An Experimental Study on the Effect of Intake Pressure on a Natural Gas-Diesel Dual-Fuel Engine
Bibliographic record
Abstract
Abstract Natural gas-diesel dual-fuel (NDDF) combustion can be a viable method to reduce diesel usage in compression ignition (CI) internal combustion engines. Potential benefits of NDDF engines in comparison to conventional diesel engines include decreases in particulate matter (PM) and carbon dioxide (CO2) emissions. This study focuses on the effect of intake pressure on a dual-fuel engine with intake port injected natural gas (NG) and in-cylinder direct injected diesel at two typical engine operation conditions—low load-high speed and high load-low speed. The research work was performed on a heavy-duty, four-stroke CI, single-cylinder research engine at a NG-diesel energy ratio of approximately 3:1. The results show that when the intake pressure was increased, the indicated thermal efficiency (ITE) decreased and increased at the low load-high speed and high load-low speed conditions, respectively, for NDDF combustion. For the low load-high speed NDDF combustion, increasing intake pressure increased the carbon monoxide, methane, and soot emissions, but decreased the nitrogen oxide (NOx) emissions. For the high load-low speed NDDF combustion, increasing intake pressure caused the methane emissions to increase, and the carbon monoxide, NOx, and soot emissions to decrease. In-cylinder temperature measured at the tip of the diesel injector showed that the injector tip temperatures were higher for NDDF cases compared to diesel cases and these temperatures could be correlated with the combustion phasing and the NOx emissions. Increasing intake pressure caused lower injector tip temperatures for both NDDF operating conditions. Equivalent CO2 emissions for the low load-high speed and high load-low speed NDDF cases were higher and lower than the corresponding diesel cases, respectively.
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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 | 0.001 |
| 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".