Bibliographic record
Abstract
Atomization is a process ubiquitous in both nature and industry where a liquid flow is fragmented into a spray of small droplets. Despite having been studied extensively for almost a century, sprays remain poorly understood. The existing models are not based on a realistic understanding of the mechanisms of breakup and thus fail to provide good prediction of the spray behaviour across a wide range of operating conditions, in particular the size distribution of the spray droplets.In this thesis, the aerodynamic atomization of liquid drops and jets is studied experimentally and theoretically, with a focus on developing realistic models for the many sub-processes that occur during the breakup. It is shown that the initial deformation rate of a liquid drop exposed to a high speed gas flow governs its breakup morphology as well as the diameter of the ligaments produced from its deformation. Following the formation of these ligaments, a plurality of mechanisms are shown to occur throughout the fragmentation, which result in the distribution of droplet sizes. Analytical models are derived for each of the sub-processes of the breakup and compared with measurements of intermediate stages of droplet breakup, ultimately resulting in a prediction of the droplet size distribution from aerodynamic droplet breakup. Notably, the model presented in this thesis is unique in that it provides a complete description of the breakup process as well as a prediction of the resulting size distribution. Finally, the framework of the droplet breakup model is leveraged to model the atomization of a coaxial, twin-fluid spray, giving a deterministic, analytical prediction of the droplet size distribution resulting from twin-fluid atomization. The models developed in this work were made available in a Python implementation that we refer to as the Aerodynamic Droplet and Atomization Model (ADAM).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".