Fuel Spray Tip Penetration Model for Double Injection Strategy
Bibliographic record
Abstract
In this paper, a semi-empiric fuel spray tip penetration model is proposed. It is applied to single and double injection strategies taking into account the early and far field penetration. The model is based on the momentum flux as initially proposed by [1] for single injection but it is derived from mean mass flow rate herein. Fuel spray interaction with entrainment air is taken into account for the second injection. The proposed model is calibrated and validated using data from 9 experiments conducted with an indirect piezoelectric diesel injector under various injection strategies. The experiments included 1) injection rate measurements using the Bosch method to determine mean mass flow rate during injector opening as well as obtaining injection duration which are both entry parameters to the model; 2) Fuel spray tip penetrations were measured in a pressure vessel using high speed photography for single and double injection strategies. The experimental results were used to calibrate the model's constants and to verify the model's ability to predict spray behavior. Finally, the experimental results for some fuel injection strategies have shown that there is an interaction between the first and second spray and the paper shortly discusses these cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".