Geometric Optimization of a Gas Turbine Blade Cooling Passage using CFD
Bibliographic record
Abstract
This work focuses on the rib-turbulated cooling which is a category of impingement cooling and aims at optimizing the geometry of rib-roughened cooling passage of a gas turbine blade. CFD analysis is carried out using Ansys/Fluent to solve the steady RANS equations. Computational domain consists of a long rectangular channel with the length of the channel being 9 times its height. Ratios of rib width, rib height and rib pitch to hydraulic diameter of the channel are taken as 0.1, 0.1 and 1.2, respectively. Numerical simulations are performed to analyze the performance of various rib shapes for Reynolds number, based upon the hydraulic diameter, in a range of 5000 to 50,000. Uniform heat flux of 800 W/m2 is applied to the ribbed wall. Incompressible air is used as the cooling fluid. Turbulent flow conditions are applied to the channel geometry with k-ω turbulence model. The effect of rib cross-section and rib pitch to rib width ratio on the heat transfer and friction factor is observed. The 2D CFD analysis revealed that the presence of ribs has significant effect on thermo-hydraulic performance of the cooling channel. Introducing square ribs in a smooth channel caused the Nusselt number to increase by two-folds. The highest value of Nusselt number was achieved by incorporating right-angle triangular ribs which caused the Nusselt number to increase by further 8%, as compared to the square ribs, and an increase in friction factor of 2.5%. The lowest value of friction factor was observed in semicircular ribs (2.95% less than the square ribs), however, the Nusselt number also decreased by 1.5%, as compared to square ribs. Decreasing rib pitch to rib width ratio increased both the Nusselt number and friction factor for all the cases. For square ribs, decreasing this ratio from 15 to 9 resulted in the rise of Nusselt number by 50% and increase in friction factor by 54%.
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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".