Examination of the Oil Combustion in a S.I. Hydrogen Engine
Bibliographic record
Abstract
Carbon monoxide (CO), carbon dioxide (CO2) and unburned hydrocarbon (UHC) are present in the exhaust gases of S.I. engines operated on pure hydrogen. These carbon-bearing species result from the oxidation of the lubricating oil and can be considered conveniently as natural tracers for indicating the lubricating oil consumption by combustion. Accordingly, such a novel approach can be employed to examine factors that affect engine oil consumption without the need to resort to more complex approaches. This contribution presents experimental results of oil combustion in a variable compression ratio single cylinder CFR engine when fueled with pure hydrogen established by determining the concentrations of CO and CO2 in the exhaust gas. The effects of changes in key operating variables that include equivalence and compression ratios, spark timing and the onset of knock on oil combustion are examined. It is to be shown that the oil consumption rates increase with increasing equivalence ratio, and hence load, while the effect of changes in compression ratio is relatively weak for non-knocking operation. These rates increase suddenly and rapidly once knocking is encountered. The oil combustion rates also correlates well with changes in the average values of the combustion duration, overall quenching distance, and the calculated maximum averaged burned products temperature.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".