Numerical investigation of the impact of thermophoresis on the capture efficiency of diesel particulate filters
Bibliographic record
Abstract
Abstract The present study investigates the impact of thermophoresis on soot capture in the clean cordierite porous wall of a diesel particulate filter (DPF). A three‐step numerical model was developed, consisting of: (1) numerically reconstructing a representative volume of the cordierite porous wall, (2) computing gas flow through the porous wall using the lattice Boltzmann method, and (3) predicting of the DPF capture efficiency based on the time‐dependent solution of a modified Langevin equation that takes thermophoresis into account. The validity of the approach was verified by carefully comparing the permeability predictions of a reconstructed cordierite porous wall to data from the literature. The impact of the magnitude and orientation of the thermophoretic force on the capture of soot particles of various sizes under different flow conditions was investigated. The thermophoretic force applied in or against the flow direction significantly affected the particle capture for the highest particle sizes and flow velocity investigated. Our results suggested that thermophoresis can potentially affect soot deposition uniformity in DPFs. Since the thermophoretic force mainly impacted soot capture efficiency in the presence of particle and flow inertia, a dimensionless criterion defined as the product of Stokes and Reynolds numbers is proposed to predict the onset of what can be called thermophoresis‐enhanced inertial impaction. While similar results have been reported by others using simple flow geometries, this is the first time, to our knowledge, that this phenomenon is reported for a complex cordierite porous wall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".