Recent advances in hybrid <scp>E</scp> ulerian– <scp>L</scp> agrangian description of atomization
Bibliographic record
Abstract
Abstract Atomization of liquid fuel is a crucial process to energy utilization. A thorough understanding of the physics of liquid atomization is challenging to acquire but necessary. During the past decades, numerical simulation methods for atomization with interface capturing schemes have been developed rapidly. However, several remaining issues need to be highlighted, such as the minimum size of the droplet to capture, numerical mass defects, and others. Thus, those simulations have been mostly limited to the primary breakup process and cannot capture the huge number of tiny droplets during the secondary breakup process. In recent years, a Eulerian–Lagrangian description of atomization has been introduced for the multi‐scale modelling of its whole process, in which the primary atomization process is treated in the Eulerian framework while the secondary atomization is treated in the Lagrangian framework. This hybrid method has been demonstrated to have many advantages in accuracy and efficiency. Considering its wide applications and contribution to the field, a comprehensive review is made in this work. First, an introduction to the phenomenon of atomization and the development of atomization numerical simulation in recent decades is made. Governing equations in the Eulerian and Lagrangian frameworks are then summarized. This is followed by a discussion of the hybrid combination method, the numerical framework, and its applications in atomization simulations. Last but not least, several relevant issues demanding attention are discussed as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".