Data-driven surrogate model for wind turbine damage equivalent load
Bibliographic record
Abstract
Abstract. Aeroelastic simulations are used to assess wind turbines in accordance with IEC standards in the time domain. Doing so can calculate fatigue and extreme loads on the wind turbine's components. These simulations are conducted for several reasons, such as reducing safety margins in wind turbine component design by covering a wide range of uncertainties in wind and wave conditions, and meeting the requirements of the digital twin, which needs a thorough set of simulations for calibration. Thus, it's essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. We suggest a data-driven approach to build surrogate models for the Damage Equivalent Load (DEL) based on aeroelastic simulation outputs to tackle this challenge. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need of time-consuming aeroelastic simulations. Our study will focus on utilizing a sequential machine-learning method to map wind speed time series to DEL. Furthermore, we demonstrate the versatility of the developed and trained surrogate models by testing them for a wind turbine in the wake and using transfer learning to enhance their prediction.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".