Coupled CFD-MBD numerical modeling of a mechanically coupled WEC array
Bibliographic record
Abstract
The development of new wave energy devices (WECs) has continued unabated over the past decades. For large-scale applications, integrating individual WEC into an array system (WEC net) requires considerable expertise and research due to its highly complex and interrelated nature. Often for a WEC net, it contains main structures and multiple sub-structures. The WEC net response is defined as the total responses from all sub-structures, which is highly complex and closely interconnected with each other. This paper aims to develop a fully-coupled numerical modeling tool that can cope with the wave-structure interaction as well as the mechanical interaction among each sub-structure in a WEC net. The fluid field is solved by a Computational Fluid Dynamic (CFD) solver coupled with a Multi-body Dynamic structural solver. The hydrodynamic and power take-off performance of Albatern 12S Squid WEC net is studied and the results are validated against available laboratory testing data, and commercial mooring and hydrodynamics analysis software. It is found that the motion response of the CFD and experimental approach is in close agreement with each other. The interaction force among sub-structures can be well captured, and the results indicate that the mode response of individual float is strongly affected by the mechanical linking-arms as well as the incident wave conditions, which is hard to achieve without such integrated CFD tool. The power take-off (PTO) is modeled using a damping system. The predicted peak output power is found to increase with the decreasing of wave period and an optimal device's damping to reach a maximum power capture exists, which is dependent on the incoming wave period and height.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".