Blade Air Cooling Feed System CFD Analysis and Validation
Bibliographic record
Abstract
A cold flow static rig test and computational fluid dynamics (CFD) analyses have been performed to verify the benefits of a modified “deflector” design to the blade air cooling feed system. The area of interest is the broach passage which is a space between the bottom of the blade fixing and the blade disk. Cooling flow enters into this region from the disk cavity and then exits upward into the blade internal cooling passages. Steady Navier-Stokes analyses of the cold flow static rig geometries were performed using the in-house code NS3D and the commercial code CFX v5.6. Due to the setup of the experimental rig, the numerical domain model with appropriate boundary conditions had to be carefully selected. Unstructured grids including wall prism layers combined with mesh adaptation were employed for both CFD codes on the baseline geometry. The commercial adaptation code OPTIMESH was used for NS3D while CFX employed its internal grid adaptation tool. No grid adaptation was necessary for the modified geometry. The pressure and mass flow measurements for the baseline and modified blade broach geometries demonstrated a reduction in losses for the new design. The CFD flow visualization showed the presence of a strong vortex in the broach for the baseline case with an accompanying low pressure zone. The modified broach design deflects the flow thus avoiding the formation of the strong vortex and low pressure zone. The CFD pressure distributions predicted the trends qualitatively. Quantitative comparisons of the CFD pressure distributions along broach walls were reasonable for the new design. For the baseline geometry, the CFD pressure drop is higher than measurements indicate. Modeling issues associated with a screen in the rig set-up is thought to be the source of the difference.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".