Design of Low-Speed Cascades for Investigating Viscous Effects in High-Speed Axial Turbines
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Bibliographic record
Abstract
For turbine flow phenomena which are dominated by viscous effects, many valuable insights into the flow physics can be gained through low-speed cascade measurements. For example, for low-pressure turbines unsteady wake-blade interactions can be investigated in cascade provided that the Reynolds number, freestream turbulence conditions and the pressure coefficient distributions are the same in the cascade as in the high-speed counterpart. This paper describes an iterative procedure for inversely designing low-speed linear cascades with prescribed blade pressure-coefficient distributions. The inverse-design problem is treated as an optimization problem. The optimization strategy features the use of a genetic algorithm and a gradient-type algorithm. At the end of each global iteration of the design procedure a Navier-Stokes analysis is used to see if the final cascade geometry gives the specified pressure-coefficient distribution to the desired degree of accuracy. Although the resulting cascade may be designed to the level of accuracy afforded by the Navier-Stokes analysis, the method takes advantage of the fact that the pressure distribution in the low-speed cascade can be predicted with good accuracy and very rapidly using a panel method solution for the potential flow through the cascade. A panel method flow solver is used to minimize the number of Navier-Stokes evaluations to three or four for a given inverse-design problem. As a result, the present procedure is very efficient.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it