Comparison of Physical Model Tests With a Time Domain Simulation Model of a Wave Energy Converter
Why this work is in the frame
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Bibliographic record
Abstract
Development of wave energy conversion systems may yield many key benefits for society such as the production of electrical power or fresh water for remote communities. However, complex ocean dynamics make it difficult for technology developers to not only address the stability and survivability of their systems, but also to establish energy conversion rates that are fundamental to proving economic viability. Building physical prototypes presents many challenges in terms of cost, accessible facilities, and time requirements. The use of accurate numerical modelling and computer simulation can help guide design and significantly reduce the number of physical prototype tests required and as a result play a primary role in the development of wave energy conversion systems that have to operate in challenging marine environments. SurfPower is an ocean wave energy converter (WEC) that converts wave motion into useful energy through surge and heave motion of a point absorber. The system pumps seawater into a high pressure hydraulic network that generates electricity via a turbine or freshwater via desalination at a facility onshore. The system is nonlinear due to the significant change in draft and mooring reaction load through the energy capture cycle of the device. This makes the use of nonlinear time domain simulation ideal for analysis and design of the system. Furthermore, utilizing a simplified nonlinear hydrodynamic model available in the time domain results in a practical early-stage design tool for system refinement. The focus of this work is to compare the results of scale model testing completed at the Institute for Ocean Technology in St. John’s, Newfoundland, with results produced from an equivalent system simulated in the time domain simulation software ProteusDS. The results give an assessment of the range of error that can be used to assess other experiments of the SurfPower WEC at full scale.
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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) | 0.000 | 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