Operations & Maintenance Simulation for Tidal Energy Converters:
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
he Netherlands is establishing its own test and development centre for marine energy: the Dutch Marine Energy Centre (DMEC), in order to facilitate essential real sea experience. However, realistic simulations in software is also important: helping avoid expensive failures, plan costs and choose vessels and equipment for offshore renewable energy farms. ECN O&M Calculator is a time domain simulation tool used to model different Operations & Maintenance (O&M) strategies for offshore wind farms and to compute the corresponding KPIs. This has been converted to be useful for tidal current energy farms, through the following adaptations: 1. An updated user interface; 2. Use of tidal currents and weather at multiple locations for operability assessment of vessels; 3. More flexible and multiple shift patterns to use short, changing access periods at slack tide; 4. Separation between deterministic tidal currents and uncertain waves for simulation, and use of tidal currents-instead of wind-for turbine performance; 5. Inclusion of a powerful weather simulator to improve understanding of long-term variability of KPIs. This new tool is applied to case studies through working closely with Tocardo International BV, a developer of tidal current energy turbines based at Den Oever, The Netherlands. The expected performance of their planned test projects, at FORCE in Canada and EMEC in Scotland, are assessed in terms of costs and availability, based on the planned O&M strategy. Subsequently, improved O&M strategies for these projects are explored through simulations. At FORCE, waiting on spares and waiting for vessels to complete long transits have a significant impact on farm performance. The 15-year average availability using the baseline O&M strategy is 69.2%, costing 5.08 M$/year. By applying a stock control to two spare parts, the availability increases to 90.7% (yield) while the costs increase to 5.88 M$/year. Further, the planned number of three standby speedboats is unnecessary, and can be reduced to one. At EMEC, a similar result is found, where stock control and improving the speed of the tug boats can significantly improve performance. The baseline O&M strategy results in average availability of 68.7%, costing 0.25M£/year. By contrast, the best strategy found gives an availability of 84.4%, costing 0.32M£/year.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it