Investigating the Effect of Spray Targeting and Impingement on Diesel Engine Cold Start
Bibliographic record
Abstract
Analysis of the cold-starting performance of diesel engines requires the development of advanced models to describe the multicomponent nature of the fuel as well as the spray impingement and wall film behavior. A new approach to modeling the multicomponent nature of commercial fuels was implemented. This model is based on a continuous distribution using a probability density function, rather than the use of discrete components, to capture more accurately the entire range of composition in commercial fuels. The model was applied to single droplet calculations to validate the predictions against experimental results. Previous discrete component wall-film modeling has been extended to include the continuous multicomponent fuel representation. A significant factor that has received little attention in analyzing the cold-start performance of diesel engines is the spray impingement angle and location. This has been investigated using the modified KIVA code. The predictions show the importance of including both the multicomponent nature of the fuel, as well as a detailed model of the wall-film and spray-wall interaction. The multicomponent fuel modeling is critical to capturing the correct vaporization trends, and the spray-film interaction modeling is crucial to capturing the spray impingement and subsequent secondary atomization that produces smaller drops. The spray targeting, by way of enhanced secondary atomization (splashing), was found to be a powerful way of enhancing cold start. However, optimal spray targeting for cold-start performance may lead to deteriorated performance at other operating conditions.
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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) | 0.000 | 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".