Study of Heat Release Shaping via Dual-Chamber Piston Bowl Design to Improve Ethanol-Diesel Combustion Performance
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In this work, an innovative piston bowl design that physically divides the combustion chamber into a central zone and a peripheral zone is employed to assist the control of the ethanol-diesel combustion process via heat release shaping. The spatial combustion zone partition divides the premixed ethanol-air mixture into two portions, and the combustion event (timing and extent) of each portion can be controlled by the temporal diesel injection scheduling. As a result, the heat release profile of ethanol-diesel dual-fuel combustion is properly shaped to avoid excessive pressure rise rates and thus to improve the engine performance. The investigation is carried out through theoretical simulation study and empirical engine tests. Parametric simulation is first performed to evaluate the effects of heat release shaping on combustion noise and engine efficiency and to provide boundary conditions for subsequent engine tests. In the engine tests, the engine performance is examined through emission measurements and efficiency analyses, and combustion characteristics are investigated focusing on the correlation between the combustion noise and heat release shaping. As demonstrated by the test results, the heat release profiles representing the combustion events in the central zone and peripheral zone can be actively controlled via diesel injections (fuelling amount and timing), towards the desired heat release shaping. The maximum pressure rise rates are substantially reduced to levels of 5~8 bar/°CA that are in the practical range of modern production diesel engines. The dual-chamber piston bowl along with the injection strategy using an early diesel pilot for the peripheral zone and a near-TDC diesel pilot for the central zone shows a great potential of achieving clean, efficient, and smooth ethanol-diesel combustion.</div></div>
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".