An Experimental Investigation on the Combustion and Emissions Performance of a Natural Gas–Diesel Dual Fuel Engine at Low and Medium Loads
Bibliographic record
Abstract
Natural gas is an abundant and inexpensive fuel in North America. It produces lower greenhouse gas emissions than diesel fuel when burned in an internal combustion engine. It is also considered to be a clean fuel because it generates lower particulate matter emissions than diesel fuel during combustion. In this study, an experimental study was conducted to investigate the combustion and emissions performance of a natural gas – diesel dual fuel engine at low and medium loads. A single cylinder direct injection diesel engine was modified to operate as the dual fuel engine. The diesel fuel was directly injected into the cylinder, while natural gas was injected into the intake port. The operating conditions, such as engine speed, load, intake temperature and pressure, were well controlled during the experiment. The effect of natural gas fraction on energy efficiency, cylinder pressure, exhaust temperature, and combustion stability were recorded and analyzed. The emissions data, including particulate matter, nitric oxides, carbon monoxide, and methane at various natural gas fractions and operating conditions were also analyzed. The results showed that natural gas – diesel dual fuel combustion slightly decreased brake thermal efficiency at low and medium load conditions and significantly reduced carbon dioxide and particulate matter emissions. Methane and NOx emissions increased in dual fuel combustion mode compared to diesel operation. The variation of carbon monoxide emissions in dual fuel mode depended on load and speed conditions.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".