A NOVEL DYNAMIC FORCING SCHEME INCORPORATING BACKSCATTER FOR HYBRID RANS/LES
Bibliographic record
Abstract
In hybrid RANS/LES, Reynolds-averaged Navier-Stokes (RANS) equations method is used to treat the near-wall region and large-eddy simulation (LES) is applied to the core turbulent region. Owing to the incompatibility of these two numerical modelling approaches, an artificial (i.e., non-physical) buffer layer forms along the interface where the model switches from RANS to LES. In this thesis, a novel dynamic forcing scheme incorporating backscatter is proposed in order to remove the artificial buffer layer. In contrast to previous forcing techniques, the proposed forcing is determined dynamically from the flow field itself, and does not require any extraction of turbulent fields from reference direct numerical simulation (DNS) or high-resolution LES databases. The proposed forcing model has been tested on three types of wall-bounded turbulent flows, namely, turbulent flow in a plane channel; turbulent flow in a spanwise rotating channel; and turbulent flow in a spanwise rotating rib-roughened channel. In order to validate the present hybrid approach, turbulence statistics obtained from hybrid RANS/LES simulations are thoroughly compared with the available DNS results and laboratory measurement data. Based on the study of a plane channel flow, transport equations for the resolved turbulent stresses and kinetic energy are introduced to investigate the effects of dynamic forcing on reduction of the thickness and impact of the artificial buffer layer. As long as the dynamic forcing is in use, the artificial buffer layer have been successfully removed, indicating that the proposed hybrid approach is insensitive to the choices of the forcing region or interface location. The predictive performance of the dynamic forcing scheme has been further evaluated by considering turbulent flows subjected to a special type of body force, i.e., the non-inertial and non-conservative Coriolis force. Due to the effects of system rotation, turbulence level is enhanced on the pressure side and suppressed on the suction side of the rotating channel. Furthermore, it is reported in this thesis that the classification of the roughness type now relies not only on the pitch ratio, but also on the rotation number in the context of rotating rib-roughened flows.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".