Hybrid turbulence models for atmospheric flow: A proper comparison with RANS models
Bibliographic record
Abstract
A compromise between the required accuracy and the need for affordable simulations for the wind industry might be achieved with the use of hybrid turbulence models. Detached-Eddy Simulation (DES) [1] is a hybrid technique that yields accurate results only if it is used according to its original formulation [2]. Due to its particular characteristics (i.e., the type of mesh required), the modeling of the atmospheric flow might always fall outside the original scope of DES. An enhanced version of DES called Simplify Improved Delayed Detached-Eddy Simulation (SIDDES) [3] can overcome this and other disadvantages of DES. In this work the neutrally stratified atmospheric flow over a flat terrain with homogeneous roughness will be analyzed using a Reynolds-Averaged Navier–Stokes (RANS) model called k – ω SST (shear stress transport) [4], and the hybrids k – ω SST-DES and k – ω SST-SIDDES models. An obvious test is to validate these hybrid approaches and asses their advantages and disadvantages over the pure RANS model. However, for several reasons the technique to drive the atmospheric flow is generally different for RANS and LES or hybrid models. The flow in a RANS simulation is usually driven by a constant shear stress imposed at the top boundary [5], therefore modeling only the atmospheric surface layer. On the contrary the LES and hybrid simulations are usually driven by a constant pressure gradient, thus a whole atmospheric boundary layer is simulated. Rigorously, this represents two different simulated cases making the model comparison not trivial. Nevertheless, both atmospheric flow cases are studied with the mentioned models. The results prove that a simple comparison of the time average turbulent quantities obtained by RANS and hybrid simulations is not easily achieved. The RANS simulations yield consistent results for the atmospheric surface layer case, while the hybrid model results are not correct. As for the atmospheric boundary layer case, no meaningful conclusion could be established for RANS, and the DES results are not satisfactory. However the SIDDES model is capable of reproducing accurately the atmospheric boundary layer over flat terrain.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".