Development of Control Strategies for Interconnected Pneumatic Wave Energy Converters
Bibliographic record
Abstract
In the field of wave energy converter control, high fidelity numerical models have become the predominant tool for the development of accurate and comprehensive control strategies. In this study, a numerical model of a novel wave energy converter, employing a pneumatic power take-off, is created to provide a low-cost method for the development of a power-maximizing control strategy. Device components and associated architectures are developed in the time domain solvers Proteus DS and MATLAB/Simulink. These two codes are dynamically coupled at run time to produce a complete six degree of freedom, time domain simulation of the converter. Utilizing this numerical framework, a genetic algorithm optimization procedure is implemented to optimally select eight independent parameters governing the PTO geometry. Optimality is measured in terms of estimated annual energy production at a specific deployment location off the West Coast of Canada. The optimization exercise is one layer of PTO force control — the parameters selected are seen to provide significant improvements in the annual power output, while also smoothing the WEC power output on both a sea-state by sea-state and wave-by-wave basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".