Diesel Active-Flow Aftertreatment Control on a Heated Flow Bench
Bibliographic record
Abstract
Empirical investigations were carried out to explore the influence of parameters such as exhaust flow temperature, exhaust flow rate, and supplemental fuel amount on diesel aftertreatment devices. A heated flow-bench system was utilized in combination with a diesel lean NOx trap (LNT) and/or a diesel particulate filter (DPF). The heated flow bench had the capability of producing stable gas temperatures and pressure drop across these aftertreatment devices. Preliminary pressure drop diagnostics were conducted with unloaded substrates meant for LNT and DPF applications. Subsequently, the DPF was loaded with varying amounts of liquid water or liquid diesel fuel and pressure drop diagnostic tests were repeated to determine if the presence of liquid substances within the substrate could be detected. With the presence of a liquid substance, the DPF exhibited relatively flat and undetectable pressure drop variation up to a critical loading level. Once this level was reached, there was a sharp and sudden increase in pressure drop. Further tests investigated the effects of exhaust flow rate and supplemental fuel amount on raising the LNT substrate temperature as required for the LNT de-NOx regeneration process. The results suggested that the maximum substrate temperature was primarily dependant on the fuel amount. Although the exhaust flow rate had very little effect on the substrate’s maximum temperature, it was significant in determining how quickly the maximum temperature was reached.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".