Comparison of Physical Model Tests With a Time Domain Simulation Model of a Wave Energy Converter
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".