Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations

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. 2 INTRODUCTION A key objective in fire safety design is to ensure that occupants of a building are able to vacate safely during an emergency. Removing smoke through exhaust systems which activate after detection of a fire is one approach to improving the safety of occupants during egress.