Application of Nonaxisymmetric Endwall Contouring to Conventional and High-Lift Turbine Airfoils
Bibliographic record
Abstract
Here, we report on the application of nonaxisymmetric endwall contouring to mitigate the endwall losses of one conventional and two high-lift low-pressure turbine airfoil designs. The design methodology presented combines a gradient-based optimization algorithm with a three-dimensional computational fluid dynamics (CFD) flow solver to systematically vary a free-form parameterization of the endwall. The ability of the CFD solver employed in this work to predict endwall loss modifications resulting from nonaxisymmetric contouring is demonstrated with previously published data. Based on the validated trend accuracy of the solver for predicting the effects of endwall contouring, the magnitude of predicted viscous losses forms the objective function for the endwall design methodology. This system has subsequently been employed to optimize contours for the conventional-lift Pack B and high-lift Pack D-F and Pack D-A low-pressure turbine airfoil designs. Comparisons between the predicted and measured loss benefits associated with the contouring for Pack D-F design are shown to be in reasonable agreement. Additionally, the predictions and data demonstrate that the Pack D-F endwall contour is effective at reducing losses primarily associated with the passage vortex. However, some deficiencies in predictive capabilities demonstrated here highlight the need for a better understanding of the physics of endwall loss-generation and improved predictive capabilities.
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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 | 0.000 | 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".