Experimental and Numerical Study on an Inter-Turbine Duct
Bibliographic record
Abstract
The inter-turbine transition duct (ITD) between the high-pressure (HP) and low-pressure (LP) turbines of a gas turbine has the potential for significant length reduction and therefore engine weight reduction and/or aerodynamic performance improvement. This potential arises because very little is understood of the flow behavior in the duct in relation to the hub and casing shapes, and the flow entering the duct (e.g., swirl angle, turbulence intensity, periodic unsteadiness and blade tip vortices from upstream HP turbine blade rows). Moreover, it is unclear how well CFD is able to predict the complex flow-field in these ducts. This paper presents the results of a detailed experimental and computational study of an ITD, which is representative of a modern engine design. The experiments were conducted in a low-speed annular test rig where the effects of inlet free-stream turbulence intensities and swirl angle were investigated. Numerical studies were performed using commercial CFD software. The capability of different turbulence models, including the B-L, S-A, k-ε and SST models, have been explored. The predicted results are compared with the experimental data. Both experimental and numerical results are analyzed in detail to investigate the flow development both inside the ITD and along the end-walls.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".