Modeling transient particle transport by fast fluid dynamics with the Markov chain method

Area of Science:

• Environmental Engineering
• Computational Fluid Dynamics
• Indoor Air Quality

Background:

• Accurate prediction of indoor particle transport is crucial for mitigating health risks.
• Existing simulation tools can be computationally expensive, limiting their application in design.

Purpose of the Study:

• To develop and validate a computationally efficient model for predicting transient particle transport indoors.
• To compare the performance of the new model against established simulation methods.

Main Methods:

• A combined Fast Fluid Dynamics (FFD) and Markov chain model was developed and programmed in OpenFOAM.
• The model's accuracy was validated using two literature cases with experimental data.
• Performance was benchmarked against Computational Fluid Dynamics (CFD)-Eulerian and CFD-Lagrangian models.

Main Results:

• The FFD-Markov-chain model showed good agreement with experimental data.
• Accuracy was comparable to CFD-Eulerian and CFD-Lagrangian models.
• The FFD-Markov-chain model demonstrated significant speed improvements, being 4.7-6.8 times faster than CFD-Eulerian and 53.3-137.4 times faster than CFD-Lagrangian.

Conclusions:

• The FFD-Markov-chain model offers a substantial reduction in computational cost for indoor transient particle transport prediction.
• This efficient model can greatly aid in the design of indoor air distribution systems.
• The findings support the use of this model for improving indoor air quality and reducing associated health risks.