Multi-Scale Numerical Simulation and Optimization Strategies for Wind Farm Layouts in High-Altitude Regions
Bibliographic record
Abstract
This study explores the optimization of wind farm layouts in high-altitude regions using a multi-scale numerical simulation approach integrated with advanced optimization strategies. Data were collected from various wind farms in the Tibetan Plateau and the Himalayan region, including wind speed, direction, air density, temperature, and terrain elevation over a five-year period. The research methodology comprised data preprocessing, wind flow modeling via Computational Fluid Dynamics (CFD) and the turbulence model, wind turbine performance modeling based on the Betz limit and Jensen wake model, and optimization using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The simulated results were validated against actual data through Root Mean Square Error (RMSE) and sensitivity analysis. The findings reveal substantial enhancements in wind farm performance, with optimized layouts significantly increasing total power output and reducing turbine interference. Specifically, the GA-optimized layout achieved a total power output of 102 MW and an efficiency of 82%, while the PSO-optimized layout attained 101.5 MW and 81.5% efficiency, compared to the initial layout’s 95 MW and 75% efficiency. This research highlights the potential of multi-scale simulations and optimization techniques to improve wind farm efficiency in challenging high-altitude environments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".