Turbullence modelling of the atmospheric boundary layer over complex topography
Bibliographic record
Abstract
Nowadays, the wind energy industry employs different types of turbulence models which are capable of reproducing the correct and realistic behaviour of relatively simple flows (e.g. Wind over flat, homogeneous and obstacle free terrain). However, as the complexity of the flow increases (e.g. wind over complex topography), the accuracy of the turbulence models may be greatly reduced, and in general, their computational cost rises significantly. Accurate and reliable flow simulations are still not practical for wind industry applications over complex terrain. \n \nTo improve wind flow simulations over complex terrain, two of the main challenges that the wind energy sector faces are addressed. The first challenge is related to the fact that ground surface modelling treatments are valid only on flat terrain. Nevertheless, it is a common practice to use those surface treatments on simulations over complex terrain. However, the k − ω SST (shear stress transport) turbulence model has a novel surface treatment that is less dépendent on flat terrain assumptions. The second challenge is the high computational cost when accuracy and reliable turbulence statistics are needed. Nonetheless hybrid turbulence models could provide a good compromise between accuracy and computational cost. A turbulence model based on the k − ω SST model and the simplified improved delayed detached-eddy simulation (SIDDES) hybrid technique is proposed to address those needs. \n \nTo validate this model for atmospheric flows, first an extensive analysis of certain canonical flows was carried out. This rigorous validation helped understand the inherent limitations of the turbulence model within the specific numerical framework. Subsequently, computations of the neutrally stratified atmospheric flow over flat homogeneous terrain and then over complex topography were conducted. The results show that the k − ω SST-SIDDES turbulence model is able to predict realistic wind behaviour over flat terrain and more complex cases. The vertical grid refinement in the near-wall region required by this model poses a major challenge for the mesh generator. But despite this limitation, k − ω SST-SIDDES turbulence model proved to be a suitable approach for modelling the wind flow over complex terrain without relying on flat terrain assumptions or requiring substantial computer resources.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".