Aerothermal shape optimization for a double row of discrete film cooling holes on the suction surface of a turbine vane
Why this work is in the frame
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Bibliographic record
Abstract
A multiple-objective optimization is implemented for a double row of staggered film holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance, which is assessed using the cooling effectiveness, while minimizing the corresponding aerodynamic loss, which is measured with a mass-averaged total pressure coefficient. Three geometric variables defining the hole shape are optimized: the conical expansion angle, compound angle and length to diameter ratio of the non-diffused portion of the hole. The optimization employs a non-dominated sorting genetic algorithm coupled with an artificial neural network to generate the Pareto front. Reynolds-averaged Navier–Stokes simulations are employed to construct the neural network and investigate the aerodynamic and thermal optimum solutions. The optimum designs exhibit improved performance in comparison to the reference design. The optimization methodology allowed investigation into the impact of varying the geometric variables on the cooling effectiveness and the aerodynamic loss.
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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 | 0.000 | 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) | 0.000 | 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