Bibliographic record
Abstract
A large eddy simulation (LES) code is developed from an existing incompressible Navier-Stokes solver based on the finite element method. Taylor-Hood, [special characters omitted], and Crouzeix-Raviart, [special characters omitted], tetrahedral elements are implemented for the spatial discretization. The time discretization uses a second order accurate, semi-implicit, split time advancement scheme. The semi-implicit scheme employs a backward differentiation time integration scheme for the viscous term and the Adams-Bashforth scheme for the convective term. Convergence results in space and time are reported for the variable viscosity Navier-Stokes solver. The Smagorinsky model is implemented for the sub-grid scale approximation. The resulting algebraic system is solved using a matrix-free conjugate gradient iterative method. Results of isotropic decaying turbulence in a triple periodic cube are compared with direct numerical simulation (DNS) and experimental data. Preliminary conclusions regarding the performance of the element-wise divergence-free Crouzeix-Raviart element compared to the Taylor-Hood element which only assures divergence-free in a weak sense (i.e., over the entire domain) are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".