A new model based on coupling of MEP/CFD/ILIA for prediction of primary atomization
Bibliographic record
Abstract
Abstract The maximum entropy principle (MEP) is one of the first methods which was used to predict droplet size and velocity distributions of sprays. Due to some drawbacks of this model, the predicted results are not well matched with experimental data. This paper presents a different approach to improve the MEP model. It is suggested that improving the available energy source in the MEP model equation be made by numerical solution of the flow inside the injector based on CFD. This will enhance the accuracy of turbulent kinetic energy calculation of sprays. The forces acting on a liquid‐gas interface lead to the growth of disturbances originating inside the injector. In this paper, the instability analysis of wave motion on the surface of a swirling liquid sheet exposed to inner and outer gas streams is presented. However, the linear instability analysis used is different from prior analyses. A cylindrical annular liquid sheet was considered in previous studies, but in this study the instability theory is implemented on a cone‐shaped annular liquid sheet for different cone angles. Applying these procedures enhances the model predictions. The liquid sheet properties found by CFD analysis and the results of improved linear instability analysis (ILIA) are also applied to calculate the momentum source in the MEP model. By coupling these models, the MEP model can be run independently of experimental data. The proposed model was applied to predict the droplet size and velocity distributions of a hollow‐cone spray. The results show close agreement with 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 | 0.000 |
| 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 | 0.000 |
| 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".