Assessment of the Flow Quality of a Transonic Turbine Cascade
Bibliographic record
Abstract
The assessment of flow quality through a newly constructed transonic turbine cascade is presented. Although the main objective of this research was to investigate the effect of the modification of a vane profile due to repair on pressure loss, only the results for checking the flow periodicity, two-dimensionality of the flow and transonic exit flow condition are described in this paper. The cascade blades were constructed using the profiles of nozzle guide vanes of a low pressure turbine of an in-service turboshaft engine. The assessment of the flow quality in the cascade was carried out using three methods: wall static pressure measurements at the inlet and exit of each flow passage of the cascade to check the flow periodicity, surface flow visualization using blackened paraffin oil to check the two dimensionality of the flow and thirdly, Schlieren flow visualization to verify the periodicity and transonic flow conditions at the exit of the cascade. The cascade inlet and exit wall pressure showed that the flow was nominally periodic in the cascade. The surface flow visualization of the suction surface showed that the flow was two-dimensional on approximately 70% of the central span and also indicated flow separations on the suction surface. The Schlieren flow visualization confirmed the flow periodicity and revealed the existence of shock waves on the suction surface and near the trailing edge of the blades.
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 | 0.001 | 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) | 0.001 | 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".