Measurements of Secondary Losses in a Turbine Cascade With the Implementation of Non-Axisymmetric Endwall Contouring
Bibliographic record
Abstract
An approach to endwall contouring has been developed with the goal of reducing secondary losses in highly loaded axial turbo-machinery. The present paper describes an experimental assessment of the performance of the contouring approach implemented in a low speed linear cascade test facility. The study examines the secondary flows of a cascade composed of Pratt and Whitney PAKB airfoils. This airfoil has been used extensively in low pressure turbine research and the present work adds intra-passage pressure and velocity measurements to the existing database. The cascade was tested at design incidence and at an inlet Reynolds number of 126,000 based on inlet midspan velocity and axial chord. Quantitative results include seven hole pneumatic probe pressure measurements downstream of the cascade to assess blade row losses, and detailed seven hole probe measurements within the blade passage to track the progression of flow structures. Qualitative results take the form of oil surface flow visualization on the endwall and blade suction surface. The application of endwall contouring resulted in lower secondary losses and caused a reduction in secondary kinetic energy associated with pitchwise flow near the endwall and spanwise flow up the suction surface within the blade passage. The mechanism of loss reduction is discussed in regards to the reduction of secondary kinetic energy.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".