Power Take-Off Simulation for Scale Model Testing of Wave Energy Converters
Bibliographic record
Abstract
Small scale testing in controlled environments is a key stage in the development of potential wave energy conversion technology. Furthermore, it is well known that the physical design and operational quality of the power-take off (PTO) used on the small scale model can have vast effects on the tank testing results. Passive mechanical elements such as friction brakes and air dampers or oil filled dashpots are fraught with nonlinear behaviors such as static friction, temperature dependency, and backlash, the effects of which propagate into the wave energy converter (WEC) power production data, causing very high uncertainty in the extrapolation of the tank test results to the meaningful full ocean scale. The lack of quality in PTO simulators is an identified barrier to the development of WECs worldwide. A solution to this problem is to use actively controlled actuators for PTO simulation on small scale model wave energy converters. This can be done using force (or torque)-controlled feedback systems with suitable instrumentation, enabling the PTO to exert any desired time and/or state dependent reaction force. In this paper, two working experimental PTO simulators on two different wave energy converters are described. The first implementation is on a 1:25 scale self-reacting point absorber wave energy converter with optimum reactive control. The real-time control system, described in detail, is implemented in LabVIEW. The second implementation is on a 1:20 scale single body point absorber under model-predictive control, implemented with a real-time controller in MATLAB/Simulink. Details on the physical hardware, software, and feedback control methods, as well as results, are described for each PTO. Lastly, both sets of real-time control code are to be web-hosted, free for download, modified and used by other researchers and WEC developers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".