Optimal Geometric Representation of Turbomachinery Cascades Using Nurbs
Why this work is in the frame
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Bibliographic record
Abstract
Simulation-based shape optimization is getting a lot of attention at present. The geometric representation of the shape plays an important role in the optimization process; it affects the number of design variables as well as the smoothness of the final profile. This work is on finding an optimum representation of gas turbine blade profiles in two-dimensions using nonuniform rational B-splines (NURBS). This parameterization involves the solution of an inverse problem for the control points and weights, where the error in the representation is minimized using Simulated Annealing (SA), the latter gives the control points and weights that would approximate a given blade shape with minimum error, yet without loosing its smoothness. The optimum parameterization involves the minimum number of control points, their position and weight for a given error tolerance, and is identified for generic as well as real compressor and turbine blade cascades. The effect of this parameterization on the blades' aerodynamic performance is then discussed.
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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 | 0.001 | 0.002 |
| 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 it