The Influence of Channel Orientation and Flow Rates on the Bubble Formation in a Liquid Cross-Flow
Bibliographic record
Abstract
For gas turbines burning liquid fuels, improving fuel spray and combustion characteristics are of paramount importance to reduce emission of pollutants, improve combustor efficiency and adapt to a range of alternative fuels. Effervescent atomization technique, which involves the bubbling of an atomizing gas through aerator holes into the liquid fuel stream, has the potential to give the required spray quality for gas turbine combustion. Bubbling of the liquid stream is presently used in a wide range of other applications as well such as spray drying, waste-water treatment, chemical plants, food processing and bio- and nuclear-reactors. In order to optimize control of the required aeration quality and thus the resulting spray quality over a wide range of operating conditions, it is important that the dynamics of bubble formation, detachment and downstream transport are well understood under these circumstances. The paper reports on an experimental study conducted to investigate the dynamics of gas bubbles in terms of bubble detachment frequency when injected from an orifice that is subjected to a liquid cross-flow. The experiments were conducted over a range of gas and liquid flow rates and at various orientations of the liquid channel. Analyses presented here are based on shadowgraph images of two-phase flow, acquired using a high speed camera and a low intensity light source. An image processing algorithm was developed for the detection and characterization of the bubble dynamics. Results show that bubble detachment frequency is a function of both liquid cross-flow rate and the gas-to-liquid flow rate ratio.
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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 | 0.001 |
| 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".