Numerical investigation of turbulent shear flows using production‐limited delayed detached‐eddy simulation
Bibliographic record
Abstract
Abstract In chemical engineering, turbulent shear flows are often encountered. The grid induced separation (GIS) and the slow RANS‐LES transition issues should be alleviated when the delayed detached‐eddy simulation (DDES) is used to simulate turbulent shear flows. This paper studies the performance of the production‐limited DDES (PL‐DDES) model in improving the GIS and the slow RANS‐LES transition issues. Since the simplified IDDES (S‐IDDES) model is proposed to improve the GIS issue, the S‐IDDES model is chosen as the model for comparison. The simulation results show that the PL‐DDES model with constant C d1 = 14 alleviates the GIS issue better than the S‐IDDES model and the PL‐DDES model with C d1 = 8. The results of the free shear layer show that the PL‐DDES model can switch RANS to LES more rapidly and unlock the Kelvin‐Helmholtz instability more effectively than the S‐DDES model. For the backward‐facing step flow, the S‐IDDES model performs poorly when unlocking the Kelvin‐Helmholtz instability in the separation zone. On the other hand, the PL‐DDES model has a rapid RANS‐LES transition after the step and produces a significant transport of momentum in the shear layer, leading to reasonable separation distance and flow structures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".