High-Fidelity Numerical Simulations of Fire Using Adaptive Mesh Refinement

High-fidelity computational predictions of fire in natural and built environments are constrained by the difficulty of modeling complex physics across wide temporal and spatial scale ranges. Fire structure and dynamics are governed by coupled nonlinear interactions between turbulence, buoyancy-driven flow, and flame chemistry, and these multi-physics phenomena typically span an enormous range of spatial and temporal scales. In this talk, we outline recent efforts to use adaptive mesh refinement (AMR), where the grid is resolved at small scales only in regions of high dynamical and physical significance, for the study of fire structure, dynamics, and evolution in a range of contexts. We will focus on two areas in particular: (i) the structure and dynamics of non-reacting buoyancy driven flows relevant to pool fires and (ii) fire spread in natural and built environments. Physical understanding resulting from these studies will allow refinement of prevailing theories as well as provide guidance on improvements to subgrid-scale models for large eddy simulations of practical fire problems. Challenges faced in the application of AMR to fire simulations are outlined, and future research directions, with a focus on outer-loop processes (e.g., optimization), are also highlighted.