Natural gas fuelling for heavy‐duty on‐road use: current trends and future direction
Bibliographic record
Abstract
The use of natural gas as an alternative fuel offers the potential for significant benefits, including lower engine‐out emissions compared to conventional fuels. Most in‐use heavy‐duty natural gas engines use a premixed charge of fuel and air which is then ignited by a spark plug. While these systems meet current emissions standards, substantial further reductions are required to meet upcoming regulations. Efficiency penalties due to poor fuel utilization at low load with such premixed charge engines are also a concern. As a result, there is scope for further research into potential improvements to natural gas‐fuelled heavy‐duty engines, especially through direct injection. This work reviews the various alternatives, both in‐use and under development, for fuelling a heavy‐duty engine with natural gas. The emphasis is placed on providing an understanding of the performance of current heavy‐duty natural gas fuelled engines and improvements that future technologies may offer. The need for further fundamental and applied research, both computational and experimental, is also identified.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".