Feature Based and Goal Oriented Mesh Adaptation for High Pressure Turbines Applications
Bibliographic record
Abstract
This paper shows the benefits of anisotropic mesh adaptation on aerothermic performances prediction of a complex component: a film cooled blade of a turbine stage. The inlet high pressure turbine temperature is increasing year after year and nowadays it is more important than ever to predict correctly the heat flux in order to forecast the life-cycle of the component. Film cooled turbines geometries are very complex and as a result it is almost impossible to mesh them with classical multi-block hexahedral meshing methods. For this reason, flexible tetrahedral meshes are more suitable. Discretization error (along with geometric error and modeling error) strongly impacts the evaluation of turbine performances. This leads to inaccurate prediction of aerothermic coefficients, if the error is not properly controlled. The legacy simulation process relies heavily on a priori knowledge of the physics (position of coherent features of the flow-field), resulting in handly crafted meshes and not automatized working chain. By exploiting the flexibility of the tetrahedral elements and containing the discretization error, metric-based anisotropic mesh adaptation appears to be a suitable technology. In this work, two different strategies of error estimate will be considered: feature-based and goal-oriented. The former relies on the standard multiscale $L^p$ interpolation error for a given feature. The latter controls the error on an engineering functional, which requires the computation of the adjoint state. Goal-oriented mesh adaptation, using the heat flux as functional, exhibits the same level of accuracy as the feature-based approach, with fewer number of points. The impact of the discretization error on aerothermic performances prediction will be discussed.
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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".