Effects of gas viscosity and liquid-to-gas density ratio on liquid jet atomization in crossflow
Bibliographic record
Abstract
Atomization of liquid jets in gaseous crossflows has many natural and industrial applications, for example, in fuel atomization in gas turbine engines, rocket engines, film cooling, and, recently, suspension and solution precursor plasma spraying processes for the development of advanced coatings. Viscosity and density of the gaseous medium may significantly vary in applications such as plasma spraying, which can affect the instability waves on the liquid jet column, resulting in a major change in the mechanism of primary and secondary breakups. In this study, a numerical model is used to investigate the impact of gas viscosity on breakup mechanisms for a wide range of density ratios and Weber numbers. Due to many challenges, only a few comprehensive atomization measurements have been performed on this subject. However, novel computational models could provide the atomization process with a thorough picture in the last two decades. The incompressible variable-density Navier–Stokes equations are solved by using finite volume schemes, and a geometric volume-of-fluid technique is used to track the gas–liquid interface. In our parametric study, three sets of density ratios and Weber numbers are chosen. In each set, four cases with different orders of magnitude of gaseous Reynolds number are simulated. Different characteristics of jet atomization are analyzed, such as the jet trajectory, breakup location, and surface instabilities generated along the jet column. Ultimately, the effects of gaseous Reynolds number, density ratio, and Weber number on jet deformation and breakup mechanisms are discussed.
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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".