Data-driven surrogate model for wind turbine damage equivalent load
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Aeroelastic simulations are employed to assess wind turbines in accordance with IEC standards in the time domain. These analyses enable the evaluation of fatigue and extreme loads experienced by wind turbine components. Such simulations are essential for several reasons, including but not limited to reducing safety margins in wind turbine component design by accounting for a wide range of uncertainties in wind and wave conditions and fulfilling the requirements of the digital twin, which necessitates a comprehensive set of simulations for calibration. Thus, it is essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. To address this challenge, we propose a data-driven approach to construct surrogate models for the damage equivalent load (DEL) based on aeroelastic simulation outputs. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need for time-consuming aeroelastic simulations. Our study focuses on utilizing a sequential machine learning (ML) method to map wind speed time series to DEL. Additionally, we demonstrate the versatility of the developed and trained surrogate models by testing them on a wind turbine in the wake and applying transfer learning to enhance their predictive accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it