Aero-Thermal Shape Optimization for the Discrete Film Cooling of a Turbine Airfoil
Bibliographic record
Abstract
A multiple-objective shape optimization is implemented for two staggered rows of discrete film cooling holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance while minimizing the corresponding aerodynamic penalty. The cooling performance is assessed using the adiabatic film cooling effectiveness, while the aerodynamic penalty is measured with a mass-averaged total pressure loss coefficient. The conical expansion angle, the compound angle, and the length to diameter ratio of the non-expanded portion of the hole are selected as geometric design variables. The effect of varying the geometric variables on the adiabatic film cooling effectiveness and the aerodynamic penalty is analyzed using the optimization method and three-dimensional Reynolds-averaged Navier-Stokes (RANS) simulations. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with an artificial neural network (ANN) to perform the multiple-objective optimization. RANS simulations are employed to construct the ANN network which produces low-fidelity predictions of the objective functions during the optimization. The Pareto front of optimum solutions is generated. Two optimum designs, denoted as the aerodynamic, and thermal optimums are chosen from the Pareto front and evaluated through RANS simulations. The optimum designs present improved performance in comparison to the reference design, which consists of cylindrical holes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".