一项关于快速模拟矿山道路火灾的研究,用于紧急决策
Yangqin Chen1,2, Jian Liu3,4, Qichao Zhou5
1College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China.
Scientific reports
|January 19, 2024
概括
一个新的神经网络模型快速预测矿山火灾条件,克服了缓慢的FDS模拟. 这可以通过估计火灾后的温度,二氧化碳度和可见度来更快地做出紧急决策.
科学领域:
- 矿山安全工程 矿山安全工程
- 计算流体动力学 计算流体动力学
- 人工智能的人工智能是人工智能.
背景情况:
- 传统的采矿火灾模拟使用FDS软件是准确的,但耗时.
- 现有的方法阻碍了矿山火灾紧急情况期间的快速决策.
- 迫切需要快速预测火灾发展和环境参数.
研究的目的:
- 开发一个用于采矿道火灾情景的快速预测模型.
- 为了克服紧急响应传统数值模拟的时间限制.
- 提供实时环境参数和因果因素度估计.
主要方法:
- 利用FDS软件在各种条件下模拟了众多的矿山道路火灾.
- 采用神经网络模型,以火源强度,道路几何形状和风速作为输入.
- 训练神经网络预测输出,包括温度,二氧化碳度,可见度和火风压力.
主要成果:
- 神经网络模型提供环境参数和致病因子度的即时预测.
- 温度的平均相对误差为12.12%,密度的平均误差为2.9%.
- 已证明的平均绝对误差为0.87m的可见度,3.49ppm的CO度和16.78Pa的火风压力.
结论:
- 与传统的FDS模拟相比,开发的神经网络模型显著减少了预测时间.
- 这种快速预测能力对于有效的矿山火灾紧急决策至关重要.
- 该模型为提高矿山安全提供了关键环境因素的可靠估计.
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