Spray Modeling for Outwardly-Opening Hollow-Cone Injector
Bibliographic record
Abstract
The outwardly-opening piezoelectric injector is gaining popularity as a high efficient spray injector due to its precise control of the spray. However, few modeling studies have been reported on these promising injectors. Furthermore, traditional linear instability sheet atomization (LISA) model was originally developed for pressure swirl hollow-cone injectors with moderate spray angle and toroidal ligament breakups. Therefore, it is not appropriate for the outwardly-opening injectors having wide spray angles and string-like film structures. In this study, a new spray injection modeling was proposed for outwardly-opening hollow-cone injector. The injection velocities are computed from the given mass flow rate and injection pressure instead of ambiguous annular nozzle geometry. The modified Kelvin-Helmholtz and Rayleigh-Taylor (KH-RT) breakup model is used with adjusted initial Sauter mean diameter (SMD) for modeling breakup of string-like structure. Spray injection was modeled using a Lagrangian discrete parcel method within the framework of commercial CFD software CONVERGE, and the new model was implemented through the user-defined functions. A Siemens outwardly-opening hollow-cone spray injector was characterized and validated with existing experimental data at the injection pressure of 100 bar. It was found that the collision modeling becomes important in the current injector because of dense spray near nozzle. The injection distribution model showed insignificant effects on spray due to small initial droplets. It was demonstrated that the new model can predict the liquid penetration length and local SMD with improved accuracy for the injector under study.
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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 | 0.001 |
| 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 | 0.000 |
| 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".