Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis. (July 2021) - Valero, Mario Miguel; Jofre, Lluís; Torres, Ricardo
Abstract: 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 to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators. Highlights: Multifidelity strategies enabled uncertainty quantification in wildfire CFD models. Multifidelity methods allowed 100x speedups in aleatoric uncertainty quantification. A multilevel estimator based on 4 fidelity levels provided the best performance. RoS sensitivity to fuel and wind parameters was quantified using WFDS.