An Experimental Analysis of EGR on Operational Stabilities of Diesel Engines
Bibliographic record
Abstract
Abstract It has been recognized that high ratios of EGR are effective for in-cylinder NOx reduction, the mechanism of which has been largely attributed to thermal NOx reduction. However, engine operations also approach zones with higher instabilities when excessive EGR is used, usually accompanied with higher cycle-to-cycle variations. Although appropriate controlling strategies are capable to set up consistent EGR operations, any drifts in engine control will affect the originally optimized EGR when sufficient feedback control is not available. In reality, without sufficient EGR feedback control, the applicable EGR ratios need to be recessed from maximum allowable ratios in considering discrepancies of EGR control and influences of operating condition variations. An experimental investigation is provided in this paper to propose a method to evaluate the influences of EGR on such instabilities, in terms of independent effects of CO2 addition, O2 dilution, and N2 balancing, with simulated EGR. Extensive tests were conducted using a synthesized intake mixture testing facility. This paper is also a continuation of a cycle-to-cycle variation analysis on such operations reported previously by the authors (Zheng and Reader 1995). The analyses are aimed at identifying thresholds of stable operation with excessive high ratios of EGR whilst without subjecting to the consecutive cyclic disturbances associated actual EGR itself.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".