Building Lagrangian injectors from resolved primary atomization simulations. Application to jet in crossflow fuel injection
Bibliographic record
Abstract
This work aims at improving Lagrangian particle injectors for the simulation of sprays. In such simulations, primary atomization is not resolved and Lagrangian particles are directly injected as a dispersed phase in the flow. Two main challenges arise in such methodology: i) the prescription of the correct droplet size and velocity distributions at injection, ii) ensuring the proper coupling of the dispersed phase with the gas phase to have the correct gas flow field after the injection. The proposed approach relies on an improved Lagrangian injector model and on resolved primary atomization simulations to feed the injector model parameters. The resolved atomization simulations are performed using a sharp-interface approach (ACLS/GFM) on unstructured grids The validation test case is a high-pressure, non-reactive kerosene jet in crossflow (JICF) atomizer configuration [3], which is representative of complex injection systems. Resolved simulations of atomization for this configuration are performed and validated against the experimental correlation for the jet trajectory, showing good accordance. These simulations are then post-processed to feed the Lagrangian injector model. Finally, the injectors are applied to the same configuration and compared to experimental data.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".