A Pilot Study on a GPU-Accelerated Voxel Simulation Framework for 3D Indoor and Urban-Scale Gas Dispersion and Aerosol Transport

The increasing complexity of environmental analysis requires new approaches for real-time simulation across in-door and urban spaces. While computational fluid dynamics (CFD) models provide detailed representations of gas dispersion and aerosol transport, they are often computationally intensive and difficult to integrate into emerging AI-native digital twins and agentic AI systems. This pilot study presents a GPU-accelerated voxel simulation framework for modeling three-dimensional gas dispersion and aerosol transport using structured voxel representations derived from BIM, LiDAR, GIS, and digital twin environments. The framework provides physically informed, CFD-inspired simulation at sub-meter to meter-scale spatial resolutions while maintaining interactive runtime performance suitable for building management, ventilation analysis, environmental monitoring, hazard assessment, and emergency response applications. Transport dynamics are modeled using a discretized advection–diffusion formulation incorporating airflow-driven advection, diffusion, source emissions, and voxel-level sink mechanisms. A key contribution is the development of a voxel-native GPU-parallel computational architecture implemented in Python using Taichi kernels. Prototype simulations demonstrate stable transport behavior, browser-based three-dimensional visualization, and efficient execution on commodity GPU hardware. Experimental scenarios include a voxelized three-story Industry Foundation Classes (IFC) building model comprising approximately 34.5 million active voxels (582 × 382 × 155 voxels) and an urban-scale 3D city model spanning approximately 300 × 300 × 150 m and containing up to 13.9 million active voxels. Simulations containing tens of millions of voxels were completed within minutes on a single consumer-grade GPU, demonstrating the scalability of the framework. These results establish a practical foundation for AI-native digital twins and agentic AI-assisted environmental simulation applications.