Diesel pressure departure ratio algorithm for combustion feedback and control
Bibliographic record
Abstract
The pursuit for higher efficiency and ultra-low exhaust emissions from diesel engines requires the combustion process to be precisely controlled so as to minimize departures from the intended engine operation. The combustion control system must be able to perform corrective actions on a cycle-by-cycle basis, with a robust feedback on the combustion process. The combustion phasing, commonly represented by the crank angle of 50% heat release and derived from the measured cylinder pressure data, shows a strong correlation to the efficiency and the engine-out nitrogen oxide emissions. To accurately estimate the combustion phasing from the derived heat-release rate, the authors previously introduced and experimentally validated a computationally efficient diesel pressure departure ratio algorithm, against selected cases of boost, engine load and exhaust gas recirculation. In this work, the formulation of the pressure departure ratio algorithm is presented in detail along with its implementation to enable combustion control during both transient and steady-state engine operations. Engine tests demonstrate that the algorithm was effective in stabilizing the combustion process on a cycle-by-cycle basis for a range of engine speeds, load and exhaust gas recirculation, which included conventional and low-temperature diesel combustion modes.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".