Infrared Heat Transfer Coefficient Measurements in an Engine-Scaled Turbine Stage
Bibliographic record
Abstract
Abstract This article presents full-field heat transfer coefficient measurements using infrared thermography in an engine-scaled, partially cooled turbine stage in the NG-Turb facility at DLR Göttingen. The work was carried out as part of the European Commission-funded FACTOR programme. Full-field heat transfer coefficient measurements are needed to account for the effects of combustor turbulence, hot spot location, and migration on turbine stage heat transfer, which remain a challenge for state-of-the-art design methods. Measurements were made on stationary vanes and struts and in the rotating frame of the blades. The work utilizes a traditional transient measurement method and a novel phase-based technique. The phase-based technique exploits the phase shift between oscillating fluid and wall temperatures, which varies with local heat transfer coefficient. Compared to the traditional transient method, the phase-based approach enhances spatial resolution by an order of magnitude, increases robustness against measurement noise and calibration errors, and lowers the heating power input. The measurement methodology is described in detail and full-field Nusselt numbers are presented on a film-cooled nozzle guide vane, a rotor blade, and a low-pressure strut. The conditioning of the measurements allows them to resolve the effects of film cooling injection, shock-boundary layer interaction, boundary layer transition, and tip leakage flow on local heat transfer coefficients.
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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) | 0.002 | 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".