Thermofluids analysis of combustion, emissions, and energy in a biodiesel (C11H22O2) / natural gas heavy-duty engine with RCCI mode (Part II: Fuel injection time/ Fuel injection rate)
Bibliographic record
Abstract
Among the measures that improve the combustion process and reduce emissions in the design of dual-fuel engines is the use of the RCCI combustion strategy. This paper attempts to investigate the effects of biodiesel and diesel fuels, fuel injection time, and fuel injection rate in the RCCI combustion mode. In this study, using CONVERGE CFD commercial software and SAGE combustion model for the simulation. Accordingly, the geometry of a biodiesel (C11H22O2) / natural gas dual-fuel engine (Caterpillar 3401E) was exploited for experimental tests and numerical simulations. Results show that: The pressure inside the combustion chamber is especially greater from -12° to +10° crank angle for biodiesel, in fact, the maximum pressure for biodiesel fuel is approximately equal to 9.12 MPa and for diesel fuel is equal to 8.9 MPa. The mass of HC emissions for the two fuels is almost equal, but the mass of CO emissions for biodiesel is higher than diesel. By reducing the duration of fuel injection from 16° to 8° crank angle, the mass of soot is facing a decreasing trend, and this amount lowers from 0.065 to 0.00086 mg. Moreover, the indicated mean effective pressure (IMEP) and production work have increased from Case A (8 and 16 mg biodiesel in the first and second injection) to C (16 and 8 mg biodiesel in the first and second injection), and the knocking rate is reached from 3.4 to 6.6.
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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 | 0.000 |
| 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) | 0.001 | 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".