Efficiency and Emission Trade-Off in Diesel-Ethanol Low Temperature Combustion Cycles
Bibliographic record
Abstract
An experimental investigation of low temperature combustion (LTC) cycles is conducted with diesel and ethanol fuels on a high compression ratio (18.2:1), common-rail diesel engine. Two LTC modes are studied; near-TDC injection of diesel with up to 60% exhaust gas recirculation (EGR), and port injected ethanol ignited by direct injection of diesel with moderate EGR (30-45%). Indicated mean effective pressures up to 10 bar in the diesel LTC mode and 17.6 bar in the dual-fuel LTC mode have been realized. While the NOx and smoke emissions are significantly reduced, a thermal efficiency penalty is observed from the test results. In this work, the efficiency penalty is attributed to increased HC and CO emissions and a non-conventional heat release pattern. The influence of heat release phasing, duration, and shape, on the indicated performance is explained with the help of parametric engine cycle simulations. Empirical and parametric analyses suggest that while HC and CO emissions are consistently high for the LTC test cases studied, their contribution to efficiency penalty largely depends on the engine load. The heat release profile has a significant effect on the maximum attainable thermal efficiency within the mechanical operating limits. Specifically, the thermal efficiency is the highest within a narrow combustion phasing window, identified as 7-12 °CA after top dead center for the test engine. A shorter burning duration has a positive effect on thermal efficiency while the peak cylinder pressure and pressure rise rate are negatively impacted. The heat release shaping and splitting have minimum effects on the thermal efficiency but influence the peak cylinder pressure rise rate. The results and discussion presented in this paper highlight the trade-offs experienced in enabling LTC with diesel and diesel-ethanol combination in comparison to the conventional diesel combustion.
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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 | 0.001 | 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".