Numerical Assessment of Turbulent Models at a Critical Regime on Unstructured Meshes
Bibliographic record
Abstract
The main objective of the present study is to investigate the performance of different turbulent models for the flow simulation around a circular cylinder at a critical Reynolds number regime (Re = 8.5×105, Tu = 0.7%). To simulate the various flow features such as laminar-turbulent transition inside the boundary layer and the unsteady vortex shedding in the wake region, a hybrid RANS/LES model (SAS model) and a correlation-based transition model (γ - Reθ model) were used and the feasibilities of them for the flow simulation at a critical Reynolds number regime were demonstrated. A vertex-centered finite-volume method was adopted to discretize the incompressible Navier-Stokes equations and an unstructured mesh technique was used to discretize the computational domain. The inviscid fluxes were evaluated by using 2nd-order Roe’s FDS and the viscous fluxes were computed based on central differencing. A dual-time stepping method and the Gauss-Seidel iteration were used for unsteady time integration. To reduce the computational costs, the parallelization strategy using METIS and MPI libraries was adopted. The unsteady characteristics and time-averaged quantities of the flow fields were compared between the turbulent models. The numerical results have been also compared with experimental data. At the critical regime, turbulent models have showed quite different results due to the different abilities of each model to predict various flow features such as laminar-turbulent transition, unsteady vortex shedding.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".