  Wildland fires are complex multi-physics problems that span wide spatial
scale ranges. Capturing this complexity in computationally affordable numerical
simulations for process studies and "outer-loop" techniques (e.g., optimization
and uncertainty quantification) is a fundamental challenge in reacting flow
research. Further complications arise for propagating fires where a priori
knowledge of the fire spread rate and direction is typically not available. In
such cases, static mesh refinement at all possible fire locations is a
computationally inefficient approach to bridging the wide range of spatial
scales relevant to wildland fire behavior. In the present study, we address
this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an
OpenFOAM solver for simulations of complex fire phenomena. The AMR
functionality in the extended solver, called wildFireFoam, allows us to
dynamically track regions of interest and to avoid inefficient over-resolution
of areas far from a propagating flame. We demonstrate the AMR capability for
fire spread on vertical panels and for large-scale fire propagation on a
variable-slope surface that is representative of real topography. We show that
the AMR solver reproduces results obtained using much larger statically refined
meshes, at a substantially reduced computational cost.
