Spray Modeling for Outwardly-Opening Hollow-Cone Injector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it