An engine cycle analysis of diesel-ignited ethanol low-temperature combustion
Bibliographic record
Abstract
Modification of the fuel–air charge properties has the potential to improve the load range of low-temperature combustion with ultra-low nitrogen oxide emissions (less than 0.2 g/kW h) and ultra-low smoke emissions (less than 0.01 g/kW h). The ignition characteristics of the cylinder charge are altered by injecting the highly reactive diesel fuel into a homogeneous lean air–fuel mixture of low-reactivity fuel. The ethanol–diesel combination has been of particular recent interest since ethanol is a renewable biofuel. The additional advantages of ethanol include excellent anti-knock properties, high volatility and reduction in the compression work through charge cooling. In this work, a detailed investigation using diesel-ignited ethanol experiments was conducted on a high-compression-ratio (18.2:1) diesel engine. The emissions, the combustion performance and the thermal efficiency characteristics are analysed at different values of the exhaust gas recirculation, the intake boost pressure, the ethanol fraction and the diesel injection timing. The empirical investigations supported by detailed zero-dimensional engine cycle simulations indicate that a diesel injection timing close to top dead centre provides direct control over the ignition timing across the engine load range. The nitrogen oxide–soot trade-off of conventional diesel combustion, which is affected by exhaust gas recirculation, is minimized to achieve clean combustion over a wide load range (indicated mean effective pressure, 4–17 bar) with increased ethanol fraction and moderate intake dilution through a combination of modulation of the exhaust gas recirculation level and an increase in the intake boost pressure. The operation at low loads is constrained by the minimum diesel amount necessary for stable and efficient combustion while progressively retarded combustion phasing is necessary at higher loads to satisfy the physical engine constraints (peak cylinder pressure, less than 170 bar; peak pressure rise rate, less than 15 bar/deg crank angle). The improved understanding of this combustion strategy through experimental and theoretical research provides the necessary guidance for obtaining clean efficient full-load operation (demonstrated at an indicated mean effective pressure of 19.2 bar).
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".