The effect of fuel octane and dilutent on homogeneous charge compression ignition combustion
Bibliographic record
Abstract
This paper presents some experimental operating and combustion properties of homogeneous charge compression ignition (HCCI) combustion. HCCI operating range, start of combustion, burn duration, indicated mean effective pressure, indicated specific emissions, and indicated specific fuel consumption are evaluated as charge dilution and octane number are varied. Primary reference fuels with octane numbers of 20, 40, and 60 are used in this study. The autoignition properties of the air-fuel mixture are varied by changing the fuel octane number, percentage of exhaust gas recirculation (EGR), and air-fuel ratio, while holding the intake temperature, engine speed, and compression ratio constant. Results show that both the start of combustion and the burn duration are sensitive to mixture dilution (excess air or EGR). The fuel octane number is not an effective method of controlling the start of combustion or the burn duration but can be used to increase the load range of the HCCI engine. Both the NO x emissions and the indicated specific fuel consumption increase as the octane number is increased due to lower dilution and higher peak temperatures. Correct amounts of dilution are critical in controlling HCCI combustion. Separating dilution into EGR and excess air, it is found that a given amount of EGR is more effective at controlling the start of combustion and the burn duration than the same amount of excess air.
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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) | 0.000 | 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".