Analysis of the Exit Flow Angle and Characteristics of Flow in Axial Turbine Cascades
Bibliographic record
Abstract
Abstract Despite a wealth of sophisticated CFD-methods, most designs are still based on one-dimensional and two-dimensional inviscid analytical tools. In such methods, realistic loss and angle assessment are indeed critical in order to arrive at correct loading, flow coefficient and reaction. The selected values are normally retained through the detailed design sequence for each iteration. This means that the throat sizing and hence the gauge angle is largely based on the early design work within the through-flow environment. Even one-degree error in angle estimation will turn into a rather large capacity error. For most designs, the exchange rate between capacity and gauge angle is on the order of 3–5 percent, per degree exit angle. In a previous publication, a methodology and equations were presented to assess the exit flow in an axial turbine blade row by Mamaev in Russian nomenclature and the tangential coordinate system. The approach, provided a unified and flow-physics based method for assessing exit angles from the geometry features like gauge angle, uncovered turning and flow features like Laval number, etc. Analysis of those formulas showed good agreement with physical flow pattern in real cascades for sub and transonic blade cascades. In this work, the same basic principal procedure is followed by employing the more international agreed nomenclature of blades such as an axial reference plane and Mach number. In the current work, the one-dimensional analysis results were compared with the three dimensional numerical modelling of a full annulus two-stage turbine. Analysis of the results showed the inherent unsteadiness specially outside the rotor blade cascades, however, comparison of the mass averaged exit angle with the one dimensional analysis showed satisfactory agreement.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".