Comparison of Large Eddy Simulation Methods for Flows Over Gas Turbine Blades
Bibliographic record
Abstract
In recent years there has been a considerable effort toward applying large eddy simulation methods (LES) to real industrial problems. However, there are still several challenges to be addressed to achieve a reliable LES solution, especially in the context of compressible flows. Furthermore, complex geometries require the unstructured meshes which then interdict the use of very high order schemes. Therefore, LES models are mainly derived and tested on classical problem of simple geometry for incompressible flow and based on higher order schemes. Here, the flow over a gas turbine blade at high Reynolds and Mach numbers is investigated using a mixed finite-volume-finite-element method. Implicit LES method (ILES) as well as Smagorinsky and its dynamic version have been studied. Different variations of the Smagorinsky method have been examined too. The interaction of the numerical dissipation of the scheme with LES models has been explored. The results show the capability of the ILES to take into account the effective viscosity of the flow and the negligible difference of the different LES models in this flow condition. Fairly good agreement with experimental data is found which is superior to RANS results. It is found that there are still some challenges in industrial LES applications which have to be addressed to lead to a better agreement with experimental data.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".