Wind Farm Layout Optimization in Complex Terrains Using Computational Fluid Dynamics
Bibliographic record
Abstract
The aim of wind farm design is to maximize energy production and minimize cost. In particular, optimizing the placement of turbines in a wind farm is crucial to minimize the wake effects that impact energy production. Most work on wind farm layout optimization has focused on flat terrains and spatially uniform wind regimes. In complex terrains, however, the lack of accurate analytical wake models makes it difficult to evaluate the performance of layouts quickly and accurately as needed for optimization purposes. This paper proposes an algorithm that couples computational fluid dynamics (CFD) with mixed-integer programming (MIP) to optimize layouts in complex terrains. High-fidelity CFD simulations of wake propagation are utilized in the proposed algorithm to constantly improve the accuracy of the predicted wake effects from upstream turbines in complex terrains. By exploiting the deterministic nature of MIP layout solutions, the number of expensive CFD simulations can be reduced significantly. The proposed algorithm is demonstrated on the layout design of a wind farm domain in Carleton-sur-Mer, Quebec, Canada. Results show that the algorithm is capable of producing good wind farm layouts in complex terrains while minimizing the number of computationally expensive wake simulations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".