Assimilating a Time-Domain Representation of a Wave Energy Converter Into a Spectral Wave Model
Bibliographic record
Abstract
To accommodate future power demands, wave energy converters (WECs) will be deployed in arrays, but largely unanswered questions of the annual energy production and environmental impact of such installations present regulatory dilemmas. In recent years, Sandia National Laboratories (SNL) has developed a modified version of the Simulating Waves Nearshore (SWAN) wave model to simulate WEC energy extraction in a propagating wave field. The SNL source code modifications to SWAN have facilitated a way to characterize the frequency dependent power absorption of a device in a spectral model using standard WEC parameterizations. The work presented in this paper seeks to build on source code modifications previously made by SNL. A new WEC meta-model, alters the incident wave spectrum based on power extracted from the sea and dissipated by hydrodynamic losses experienced at the WEC. These losses are calculated in an external six degree of freedom (DOF) time domain WEC simulation. The two WEC models were compared in terms of significant wave height reduction in the WEC’s lee and annual power production. The new model reduced the estimated distance required for the waves to recover 95% of the incident wave height by 50% for the same sea state. A 4.5% difference in annual power production was observed for a WEC operating in the lee of another device when deployed off the west coast of Canada.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".