Transition-Flow-Occurrence Estimation: A New Method
Bibliographic record
Abstract
There is a current need for a simple and effective way for determining the transition onset and transition extent on a solid surface in a general CFD solver, in order to include the transition effects in the aerodynamic coefficients calculation. The present paper propose a new method for estimating the transition onset and extent based on the temporal variation of the skin friction coefficient and the flow vorticity at the wall. The method consists in two steps: in the first step an unsteady Navier-Stokes simulation is performed around the aerodynamical configuration, in this step the skin friction and vorticity at the wall are recorded, this two parameters are used for determining the transition onset and transition length. In a second step of the method the computational grid is split in three regions: a laminar, a transition and a turbulent region; and a new simulation is done on this zonal mesh using the Reynolds Averaged Navier-Stokes. In order to coupling the transition region with the laminar and turbulent regions a linear intermittency function is used that multiplies the turbulent part of the viscosity. The intermittency function is zero in the laminar region and one in the turbulent region. The presented method is further validated with experimental data from literature; it is shown that the relative error in the drag coefficient calculated with the present method is lower than 8%, when a fully-turbulent simulation can introduce errors up to 50%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".