Predicting thermophoresis induced particle deposition by using a modified Markov chain model

Thermophoresis is considered as one of the major causes leading to deposition of suspended submicron aerosols with the presence of temperature gradient. Prompt obtaining of information about particle deposition and spatial distribution is crucial to environment control. This study proposed a modified Markov chain model to include the effect of thermophoresis. A steady-state flow field and a constant temperature field are firstly established by using Computational Fluid Dynamics (CFD) tools. Then the data of flow and temperature fields are exported out as data files, which will be used by the proposed model to realize the particle phase simulation. A horizontal steady-state laminar duct flow case is used to validate the model. Results show that the proposed model is able to efficiently predict thermophoresis induced particle deposition and dispersion with reasonable accuracy. Detailed information also indicate that the particle deposition efficiency is dependent on both temperature gradients and particle diameters. However, the beginning of the particle escape (if any) is irrelevant to those two parameters. The highest deposition efficiency on the deposition surface is found to gradually increase over time and the location of highest deposition efficiency (peak) moves along with the main flow. The average speed of the moving peak is inversely proportional to temperature gradient, but the ultimate deposition efficiency is still proportional to temperature gradient. The proposed model is deemed a practical method in predicting aerosol particle deposition/transport as well as contaminant control in non-isothermal environment.