Evaluation of turbulence models on roughened turbine blades
Bibliographic record
Abstract
The accuracy of turbulence models for the Reynolds-Averaged Navier-Stokes (RANS) equations in rough-wall flows is evaluated by comparing the model predictions with the data obtained from large-eddy simulations (LES). We have considered boundary layers in favourable and adverse pressure gradients mimicking those encountered in hydroturbines. We find that some features of the flow cannot be captured accurately by any model, due to the fundamental modelling assumptions. An example is the flow reversal that occurs in the roughness sublayer prior to separation, which cannot be predicted by the commonly used approaches, which bypass the roughness sublayer while modifying the boundary conditions. In mild pressure gradients most models are sufficiently accurate for engineering applications, but if strong favourable or adverse pressure gradients are applied (especially those leading to separation) the model performance rapidly degrades. A particularly difficult problem (both for rough- and smooth-wall cases) is the return to equilibrium after a strong perturbation, a known limitation of RANS models. Simulations of real configurations using commercial codes are also considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".