Operations expenditure modelling of the X-Rotor offshore wind turbine concept
Bibliographic record
Abstract
Abstract O&M of an offshore wind farm is becoming increasingly challenging as farms are being commissioned further from shore. Weather windows are more difficult to navigate leading to longer downtime for turbines. The X-Rotor offshore wind turbine concept directly tackles these O&M challenges by, amongst other advantages, removing the requirements for components that have traditionally contributed high failure rates, repair times and downtimes, and by placing the heavy and expensive machinery closer to sea level. The turbine also benefits from having modular small rotors that can be quickly replaced and repaired onshore, and being able to operate at reduced capacity when there are failures in the modular rotors. This paper presents the StrathX-OM OpEx model. This model features changes to OpEx modelling that will allow for comprehensive analysis of the operations and maintenance costs for a wind farm made up of radical X-Rotor wind turbines with the flexibility to handle changing designs as the technology progresses. The calculation of lifetime O&M costs for a wind farm 100 km from shore showed that the X-Rotor has lower O&M costs than conventional HAWTs for an established design. A sensitivity study on the estimated failure rates of X-Rotor is also presented. This shows that even with significantly over-estimated failure rates the X-Rotor would still be competitive in today’s market.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".