Predicting DPF Performance Based on 3D Microscopic Structure from CT-Scan

Yujun  Wang, Rypos Inc.

To enhance the understanding of the particle filtration process in fibrous or granular filter, it is crucial to predict filter performance from the realistic and complicated microscopic structure of the filtration media. In the present study, the 3D structure of sintered metal fiber diesel particulate filter (DPF) is obtained from computational tomography scan (CT-Scan). Five filter samples of size 1mm×1mm×1.8mm (depth) were scanned. Based on the 3D digital structure, computer models are then built to predict DPF pressure drop and filtration efficiency.

An open source CFD code – OpenFOAM was used to solve the micro-fluid motion through the digital samples. Since the Knudsen number in the current study is less than 0.004, the Navier-Stokes equation and no slip boundary condition still hold. The predicted pressure drop from micro-fluid CFD study is then compared with in-house flow measurement. The prediction error was found to be less than 3%.

Furthermore, the Langevin equation, a stochastic description of particle motion, is solved, which considers fluid motion, particle inertia, and Brownian dynamics to track the particle movement in the porous media. The filtration model is based on the realistic media structure and detailed particle motion physics. Monte Carlo simulations were conducted to obtain statistical filtration efficiency. The predicted filtration efficiency reflects the right trend and agrees with the currently available experimental data.

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