Effects of Subgrid Scale Modeling on the Deterministic and Stochastic Turbulent Energetic Distribution in Large-Eddy Simulations of a High-Pressure Turbine Stage
Bibliographic record
Abstract
This study focuses on the engine-representative MT1 transonic high-pressure turbine. Simulated by use of wall-modeled large-eddy simulations (LES) with three different subgrid scale (SGS) closures, mean pressure profiles across the blades as well as mean radial profiles at the rotor exit are found to be in good agreement with experimental data with only local differences between models. Unsteady flow features, inherently present in LES, are however affected by SGS modeling. This is evidenced by the relative energetic content of the deterministic to stochastic turbulent contributions evaluated, thanks to the triple decomposition analysis of the simulations. Origins of such differences are found to impact the entire radial distribution of the flow and activity, with deterministic and chaotic contributions distributed differently depending on the SGS model and reference frequency used to extract the deterministic signal. Such flow responses can be attributed to the different SGS capacities to satisfy basic turbulent flow features that translate in different dissipative and turbulent diffusive contributions of the three SGS models.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".