3 D Simulation of Multiple Injections in DI Diesel Engine(Diesel Engines, Combustion Modeling II)
Bibliographic record
Abstract
Three dimensional calculations of a split injection in DI Diesel engine were performed using the KIVA3V, release 2, CFD code. The detailed chemistry approach used involves the Partially Stirred Reactor (PaSR) model for the turbulence-chemistry interaction coupled with the chemical mechanism of a diesel fuel surrogate represented by a mixture of n-heptane and toluene (68 species, 270 reactions). When simulating the Volvo NEDS DI Diesel engine in a split injection mode, it was found that the droplet collisions play a considerable role in predicting the rate of heat release during the pilot injection. It was found that if a collision probability is over-predicted, it causes a droplet cluster formation and too fuel lean conditions resulting in a decrease in combustion intensity. The default collision model in the KIVA3V code, formulated by O'Rourke, is replaced by the modified model proposed in Nordin. The O'Rourke model formulation defines collision frequency inverse proportional to the cell volume that causes grid dependence and does not discriminate droplet trajectories which are not intersecting each other. In a new grid independent formulation, two vital requirements have to be met for collision between two droplets to occur: first, they have to travel towards each other being in a so-called "collision" cone and second, all colliding droplets are constrained to a certain exponential time-decaying initial probability in the Poisson law. Using the new formulation, higher combustion intensity was achieved during the combustion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".