Self-Adaptive Upwinding for Large Eddy Simulation of Turbulent Flows on Unstructured Elements
Bibliographic record
Abstract
A self-adaptive upwinding method for Large Eddy Simulation (LES) is proposed to reduce the numerical dissipation of a low order numerical scheme on unstructured elements.This method is used to extend an existing Reynolds-averaged Navier-Stokes (RANS) code to an LES code by adjusting the contribution of the upwinding term to the convective flux.This adjustment is essentially controlled by the intensity of the local wiggle and reduces the upwind contribution in Roe MUSCL scheme.First, the stability characteristic of the new scheme is studied, using a channel flow stability test.It is essential to ensure that the proposed scheme is able to adjust upwinding in the presence of very high gradients and prohibits the divergence of the simulation.Second, the decaying isotropic turbulence is simulated in order to study the capability of the new scheme in generating the suitable decaying rate for the total kinetic energy and also its influence over the slope of energy spectrum at different computational times.Finally, the flow separation phenomena over a NACA0025 profile is numerically investigated and results are compared with experimental data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".