An implicit large-eddy simulation study of the turbulent Taylor-Couette flow with an inner rotating cylinder
Bibliographic record
Abstract
The Taylor-Couette flow case is a turbulent benchmark that assesses the capability of numerical methods to simulate problems with curved boundaries and boundary layers. In this study, we consider the case with a rotating inner cylinder and a stationary outer cylinder at a Reynolds number Re=4000. This allows us to assess the accuracy of our continuous Galerkin finite element solver, which uses an implicit Large-Eddy Simulation (LES) approach and SUPG/PSPG stabilisation techniques. We perform numerical experiments using different polynomial orders p = 1, 2, 3 with up to 6M cells and 716M degrees of freedom. We compare enstrophy and kinetic energy profiles, along with vorticity and Q criterion distributions. Moreover, we compute the numerical dissipation of the implicit LES approach using the energy equation. The results show that the prediction of enstrophy is more accurate with increasing order p and refinement of the mesh. The energy balance analysis allows us to show that high-order elements can obtain less numerical dissipation with a lower number of degrees of freedom and with fewer computational resources. The work raises the question of what is an acceptable amount of numerical dissipation and provides valuable data for future users of this benchmark.
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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 | 0.002 |
| 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 | 0.001 |
| 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".