An Empirical Study to Extend Engine Load in Diesel Low Temperature Combustion
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In this work, engine tests were performed to realize EGR-enabled LTC on a single-cylinder common-rail diesel engine with three different compression ratios (17.5, 15 and 13:1). The engine performance was first investigated at 17.5:1 compression ratio to provide baseline results, against which all further testing was referenced. The intake boost and injection pressure were progressively increased to ascertain the limiting load conditions for the compression ratio. To extend the engine load range, the compression ratio was then lowered and EGR sweep tests were again carried out. The strength and homogeneity of the cylinder charge were enhanced by using intake boost up to 3 bar absolute and injection pressure up to 180 MPa. The combustion phasing was locked in a narrow crank angle window (5~10° ATDC), during all the tests. The results indicate that a lower compression ratio helps to extend the engine load, while a combination of both intake boost and injection pressure is necessary to maintain low-NOx and low-soot emissions, and to mitigate the fuel efficiency penalty. This research intends to identify the major parameters that affect diesel LTC performance and to provide guidelines for improving the performance of such combustion modes.</div></div>
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".