Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity overcome these limitations. Therefore, this paper explores applicability of approaches wildland fire spread prediction problems. Using canonical simulation scenario, we assessed performance control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups up 100x comparison standard method. This improvement was leveraged quantify aleatoric analyze sensitivity rate (RoS) weather fuel parameters using full-physics model, namely Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used uncertainty other relevant metrics such as heat transfer, consumption smoke production indicators.