Understory

The tutorial examples are designed to help you understand basic FuelCalc functionality.

We demonstrate the use of a deep learning (DL) approach for representing the behavior of a high-resolution physics-based wildland fire spread model. The ultimate objective is being able to efficiently use the DL model for intensive simulations of large fires while retaining fidelity to the fine-scale physical processes. We begin with a fire model that reduces the spatial domain of the fire spread problem to one dimension (1D). The 1Dmodel explicitly resolves cm-scale fuel variations, heat transfer and heating/drying dynamics of individual fuel particles and burning behavior of the bed. We then...

We describe the development and performance of a dynamical one-dimensional (1D) model of fire spread (LIHTFire, Linear Ignition and Heat Transfer). The model resolves burning rates, heat transfer, and ignition at cm-scales with explicit accounting of heterogeneous fuels and time-varying weather. An empirical convective heating method speeds computation compared to CFD methods. Heating, drying, pyrolysis, ignition, and solid phase combustion of fuel with varying sizes and shapes is represented by a particle model that captures within-particle gradients. The heat transfer, burning behaviors, and...

Current research projects under FireBGCv2