Combustion Performance and Unburned Hydrocarbon Emissions of a Natural Gas–Diesel Dual Fuel Engine at a Low Load Condition
Bibliographic record
Abstract
The combustion of natural gas reduces fuel cost and generates less emissions of carbon dioxide and particulate matter than diesel and gasoline. Replacing diesel by natural gas in internal combustion engines is of great interest for transportation and stationary power generation. Dual fuel combustion is an efficient way to burn natural gas in internal combustion engines. In natural gas–diesel dual fuel engines, unburned hydrocarbon emissions increase with increasing natural gas fraction. Many studies have been conducted to improve the performance of natural gas–diesel dual fuel engines and reported the performance of combustion and emissions of regulated pollutants and total unburned hydrocarbon at various engine operating strategies. However, little has been reported on the emissions of different unburned hydrocarbon components. In this paper, an experimental investigation was conducted to investigate the combustion performance and emissions of various unburned hydrocarbon components, including methane, ethane, ethylene, acetylene, propylene, formaldehyde, acetaldehyde and benzaldehyde, at a low engine load condition. The operating conditions, such as engine speed, load, intake temperature and pressure, were well controlled during the experiment. The combustion and emissions performance of pure diesel and natural gas–diesel dual fuel combustion were compared. The effect of diesel injection timing was analyzed. The results show that appropriately advancing diesel injection timing to form a homogeneous charge compression ignition-like combustion is beneficial to natural gas–diesel dual fuel combustion at low load conditions. The emissions of different unburned hydrocarbon components changed in dual fuel combustion, with emissions of some unburned hydrocarbon components being primarily due to the combustion of natural gas, while those of others being more related to diesel combustion.
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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 | 0.001 |
| 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".