Fuel Property Effects on PCCI Combustion in a Heavy-Duty Diesel Engine
Bibliographic record
Abstract
An experimental study was performed to investigate fuel property effects on Premixed-Charge Compression Ignition (PCCI) combustion in a heavy-duty diesel engine. A matrix of research diesel fuels designed by the Coordinating Research Council, referred to as the Fuels for Advanced Combustion Engines (FACE), was used. The fuel matrix design covers a wide range of cetane numbers (30 to 55), 90% distillation temperatures (270 to 340°C) and aromatics content (20 to 45%). The fuels were tested in a single-cylinder Caterpillar diesel engine equipped with a common-rail fuel injection system. The engine was operated at 900 rpm, a relative air/fuel ratio of 1.2 and 60% exhaust gas recirculation (EGR) for all fuels. The study was limited to a single fuel injection event starting between −30° and 0°CA with a rail pressure of 150 MPa. The brake mean effective pressure (BMEP) ranged from 3.2 to 3.6 bar depending on the fuel and fuel injection timing. The experimental results show that cetane number was the most important fuel property affecting PCCI combustion behavior. The low cetane number fuels had better BSFC due to more optimized combustion phasing and shorter combustion duration. They also had a longer ignition delay period available for premixing, which led to near-zero soot emissions. The two fuels with high cetane number and high 90% distillation temperature produced significant soot emissions when the start of combustion occurred before the end of fuel injection. The two fuels with high cetane number and high aromatics produced the highest brake specific NOx emissions, although the absolute values were below 0.1 g/kW-hr. Brake specific HC and CO emissions were primarily a function of the combustion phasing, but the low cetane number fuels had slightly higher HC and lower CO emissions than the high cetane number fuels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".