Influence of Freestream Turbulence on the Aerodynamic Performance of Transonic Vanes
Bibliographic record
Abstract
The present authors have reported a noticeable reduction in the aerodynamic performance of turbine vanes which had been modified as a result of commonly applied repair processes. These tests were done at a low turbulence level to isolate the profile-only effect. In the present research, the effect of the same profile modification on the performance of the vanes was investigated at engine representative flow conditions by increasing the turbulence level and length scale. Since the tested vane profiles in the present research were synthesized using the profile of LPT vanes, the turbulence level was maintained at around 4% and the length scale was set at 2 cm. In the present investigation, calculations with computational fluid dynamics and measurements in a transonic cascade rig were carried out. The high turbulence level in the cascade rig was produced using a passive turbulence-generating grid and in CFD by specifying the desired level and length scale. Coordinates of the baseline profile were obtained from the LPT vanes of an in-service turboshaft engine using 3D optical scanning and digital modeling. The repaired vanes were synthesized using profiles representative of two specific repair types. In both methods, flow visualization was carried out using axial density gradient or schlieren and exit total pressure was obtained numerically or using a multihole probe. Further insight into the flow phenomenon was obtained by surface flow visualization in the cascade rig using a graphite and paraffin oil mixture and by computed surface pressure distributions on the vane. The shock pattern in the cascade for low and high turbulence flows was similar; however, the surface flow pattern exhibited a significant difference for the two conditions. The total pressure ratio and cascade loss also showed some differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".