Effect of Diesel Injection Split on Combustion and Emissions Performance of a Natural Gas–Diesel Dual Fuel Engine at a Low Load Condition
Bibliographic record
Abstract
As an inexpensive and low carbon fuel, the combustion of natural gas reduces fuel cost and generates less carbon dioxide emissions than diesel and gasoline. Natural gas is also a clean fuel that generates less particulate matter emissions than diesel during combustion. Replacing diesel by natural gas in internal combustion engines is of great interest for industries. Dual fuel combustion is an efficient way to apply natural gas in internal combustion engines. An issue that to a certain extent offsets the advantage of lower carbon dioxide emissions in natural gas–diesel dual fuel engines is the higher methane emissions and low engine efficiency at low load conditions. In order to seek strategies to improve the performance of dual fuel engines at low load conditions, an experimental investigation was conducted to investigate the effect of diesel injection split on combustion and emissions performance of a heavy duty natural gas–diesel dual fuel engine at a low load. The operating conditions, such as engine speed, load, intake temperature and pressure, were well controlled during the experiment. The effects of diesel injection split ratio and timings were investigated. The engine efficiency and emissions data, including particulate matter, nitric oxides, carbon monoxide and methane were measured and analyzed. The results show that diesel injection split significantly reduced the peak pressure rise rate. As a result, diesel injection split enabled the engine to operate at a more optimal condition at which engine efficiency and methane emissions could be significantly improved compared to single diesel injection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".