Evolving offshore wind: A genetic algorithm-based support structure optimization framework for floating wind turbines
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a genetic algorithm-based optimization framework for floating offshore wind turbine support structures. Using a nine-variable support structure parameterization, this framework spans a greater extent of the design space than preexisting optimization approaches in the literature. With a frequency-domain dynamics model that includes linearized hydrodynamic forces, linearized mooring forces, and linearized wind turbine effects, the framework provides a good treatment of the important physical considerations while still being computationally efficient. The genetic algorithm optimization approach provides a unique ability to visualize the design space. Application of the framework to a hypothetical scenario demonstrates the framework's effectiveness and identifies multiple local optima in the design space - some of conventional configurations and others more unusual. By optimizing to minimize both support structure cost and root-mean-square nacelle acceleration and plotting the design exploration in terms of these quantities, a Pareto front can be seen. Clear trends are visible in the designs as one moves along the front: designs with three outer cylinders are best below a cost of $6M, designs with six outer cylinders are best above a cost of $6M, and heave plate size increases with support structure cost. The complexity and unconventional configuration of the Pareto optimal designs may indicate a need for improvement in the framework's cost model.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it