On the evaluation of wind loads for wind turbines' foundation design: Experimental and numerical investigations
Bibliographic record
Abstract
In the past two decades, wind farms have been enjoying renewed interest as means for clean and renewable energy production. Larger and taller wind turbines are used for harvesting wind energy. In this paper, a boundary-layer wind tunnel experiment was carried out on a model of the 5-MW National Renewable Energy Laboratory (NREL) wind turbine, to evaluate overall wind-induced base loadings in a parked condition. While mean and background base loadings were measured experimentally, a posttest dynamic analysis framework is developed to assess inertial loads analytically. The analytical analysis is carried out under both rigid and flexible tower-foundation assumptions. Whenever applicable, the wind tunnel measurements are compared with NREL results, which were obtained by using the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) software. The comparison shows a good agreement between the proposed approach and the available FAST results. In addition, the study indicates that the flexibility of the foundation may result in a reduced overall wind loads, due to base isolation effects. However, the assumption of a rigid foundation results in a slightly conservative base loads. This said, depending on the available foundation system, the methodology followed in the current paper remains in force and the base stiffness can be updated to permit the estimation of actual foundation loadings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".