Single and Multipoint Shape Optimization of Gas Turbine Blade Cascades
Bibliographic record
Abstract
*† A multipoint shape optimization method for the aerodynamic performance of gas turbine blade cascades is presented and is applied to the design of a transonic and a subsonic compressor rotor. The optimization method uses a Genetic Algorithm (GA), which is combined with an Artificial Neural Network (ANN) that uses a back propagation algorithm, so as to design two-dimensional gas turbine cascades. The ANN is used to build a low fidelity model that approximates the optimization objective and constraints. The latter are to achieve a better aerodynamic performance over the full operating range of gas turbine cascades by varying the blade profile, which is described by the blade camber line and thickness distribution. The blade geometry is parameterized using a Non-Rational B-Splines (NURBS) representation. To reduce computation time the optimization scheme was parallelized on an SGI 2000 computer using Message Passing Interface (MPI). The cascade aerodynamic performance, which is used in computing the objective function and in training/testing the ANN, is determined by solving the two-dimensional Reynolds-Averaged Navier-Stokes equations using a cell-vertex finite volume method on an unstructured triangular mesh and turbulence is modeled using the Baldwin-Lomax model. The chosen objective function and optimization methodology results in a significant improvement in terms of efficiency and pressure ratio, and the use of ANN results in a ten-fold speed-up of the design process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".