Performance and Emissions of a DI Diesel Engine Operated with LPG and Ignition Improving Additives
Bibliographic record
Abstract
This research investigated the performance and emissions of a direct injection (DI) Diesel engine operated on 100% butane liquid petroleum gas (LPG). The LPG has a low cetane number, therefore di-tertiary-butyl peroxide (DTBP) and aliphatic hydrocarbon (AHC) were added to the LPG (100% butane) to enhance cetane number. With the cetane improver, stable Diesel engine operation over a wide range of the engine loads was possible. By changing the concentration of DTBP and AHC several different LPG blended fuels were obtained. In-cylinder visualization was also used in this research to check the combustion behavior. LPG and only AHC blended fuel showed NOX emission increased compared to Diesel fuel operation. Experimental result showed that the thermal efficiency of LPG powered Diesel engine was comparable to Diesel fuel operation. Exhaust emissions measurements showed that NOX and smoke could be considerably reduced with the blend of LPG, DTBP and AHC.
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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 | 0.000 | 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) | 0.002 | 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".