Influence of Mach Number on Profile Loss of Axial-Flow Gas Turbine Blades
Bibliographic record
Abstract
The current profile loss prediction methods for axial turbine blades usually predict a monotonic increase in profile losses at outlet Mach numbers above 1.0, while linear cascade testing in the literature has revealed a more complex behaviour. An objective of this investigation was to help clarify the flow features that are most influential on the profile losses in the transonic and supersonic regimes. Four linear cascades of turbine blades were investigated both experimentally and computationally, at design incidence. Measurements were carried out over an outlet Mach number range of roughly 0.5 to 1.4, and a Reynolds number range of about 5 × 105 to 1.4×106. It was found that the profile losses of the four cascades exhibited a loss “plateau”, where the total pressure loss coefficient became approximately constant over a range of outlet Mach numbers spanning the low supersonic range. Cascades of different geometries exhibited different extents of this loss plateau, and a commonly used Mach number correction for profile losses did not capture the behaviour. In the literature, a relationship has been observed between the base pressure and the profile losses. The base pressure was linked to the losses in the trailing edge wake and in the trailing edge shock system. For this reason, base pressure data were obtained from blades instrumented with a static tap at the trailing edge, and also from computational fluid dynamics (CFD). The results provided insight into the role of the base pressure in the profile losses through the transonic regime. It was concluded from this study that an accurate prediction of the base pressure may serve as a basis for a revised Mach number correction to be applied to the profile loss correlation in the transonic and supersonic flow regimes.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".