High-Fidelity Film-Cooled Rotor High Pressure Turbine Simulation using Metric-Based Anisotropic Mesh Adaptation
Bibliographic record
Abstract
This paper shows the ability of metric-based anisotropic mesh adaptation to accurately predict the aerodynamic flow field on a particularly complex test case: a film-cooled rotor high pressure turbine. Film-cooled high pressure turbine blades are very complex turbomachinery components that in recent years results in very complex geometries. In fact increasing temperatures in the combustion chamber results in cooling needs on the blade surface. For this reason, the film cooled turbine are composed of a cooling network and many different-shaped film-cooling holes which makes these geometries extremely complex to mesh with structured standard meshing approaches. Consequently, metric-based anisotropic mesh adaptation seems quite appropriate for such applications thanks to the fact that it provides automation of the whole process and it allows significant gains in terms of simulation accuracy by capturing coherent flow features proper to the turbomachinery physics. The benefits in terms of accuracy and mesh-convergence are demonstrated on a realistic film-cooled rotor high pressure turbine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".