REVIEW OF FUEL SPRAY DISTRIBUTIONS TO PREDICT PERFORMANCE OF ROTARY ATOMIZERS IN A SLINGER GAS TURBINE COMBUSTOR
Bibliographic record
Abstract
A research program was pursued on a rotary fuel nozzle commonly called a "slinger" atomizer for gas turbine application in order to characterize its spray distribution. The main advantage to be expected from this type of nozzle is a more uniform spray distribution leading to a more evenly distributed exit temperature at the combustor exit, thus providing a longer turbine life. In the course of this project, an in-depth open literature review was completed on rotary atomizers to find suitable correlations for an adequate spray distribution to eventually validate computational fluid dynamics (CFD) simulations to assess various potential injector configurations. After a substantial review of available correlations for the most promising types of rotary atomizers, i.e., flat disks, and plain (unperforated) and perforated cups, many uncertainties and inconsistencies were observed between reported test results and generated correlations to predict the average droplet size and a Rosin-Rammler spray distribution. A significant effort was devoted to validate and compare these found correlations in relation to their range of validity, to the flow regime, and to the measurement method for droplet diameters which showed as the most significant unknown parameters leading researchers to generate unreliable correlations. Ultimately after a thorough analysis, when comparing these three types of rotary atomizers for a given geometry and operating conditions, corresponding to a small typical gas turbine, the smallest SMD (based on idle condition) is achieved by the perforated cup operating in supercritical film mode, followed very closely by the plain cup, the flat disk, and the perforated cup operating in subcritical mode.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".