Investigating the influence of varying split injection profiles on stability of diesel engine operated under partially premixed mode with methanol
Bibliographic record
Abstract
The present study explores the prospects of operational stability of dual fuel operation comprehensively, considering that; methanol and diesel are of two different kinds which exhibit different combustion processes. To this effect, investigating the stability of engine operation under such condition becomes important. In this investigation, the harshness of the operation and the non-repeatability of the combustion cycles are examined, which have been identified as the major stability indicator in many studies. Such stability indices are characterized in this study through a comprehensive set of parameters, including of maximum pressure rise rate (ROPRmax) and coefficient of variation of indicated mean effective pressure (COVIMEP); and of peak pressure (COVPP) and crank angle of 50% mass fraction burn (COVCA50). The experimental investigation is carried out under a split injection strategy by varying injection timings as well as injection mass percentages at predefined methanol injection durations. It is shown that the partially premixed mode under the split injection strategy exhibits significant potential in reducing the harshness of operation indicated by lower peak pressure rise and increasing the repeatability of the combustion cycles implied by the substantially lower scores of the considered parameters for mapping stability.
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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 | 0.000 | 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 | 0.000 |
| 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".