Comparison between the two-equation turbulence models of Jones and Launder and of Wilcox and Rubesin applied to aerospace problems
Bibliographic record
Abstract
In the present work, the Van Leer flux vector splitting scheme is implemented on a finite-volume context. The two-dimensional Favre-averaged Navier-Stokes equations are solved using an upwind discretization on a structured mesh. The Jones and Launder and the Wilcox and Rubesin two-equation models are used in order to close the problem. The physical problems under studies are the low supersonic flow along a ramp and the moderate supersonic flow around a blunt body configuration. The implemented scheme uses a MUSCL (Monotone Upstream-centered Schemes for Conservation Laws) procedure to reach second order accuracy in space. The time integration uses a Runge-Kutta method of five stages and is second order accurate. The algorithm is accelerated to the steady state solution using a spatially variable time step. This technique has proved excellent gains in terms of convergence rate as reported in Maciel. The results have demonstrated that the Wilcox and Rubesin model has yielded more critical pressure fields than the ones due to Jones and Launder. The shock angle of the oblique shock wave in the ramp problem and the stagnation pressure ahead of the blunt body configuration are better predicted by the Wilcox and Rubesin turbulence model. Key words: Van Leer algorithm, Jones and Launder turbulence model, Wilcox and Rubesin turbulence model, finite volumes and structured discretization, Navier-Stokes equations.
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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 | 0.000 | 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) | 0.001 | 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".