LIQUID JET TRAJECTORY IN A SUBSONIC GASEOUS CROSS-FLOW: AN ANALYSIS OF PUBLISHED CORRELATIONS
Bibliographic record
Abstract
Liquid jet penetration/trajectory in a gaseous subsonic cross-flow has been studied extensively. Numerous correlations were proposed to predict the penetration of a liquid jet in a gaseous cross-flow. However, there are considerable inconsistencies between these correlations that negatively affect the reliability of this wealth of data. Therefore the objective of the present study was to address this issue. To do so, published correlations were grouped/categorized based on jet liquid type and ambient conditions of cross-flow. This resulted in four groups: (1) water jet in a cross-flow at room conditions, (2) liquid (excluding water) jet in a cross-flow at room conditions; (3) liquid (including water) jet in a cross-flow at room temperature and elevated pressure (i.e., P = 1−20 bar), and (4) liquid (including water) jet in a cross-flow at elevated pressure and temperature conditions (i.e., P = 1−20 bar and T = 280−650 K). A thorough analysis of published correlations in each group was carried out based on the most influencing factors/parameters. For instance, gas (cross-flow) Weber number has a significant effect on a liquid jet trajectory at low We, whereas it has a negligible effect at high We. A liquid jet with high viscosity and surface tension exhibited a trajectory closer to the wall. An increase in cross-flow temperature and pressure yielded a decrease in jet penetration height at a fixed momentum flux ratio and Weber number. On the basis of these analyses, a universal correlation form was developed to predict the penetration (or trajectory) of a liquid jet in a subsonic cross-flow. Finally, it should be noted that only jets issuing from injectors/nozzles with rounded exit circular orifices were considered in this study.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".