A Markov chain model for predicting transient particle transport in enclosed environments

Area of Science:

• Environmental engineering
• Computational fluid dynamics
• Infectious disease control

Background:

• Particle dispersion in enclosed spaces is vital for understanding and mitigating infectious disease transmission.
• Accurate modeling of particle transport is essential for designing effective ventilation strategies.

Purpose of the Study:

• To develop a computationally efficient Markov chain model for predicting particle dispersion in rooms.
• To validate the model's accuracy using experimental data.
• To assess the model's performance in simulating person-to-person particle transport.

Main Methods:

• A Markov chain model was developed based on steady-state flow fields from computational fluid dynamics.
• Particle transport equations were solved without time-step iterations to reduce computational cost.
• The model was validated against two experimental datasets for transient particle transport.

Main Results:

• The Markov chain model demonstrated reasonable agreement with experimental data for particle concentration distributions.
• Simulations of person-to-person particle transport in a ventilated room were performed.
• The model achieved calculation speeds 8.0 and 6.3 times faster than Lagrangian and Eulerian models, respectively.

Conclusions:

• The developed Markov chain model offers a significant computational advantage for particle dispersion analysis.
• This efficient model can aid in assessing and controlling infectious disease transmission risks in indoor environments.
• The model provides a viable alternative to traditional methods for particle transport simulations.