Constructing Markov matrices for real-time transient contaminant transport analysis for indoor environments

Area of Science:

• Environmental Science
• Computational Fluid Dynamics
• Public Health

Background:

• Predicting indoor contaminant movement is crucial for public health applications like disease tracking and ventilation.
• Current methods for contaminant transport analysis using Markov matrices lack standardization.
• Computational Fluid Dynamics (CFD) data offers a basis for detailed airflow analysis.

Purpose of the Study:

• To develop a standardized, real-time methodology for calculating Markov matrices for contaminant transport analysis.
• To enable faster and more efficient prediction of contaminant movement in indoor environments.
• To provide a robust tool for sensing and control in critical indoor spaces.

Main Methods:

• A set theory-based methodology was developed to calculate contaminant transport using Markov matrices.
• The method determines Markov states, time steps, and matrix entries from CFD or discrete flow data.
• The approach was benchmarked against scalar transport simulations in validated airflow fields.

Main Results:

• The developed methodology enables near real-time calculation of contaminant transport.
• Contaminant transport was predicted over 3000 times faster than solving partial differential equations.
• The approach is applicable to general airflow fields and provides a rigorous yet simple strategy.

Conclusions:

• The new methodology offers a significant advancement in real-time indoor contaminant transport prediction.
• This rapid analysis capability supports enhanced sensing and control strategies for diverse indoor environments.
• Applications include critical care facilities, transportation hubs, and enclosed public spaces.