Thermofluids analysis of combustion, emissions, and energy in a biodiesel (C11H22O2) / natural gas heavy-duty engine with RCCI mode (Part I: Single/ two -stage injection)
Bibliographic record
Abstract
One of the novel methodologies used to increase energy efficiency and reduction of environmental pollutants in internal combustion engines is the idea of low-temperature combustion (LTC) especially reactivity controlled compression ignition (RCCI). Given that the ultimate goal of RCCI combustion is combustion controllability through in-cylinder reactivity stratification by using two different fuels, there are many modifiable factors, which can be improved or adjusted. The aim of this research is to use the concept of RCCI combustion in a biodiesel (C 11 H 22 O 2 ) / natural gas heavy-duty engine and the performance and amount of pollutants of engine output is evaluated and compared by modifying the input parameters (different fuel injection strategy). Accordingly, numerical simulations have been carried out to study combustion in the geometry of the Caterpillar 3401E engine with CONVERGE computational fluid dynamic software and the SAGE combustion model. Results show that: By changing the biodiesel injection time (from -40° to -60°), although, the rate of heat released decreases, output work and Indicated mean effective pressure (IMEP) increase. By increasing the lag of time between the first and second injections (in Cases 1–3), the IMEP will increase from 6.9 to 8.2 bar and the work from 1686 to 1997 J. In Cases (7–9), the mass of HC and CO pollutants has drastically decreased with the onset of injection earlier, whereas the mass of NOx pollutants has increased.
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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".