Toward Scale-Adaptive Subgrid-Scale Model in LES for Turbulent Flow Past a Sphere
Bibliographic record
Abstract
This study explores the dynamics of turbulent flow around a sphere at a Reynolds number of Re=103 using large-eddy simulation, focusing on the intricate connection between vortices and strain within the recirculation bubble of the wake. Employing a relatively new subgrid-scale modeling approach based on scale adaptivity, this research implements a functional relation to compute ksgs that encompasses both vortex-stretching and strain rate mechanisms essential for the energy cascade process. The effectiveness of this approach is analyzed in the wake of the sphere, particularly in the recirculation bubble, at the specified Reynolds number. It is also evaluated in comparison with two different subgrid-scale models through detailed analysis of the coherent structures within the recirculation bubble. These models—scale-adaptive, k-Equation, and dynamic k-Equation—are assessed for their ability to capture the complex flow dynamics near the wake. The findings indicate that while all models proficiently simulate key turbulent wake features such as vortex formation and kinetic energy distribution, they exhibit unique strengths and limitations in depicting specific flow characteristics. The scale-adaptive model shows a good ability to dynamically adjust to local flow conditions, thereby enhancing the representation of turbulent structures and eddy viscosity. Similarly, the dKE model exhibits advantages in energy dissipation and vortex dynamics due to its capability to adjust coefficients dynamically based on local conditions. The comparative analysis and statistical evaluation of vortex stretching and strain across models deepen the understanding of turbulence asymmetries and intensities, providing crucial insights for advancing aerodynamic design and analysis in various engineering fields and laying the groundwork for further sophisticated turbulence modeling explorations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".