Innovative Large‐Scale Prefabricated Onshore Lattice Wind Turbine Support Structures: Multiparameter Collaborative Optimization and Design Guidelines
Bibliographic record
Abstract
ABSTRACT Innovative large‐scale lattice wind turbine support structures can effectively utilize wind resources in low wind speed areas, promoting sustainable energy development. In the design of large‐scale lattice support structures, the structural layout and geometric parameters of the lattice segment significantly impact structural efficiency. This study proposes innovative upright and conical lattice wind turbine support structures based on a wind power project. A comprehensive and in‐depth exploration of multidisciplinary multiparameter collaborative optimization for proposed structures was conducted, integrating machine learning, numerical simulation, and secondary finite element development. Additionally, a practical design guideline was established. Utilizing multiphysical field coupled load simulation, integrated fatigue assessment methods, and multiparameter collaborative optimization techniques, finite element analysis models were established for 6452 different design parameters. Design constraints included strength, stiffness, stability, eigenfrequency, and fatigue damage, while decision variables were made based on parameters such as web bar layout, top–bottom diameter ratio, bottom outer diameter, edge number, and number of segments. The optimization objective focused on minimizing steel usage. Feature importance analysis based on machine learning indicated that the bottom outer diameter, number of segments, and top–bottom diameter ratio are the design parameters with the greatest impact on steel usage. Parameter sensitivity analysis examined the influence of different design variables on the steel usage of the proposed support structure. The analysis results revealed the optimal structural configuration strategy and key mechanisms, providing a reference basis for the optimization design of such structures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".