Experimental and Computational Investigation of Losses in a Circular to Oblong Diffusing Gas Turbine Transition Duct With Offset
Bibliographic record
Abstract
The installation of a gas turbine engine onto an aircraft often necessitates the use of complicated ducting for the inlet and exhaust from the engine nacelle. These ducts may involve shape transitions as well as significant redirection of the flow. The optimization of these ducts may reduce the performance losses typically incurred with inlet and exhaust ducting. This study examines the aerodynamic and performance characteristics of a specific duct geometry that is a combination of a transition duct and an s-shaped duct. The study is motivated by aircraft gas turbine exhaust design considerations including lower engine back pressure, reduced wing sooting, and the reduction of thermal fatigue issues with aircraft structures. Experimental and computational (CFD) techniques were used to investigate the internal flow structure and distortion losses. Swirl was introduced to determine the sensitivity of the duct to the rotating flow typical of a gas turbine. Cold flow testing was performed for a scale model of the duct and compared with results from a commercial 3D CFD code. Velocity profiles were well predicated but loses were underpredicted by the RNG-kε turbulence model. The study has shown the value of CFD for the prediction of general performance trends in the design of a practical engineering device.
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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 | 0.001 |
| 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".