Prediction of Turbulent Separated Flow in a Turnaround Duct Using Wall Functions and Adaptivity
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Bibliographic record
Abstract
Abstract This paper presents an application of adaptive remeshing to the prediction of turbulent separated flows. The paper shows that the κ - ε model with wall functions can predict separated flows along smooth curved surfaces. Success is achieved if the wall functions exhibit values of y+ close to 30, and if meshes are fine enough to guarantee that wall function boundary conditions are grid converged. Adaptive remeshing proves to be a very cost effective tool in this context. The methodology is demonstrated on a problem possessing a closed form solution to establish the performance and reliability of the proposed approach. The method is then applied to prediction of turbulent flow in an annular, axisymmetric turnaround duct (TAD). Predictions from two computational models of the TAD are compared with experimental measurements. The importance of appropriate meshes to achieve grid independent solutions is demonstrated in both cases. Better agreement with measurements is obtained when partially developed profiles of u, κ, and ε are specified at the TAD inlet.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it