Hydrodynamic Flexible Spindle (HydroFlex) Polishing of turbine blade internal cooling channels for oxide removal
Bibliographic record
Abstract
Internal cooling channels are essential to turbine blades for high efficiency power generation. Effective removal of aluminum oxide build-up in turbine blade cooling channels is of critical importance to refurbishment and prolonged service life of turbine blades. Conventional internal polishing processes, including abrasive flow machining, chemical polishing, and electrical discharge machining cannot effectively remove the oxide layer within the internal cooling channels due to the complex geometry with high aspect ratio and diameter variation and the electric insulation of the oxide layer. In this case study, we investigated the application of a novel hydrodynamic flexible-spindle (HydroFlex) polishing process to remove the oxide layer within the internal cooling channels of an Inconel 738 turbine blade that was taken out of serve due to oxide build-up. For a 350 mm long cooling channel featured with an inner diameter transition from ϕ4 mm to ϕ2.5 mm, within 12 min, at the grinding wheel rotational speed of 50,000 rpm and 30,000 rpm, HydroFlex was able to completely remove the 14.31 µm thick oxide layer off from the wall of the turbine blade internal cooling channel, improve the channel circularity by 54.7 %, and decrease the channel surface roughness by up to 64.3 %. The results demonstrated the effectiveness of HydroFlex in polishing complex internal cooling channels of turbine blades for oxide removal and potential blade service life extension.
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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 | 0.000 | 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) | 0.001 | 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".