Design of Low-Speed Cascades for Investigating Viscous Effects in High-Speed Axial Turbines
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".