoke parameters based on the Transformer was established. The results indicate that when the angle of cross passage is 60°, while the longitudinal position of the fire source is 0 m, the effect on controlling smoke spread is optimal. The Transformer-CNN and Transformer-GRU fire smoke parameter prediction models, constructed based on FDS numerical simulation results, can achieve rapid and accurate predictions of longitudinal temperature, visibility, and smoke height within the tunnel.

     

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