Load Distribution Optimization Method for Fatigue Test of Full-scale Structure of Biaxial Resonant Wind Turbine Blade
Bibliographic record
Abstract
Wind turbine blade is the key component of wind turbine to realize wind energy capture, so it is necessary to verify the rationality of blade structure design through full-scale structural fatigue test. In order to improve the test accuracy and efficiency, this paper proposes to establish the dynamic analysis model of the multi-degree of freedom system of the tested blade by using the finite beam element to realize the calculation method of the load amplitude of the biaxial fatigue test and integrate the dynamic analysis model and the target load calculation method into the particle swarm optimization algorithm to realize the optimization of the load distribution of the biaxial resonant full-size structure fatigue test. Through the design of a biaxial resonant loading scheme of 2.5MW-52.5m wind turbine blades, the results show that this method can quickly and accurately adjust the optimization parameters such as the position of the exciter and the motion quality of the exciter in the flapwise and edgewise on the premise that the fatigue cumulative damage caused by the test load is not less than the cumulative damage of the target load.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".