Modeling filtration performance of pleated fibrous filters by Eulerian-Markov method

Abstract Computational fluid dynamics (CFD) is a powerful tool for the design of pleated fibrous filters. The Lagrangian method is widely used to simulate particle motion, filtration and clogging in pleated filters. However, the Lagrangian method is very time-consuming because it tracks large number of particle trajectories. In order to overcome the disadvantages, the current study proposed a new Eulerian-Markov method. The Eulerian method and Markov chain model were utilized to simulate the particle concentration field in the air zone and filter medium of a pleated filter respectively. The hybrid method was validated by experimental results from previously published literature. It was found that the Eulerian-Markov method is accurate, time-saving and widely applicable. Compared to the Lagrangian method, the newly developed Eulerian-Markov method could reduce computing time by 80%–90%.