Natural gas fuelling for heavy‐duty on‐road use: current trends and future direction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. Keywords: Heavy‐duty on‐road enginesAlternative fuelsNatural gasEmissionsEfficiency
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it