A Numerical Investigation of Natural Gas-Diesel Dual Fuel Engine Combustion and Emissions Using CFD Model
Bibliographic record
Abstract
Natural gas (NG)-diesel dual fuel engines have been highlighted for their fuel flexibility and high thermal efficiency comparable to diesel engines. However, the addition of NG to compression ignition diesel engines was reported to elongate ignition delay and to increase the emissions of carbon monoxide (CO), unburned methane (CH4), and nitrogen dioxide (NO2). Past research on dual fuel engines has focused on the experimental research on the engine performance, combustion process, and exhaust emissions. The research on detailed mechanism dominating the impact of CH4 on formation of CO and NO2 in cylinder, and the mechanism for CH4 to survive the combustion process and slip through the cylinder is limited. The examinations of these mechanisms require the simulation of dual fuel engine combustion using a CFD model coupled with chemical kinetic mechanism. This research numerically investigates the combustion process and exhaust emissions from two NG-diesel dual fuel engines using a CFD model coupled with a reduced primary reference fuel (PRF) chemistry. The CFD model used is Converge-SAGE model with a maximum of 300000 grid points. The fuel chemistry used is a reduced PRF mechanism with 45 species and 142 reactions including a reduced NOx mechanism with 4 species and 12 reactions. The CFD model with reduced PRF chemistry has been validated against experimental data measured in a single-cylinder compression-ignition engine over a wide range of CH4 substitution ratio. A post-processing tool has been developed to calculate, analyze, and visualize the instantaneous rate of production (ROP) of key species in each cell with the known temperature, pressure, and species concentration exported by CFD code. The simulation results are further post-processed to numerically investigate the combustion process and the formation mechanism of CO, and NO2 in a dual fuel engine. The mechanism for CH4 to survive the main combustion process and post-combustion oxidation process is numerically examined.
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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 | 0.001 | 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".