Numerical Investigation of the Performance Impact of Stator Tilting Endwall Designs on a Mixed Flow Turbine
Bibliographic record
Abstract
This paper numerically investigates stator endwall designs for a mixed flow turbine. One key design parameter studied is the tilting angle of the stator endwall. By examining stator designs with different tilting angles, the aim of this paper is to improve the efficiency of the studied mixed flow turbine at low velocity ratio working conditions. The performance curve at the design speed was chosen for the comparison between the baseline design and the tilted endwall designs. First, the numerical predictions for the baseline design were validated with experimental data. Then, to understand the mechanism of the performance variation between the different designs, the internal flow field was analyzed in detail. It was found that the tilting stator endwall could form a geometric “kink” in the endwall profiles. On the shroud side, certain designs with such kink caused local flow separations upstream the rotor leading edge. This separation could have the effect of reducing the intensity of the tip leakage vortex and the exit kinetic energy losses at the rotor outlet and may also improve the performance of the exhaust diffuser. As a result, the peak of the efficiency curve shifted toward lower velocity ratio. If the turbine stage incorporated a downstream exhaust diffuser, the optimal design in this study showed a shift of the velocity ratio of the peak efficiency point from 0.62 to 0.60 compared with the baseline. The maximum efficiency improvement was 1.3% points, which occurred at low velocity ratio. Meanwhile, the peak efficiency was 0.2% points higher than the baseline. If the exhaust diffuser was removed, a similar shift of the efficiency curve was observed but less efficiency gain was achieved at the low velocity ratio condition. A preliminary unsteady simulation was also conducted for the optimal design in this study.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".