An Experimental and Modeling Study of HCCI Combustion Using n-Heptane
Bibliographic record
Abstract
Homogeneous charge compression ignition (HCCI) is an advanced low-temperature combustion technology being considered for internal combustion engines due to its potential for high fuel conversion efficiency and extremely low emissions of particulate matter and oxides of nitrogen (NOx). In its simplest form, HCCI combustion involves the auto-ignition of a homogeneous mixture of fuel, air, and diluents at low to moderate temperatures and high pressure. Previous research has indicated that fuel chemistry has a strong impact on HCCI combustion. This paper reports the preliminary results of an experimental and modeling study of HCCI combustion using n-heptane, a volatile hydrocarbon with well known fuel chemistry. A Co-operative Fuel Research (CFR) engine was modified by the addition of a port fuel injection system to produce a homogeneous fuel-air mixture in the intake manifold, which contributed to a stable and repeatable HCCI combustion process. Detailed experiments were performed to explore the effects of critical engine parameters such as intake temperature, compression ratio, air/fuel ratio, engine speed, turbocharging, and intake mixture throttling on HCCI combustion. The influence of these parameters on the phasing of the low-temperature reaction, main combustion stage, and negative temperature coefficient delay period are presented and discussed. A single-zone numerical simulation with detailed fuel chemistry was developed and validated. The simulations show good agreement with the experimental data and capture important combustion phase trends as engine parameters are varied.
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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 | 0.001 | 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) | 0.001 | 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".