Progress Towards Efficient Simulation of Large-Scale Fires

Wildland fires and other fire phenomena (e.g., pool fires, fire whirls) are an increasing concern as extreme weather events become more frequent, and understanding how large-scale fires behave will be key to mitigating human and structural losses, as well as supporting fire suppression efforts. Simulations provide an appealing framework to study fire phenomena, but can quickly become intractable; the spatial and temporal scales of large fires span many orders of magnitude, motivating efforts to reduce computational expense. In this talk, we present fireDyMFoam, a new version of the native OpenFOAM solver fireFoam, that has been extended to incorporate Adaptive Mesh Refinement (AMR). The use of AMR in the solver allows run-time mesh refinement in areas of interest, while large regions of the domain remain coarse, greatly decreasing computational cost. New functionality is demonstrated through a series of test cases with significantly reduced computational costs when compared to uniform mesh simulations with identical finest-scale resolution. Future work will focus on incorporating adjoints for optimization (e.g., where the objective is to minimize fire spread) and coupling extended dynamic meshing functionality with native OpenFOAM pyrolysis modeling capability.