Bibliographic record
Abstract
The need to control climate change and improve the fuel effieciency of internal combustion engines has prompted global efforts to develop alternative fuels in order to reduce dependence on conventional petroleum derivatives. This thesis deals with three such alternative fuels: compressed natural gas (CNG), pure methane (used to mimic biogas), and methanol. The first parts of the thesis discuss experimental investigations into conventional gas-Diesel dual-fuel combustion.The effect of the CNG/methane supplement ratio on engine performance and emissions was explored at two different load points. The results indicated improved performance and emissions at intermediate load more than at low load. In addition, a 3D dual-fuel combustion model developed at Chalmers was validated against experimental data generated during these studies. Reasonably good agreement was achieved between experiment and simulation for most aspects of engine performance, but there were some discrepancies regarding the onset of ignition delay and emissions.The later parts of the thesis deal with studies on a low temperature combustion concept, Reactivity Controlled Compression Ignition (RCCI), using two alternative fuels: CNG and methanol. Engine performance and emissions were studied for both CNG-Diesel and methanol-Diesel RCCI combustion. Experiments on CNG-Diesel RCCI combustion were performed to explore the effcts of different engine parameters on engine performance and emissions, revealing that high indicated thermal efficiencies (over 50%) could be achieved. However, this combustion strategy presented difficulties when operating at high load and high compression ratios due to the peak incylinder pressure limitation. Another CNG-Diesel RCCI combustion study was therefore conducted, focused on extending the operational load range for this combustion strategy and improving combustion phasing by using late inlet valve closing (IVC). This approach increased the maximum load for CNG-Diesel RCCI combustion by 40% compared to the first CNG-Diesel RCCI study. Finally, experiments on methanol-Diesel RCCI combustion showed that port injection of methanol offered better performance than direct injection of methanol during either the intake stroke or the compression stroke in terms of net indicated thermal efficiency and emissions of HC and CO.
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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 | 0.001 | 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".