Heat-Transfer-Driven Voxel-Based Simulation: An Exploratory GPU-Accelerated Framework for Urban-Scale 3D Fire Spread

Abstract

The rapid growth of high-resolution 3D voxel datasets derived from LiDAR, BIM, and urban digital twin platforms has created new opportunities for volumetric environmental simulation. However, existing fire-spread models are often surface-based or computationally intensive for large-scale 3D applications, motivating the investigation of efficient voxel-native alternatives. This study presents a pilot investigation of a physics-based, GPU-accelerated framework for rapid 3D fire-spread simulation in wildland–urban interface (WUI) environments. Fire propagation is represented through simplified formulations of conduction, radiation, and wind-driven convection on a structured voxel grid, with combustion behavior parameterized using fuel and material properties. The framework is not intended to replace high-fidelity computational fluid dynamics (CFD) models, but rather to provide a computationally efficient approach for rapid evaluation of fire-spread scenarios in large 3D urban environments. A voxel-native parallel memory layout and stencil-based computational scheme enable efficient neighbor access and GPU-parallel updates. The framework is demonstrated using a voxelized model of Liverpool, NSW, Australia, and its computational performance is evaluated on both local GPU and high-performance computing (HPC) platforms. The results demonstrate predictable runtime scaling and practical performance for domains exceeding one million active burnable voxels. An initial cross-model comparison with the FDS CSIRO scenario further demonstrates substantial spatial agreement while identifying remaining differences in burned area. The results demonstrate the feasibility of the framework for rapid urban-scale 3D fire-spread evaluation, with potential future applications in emergency response and time-critical decision support following further calibration and validation.