Combustion and Greenhouse Gas Emissions of a Natural Gas-Diesel Dual Fuel Engine at Low and High Load Conditions
Bibliographic record
Abstract
Abstract The Paris agreement is exerting pressure on industries that generate significant greenhouse gas (GHG) emissions, such as transportation. Electrification can help reduce GHG emissions from light duty vehicles, but it is unfeasible for heavy duty vehicles that are predominately powered by diesel engines. Fuel switching from diesel to low carbon fuels is a more practical way helping reduce GHG emissions from heavy duty vehicles. Natural gas and renewable natural gas are low carbon or renewable fuels that generate much less carbon dioxide (CO2) emissions than diesel during combustion. Natural gas/renewable natural gas – diesel dual fuel combustion is an efficient way to replace diesel by natural gas/renewable natural gas in heavy duty diesel engines. This paper reports an experimental investigation on combustion and GHG emissions of a heavy duty natural gas – diesel dual fuel engine at different load/speed conditions. The variation in the effect of natural gas fraction on engine performance with changing engine load was compared and analyzed. Nitrous oxide (N2O), nitrogen oxides (NOx), methane (CH4) and CO2 emissions were experimentally investigated and analyzed. The results revealed that the effect of natural gas fraction on engine performance changed with varying engine load and speed condition. N2O emissions from a dual fuel engine changed with increasing natural gas fraction, but the effect of N2O emissions on overall GHG emissions was not significant. However, CH4 emissions contributed significantly to the overall GHG emissions in a dual fuel engine, especially at low load conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".