Aerodynamic Shape Optimization of Turbine Blades Using a Design-Parameter-Based Shape Representation
Bibliographic record
Abstract
Currently, most shape optimization activities for 2D blade sections focus on modifying the blade shape locally to get an optimum one, which implicitly assumes that the global shape is near optimum. Moreover, the common design parameters in most cases are not the variables used in shape optimization, hence the designer does not have control over the parameters that he or she uses in the design. In this work, the turbine blade shape at any given radial location, is represented with the MRATD model (Modified Rapid Axial Turbine Design), which is a low-order representation that describes the blade profile using a maximum of 17 aerodynamic design parameters that are given (and used) by the turbine designer, e.g. the blade axial chord, stagger, maximum thickness, throat, uncovered turning, inlet and exit blade and wedge angles, LE and TE radii etc... This representation is used in an optimization scheme to sweep the design space and identify the design parameters that would accomplish a certain optimization objective (e.g. maximum adiabatic efficiency) subject to some constraints (e.g. fixed throat area or minimum TE radius or maximum TE wedge angle or metal angles etc...). The optimization scheme uses evolutionary optimization algorithm, Genetic Algorithm(GA) and, to save computing time, Artificial Neural Network (ANN) is introduced to approximate the optimization objectives and constraints; it is trained and tested using a relatively small number of high fidelity CFD flow simulations. This approach to geometry representation is used to carry out a sensitivity study of the effect of the different design parameters on the blade performance of a highly efficient subsonic turbine blade. Its impact on the design process is also demonstrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".