用AI加速火灾模拟，省时省电还更准。
Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations
- 用定制机器学习模型预测热传播，速度比传统软件快数个量级。
- 通过智能优化减少90%模拟次数，定位危险火点效率提升十倍。
- 推出Simvue平台，自动管理数据与流程，避免重复工作。
火灾模拟在规模和数量上需求巨大。本文提出多路径方法以降低其耗时与能耗。我们展示了一种定制的机器学习代理模型，可比当前最先进的CFD软件快数个量级地预测热传播动态。同时，通过引导式优化程序，利用轻量级模型决策哪些模拟需执行，使在建筑内定位最危险火灾位置（基于烟雾对能见度的影响）所需的模拟次数减少十倍。最后，我们推出了Simvue框架与产品，集成上述工具及一系列自动化组织与追踪功能，支持数据复用，通过更好管理模拟流程和消除冗余实现进一步节省。
原文摘要 · Abstract (English)
There is high demand on fire simulations, in both scale and quantity. We present a multi-pronged approach to improving the time and energy required to meet these demands. We show the ability of a custom machine learning surrogate model to predict the dynamics of heat propagation orders of magnitude faster than state-of-the-art CFD software for this application. We also demonstrate how a guided optimisation procedure can decrease the number of simulations required to meet an objective; using lightweight models to decide which simulations to run, we see a tenfold reduction when locating the most dangerous location for a fire to occur within a building based on the impact of smoke on visibility. Finally we present a framework and product, Simvue, through which we access these tools along with a host of automatic organisational and tracking features which enables future reuse of data and more savings through better management of simulations and combating redundancy.
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