Mooring System Considerations for Renewable Energy Standards
Bibliographic record
Abstract
Abstract Marine renewables have made great strides in recent years. The IEC, ABS, and DNV GL continue to generate standards and recommended practices in an effort to formulate approved processes as the renewable products make their way offshore and into the market. There are many similarities in some of the processes and designs when compared to oil and gas structures, especially when it comes to moorings. However, many design areas are uniquely related to renewables, even within the same field of energy conversion (e.g. multiple types of wave energy converters). As more renewable systems are installed, the standards will continue to transition from philosophical to more prescriptive recommendations. One area in which the lines are blurred between oil and gas and renewable industries is mooring systems. The interdependency between the mooring and power generation systems plays a crucial role early in the design phase. Modeling marine energy converters and the mooring system can be complex due the variability of moving parts, and without proper attention, it may be easy to underestimate the loads and fatigue cycles to which moorings will be exposed. Moorings for these structures should incorporate existing standards and recommended practices to ensure safety and reliability. Inspection, maintenance, repair, and replacement should also be considered. As the renewable industry continues to move forward from scaled prototypes to farms of devices, the oil and gas supply chain will contemplate when to become involved from a financial and resource perspective. However, there are still hurdles within the US authorization bodies like BSEE, BOEM, FERC, NOAA, USCG, etc. to overcome. This paper addresses the existing mooring related standards and delineates areas that need further refinement or conservatism as the renewable industry moves forward with the installation of offshore energy converters.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".