Mixed subgrid-scale models in generalized curvilinear coordinates for large-eddy simulations of heterogeneous turbulent flows
Bibliographic record
Abstract
Atmospheric boundary layer (ABL) flows govern surface weather patterns, wind energy forecasting, and urban airflow modeling, making them critical to a wide range of meteorological, engineering and environmental applications. Many ABL flows are characterized by high levels of flow heterogeneity and turbulence anisotropy due to the complex shape of the local terrain, making analysis via large eddy simulation challenging. This study evaluates mixed subgrid-scale models and higher-order numerical schemes formulated in generalized curvilinear coordinates (GCC) aimed at simulating turbulent flows in heterogeneous conditions. The anisotropic minimum-dissipation model, its mixed-model variant, the Bardina-anisotropic minimum dissipation (BAMD) model, and the baseline Bardina–Vreman (BV) model are formulated within GCC, which provides enhanced geometrical flexibility, enabling higher numerical accuracy and stability for resolving heterogeneous turbulent flows compared to traditional Cartesian grids. The mixed formulations combine the dissipative feature of functional subgrid closures with the structural accuracy of scale-similarity-based models. The mixed models are compared with the Lagrangian scale-dependent and localized dynamic Smagorinsky subgrid-scale models using five different convection schemes: second-order central difference, fourth-order central difference, fourth-order central difference with hyper-viscosity (CD4H), third-order upwind-biased, and QUICK. Simulations are conducted for the classical Taylor–Green vortex case, turbulent channel flow at frictional Reynolds number ( Re τ ) = 395, and a neutral atmospheric boundary layer over heterogeneous terrain. Results consisting of first-, second-, and third-order moments are presented alongside joint probability density functions of the resolved velocity gradient tensor and barycentric maps representing turbulence anisotropy. Among all tested combinations, the BAMD model coupled with the CD4H scheme shows the best balance between accuracy and efficiency, highlighting the effectiveness of combining mixed subgrid-scale models and high-order convective schemes with the geometrical flexibility of finite-volume methods constructed in generalized curvilinear coordinates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".