Modeling particulate matter concentration in indoor environment with cellular automata framework

This study attempts to develop a new 3D approach for modeling concentration of airborne particulate matter (PM) in indoor environment with cellular automata (CA) framework. So far numerical simulations of transient 3D indoor PM transport and distribution are mostly using the Lagrangian and Eulerian approaches. Each approach has its own advantages and drawbacks. For the purpose of increasing numerical efficiency without losing necessary accuracy, a new 3D CA-based approach for transient PM concentration estimation is proposed by considering four major PM transport mechanisms (flow advection, turbulent diffusion, gravitational settling, and boundary deposition). This CA approach enables us to use simple explicit algebraic equations to simulate the above four mechanisms without adopting numerical iteration and/or matrix solving, so that the numerical efficiency can be enhanced. Two test cases having reliable measured PM concentration profiles in indoor chambers are used to verify the accuracy and efficiency of the proposed approach with two Eulerian drift flux models and two Lagrangian particle tracking models for comparison. The simulated results indicate that the proposed approach can simulate as accurate as the two Eulerian drift flux models with significant reduction on its execution time. For the present two test cases, it can be at least 483.4%-564.6% faster than the other Eulerian drift flux models. Thus, it has considerable potentials as a useful tool for 3D numerical simulations of indoor PM transport and distribution.