Approximate pressure drop and filtration efficiency expressions for semi‐open wall‐flow channels
Bibliographic record
Abstract
Abstract Semi‐open wall‐flow channels are conventional filter channels with missing plugs either in the frontal or rear side. This configuration has lately become an interesting modelling issue with respect to partial filters designed for the emerging markets and as a special case of wall‐flow filters, that have suffered damage due to regeneration. This paper presents the development of approximate analytical expressions for the estimation of pressure drop and filtration efficiency of semi‐open channels. In order to facilitate the derivation, realistic assumptions are employed and the results are extensively validated using a commercial simulation software, able to solve the differential equations system of the 1D channel model. The validation covers important design parameters and operational conditions, namely filter permeability, length, mass flow rate and soot loading. It is shown that the analytical expressions can predict all trends and moreover offer clear physical explanations. The pressure drop calculation does not exceed 6% of the numerical prediction, while the filtration efficiency absolute error lies within 6% except for specific cases. Finally, a partially failed DPF is simulated using the developed expressions and a commercial CFD package in order to estimate the failure effect on filtration and pressure drop performance. In this way, the usability of the proposed modelling approach is demonstrated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".