Trajectory of a Liquid Jet in a High Temperature and Pressure Gaseous Cross Flow
Bibliographic record
Abstract
An experimental study of liquid jet injection into subsonic air crossflow is presented. The aim of this study was to relate the jet trajectory to flow parameters, including jet and air velocities, pressure and temperature, as well as a set of nondimensional variables. For this purpose, an experimental setup was developed, which could withstand high temperatures and pressures. Images were captured using a laser-based shadowgraphy system. A total of 209 different conditions were tested and over 72,000 images were captured and processed. The crossflow air temperatures were 25 °C, 200 °C, and 300 °C; absolute crossflow air pressures were 2.1, 3.8, and 5.2 bars, and various liquid and gas velocities were tested for each given temperature and pressure. The results indicate that the trajectory and atomization change when the air and jet velocities are changed while keeping the momentum flux ratio constant. Therefore, it is beneficial to describe the trajectory based on air and jet Weber numbers or momentum flux ratio in combination with one of the Weber numbers. Also, examples are given where both Weber numbers are kept constant but the atomization is changed, and therefore, other terms beyond inertia terms are required to describe the spray behavior. It is also shown that the gas viscosity has to be considered when developing correlations. The correlations that include this term are generally better in predicting the trajectory. Therefore, Ohnesorge numbers in combination with the Weber numbers is used in the present correlations to describe the trajectories.
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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 | 0.000 | 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) | 0.001 | 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 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".