Non-Evaporating Kerosene Fuel Spray Tip Penetration: A Comparison between Phenomenological Models and Experiments
Bibliographic record
Abstract
Interest in the use of kerosene fuel in diesel engines has garnered researchers’ attention in the past few years due to its improve premixed combustion and its ability to decrease soot emission. The potential of using kerosene in the design stage of a diesel engine is thus a great motivator to study fuel spray development and to evaluate known fuel spray tip correlations and models with respect to their predictive capability with such a fuel. Therefore, the present paper proposes to investigate the spray development of a multi-hole solenoid injector fueled with kerosene under non-evaporative conditions. Moreover, the experimental results are used to evaluate how different phenomenological models proposed in the literature for diesel fuel are able to predict kerosene spray tip penetration. The experimental test rig is composed of a constant-volume pressurized vessel and a camera allowing to visualize the liquid phase using a backlight illumination technique. The influence of the injection pressure is studied at 400, 800 and 1600 bar, while three different injection durations (0.5, 1, and 2 ms) and five ambient pressures (2.5, 5, 10, 15 and 20 bar) are investigated. The experimental results are presented using a nondimensional time and fuel spray tip penetration to facilitate the analysis. The results show, as expected, that increasing the injection pressure or decreasing the ambient pressure results in a faster fuel spray tip penetration. The models that are evaluated include a constant ambient density hypothesis formulation, a variable ambient density model and three empirical correlations. A comparison between the models and experimental results shows that low injection pressure and short injection duration are two conditions in which the models have difficulty to predict the fuel spray tip penetration. Overall, the best performance was offered by the variable density model, which predicted the experimental data well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".