Large-eddy simulation of wind-turbine wakes over two-dimensional hills
Bibliographic record
Abstract
Wind-turbine wakes over two-dimensional (2D) hills with different slope gradients are systematically investigated using large-eddy simulation with wind turbine parameterized as actuator disk model and hilly terrain modeled by immersed boundary method. The chosen hill models represent typical hilly terrains with and without flow recirculation in the wake of the hills. The flow characteristics of wind-turbine wakes [including mean velocity, wake-center trajectory, turbulence statistics, and mean kinetic energy (MKE) budgets] and the power performance are analyzed, and the related flow mechanisms are elucidated in our study. It is found that the velocity deficit in turbine wakes cannot be acceptably represented by the Gaussian model in the wake of the steep hill until at a further distance. It is also found that the assumption that the wake-center trajectory maintains a nearly constant elevation downwind of the hilltop proposed by Shamsoddin and Porté-Agel [“Wind turbine wakes over hills,” J. Fluid Mech. 855, 671–702 (2018)] may not be applicable in particular for the steep hill cases. Furthermore, the hilltop is the optimal location for turbine placement because the turbine harvests more wind energy due to the speed-up effect and suffers less fatigue loading due to the lower turbulence levels. Both the turbulence levels and the magnitude of vertical turbulent flux are found to drop below those of the flat ground case on the windward side of the hills, and they also decrease within the hill wake region compared with the no-turbine cases. A detailed analysis of MKE budgets reveals that the budgets of pressure transport and mean convection are mainly responsible for balancing the MKE in turbine wakes over hilly terrain.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".