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Record W2778900361 · doi:10.1177/0309524x18756962

On mooring line tension and fatigue prediction for offshore vertical axis wind turbines: A comparison of lumped mass and quasi-static approaches

2018· article· en· W2778900361 on OpenAlexaff
D Cevasco, Maurizio Collu, CM Rizzo, Matthew Hall

Bibliographic record

VenueWind Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsTurbineMarine engineeringMooringOffshore wind powerWind powerHorizontal axisAerodynamicsTension (geology)Vertical axisRotor (electric)EngineeringStructural engineeringAerospace engineeringCompression (physics)Mechanical engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Despite several potential advantages, relatively few studies and design support tools have been developed for floating vertical axis wind turbines. Due to the substantial aerodynamics differences, the analyses of vertical axis wind turbine on floating structures cannot be easily extended from what have been already done for horizontal axis wind turbines. Therefore, the main aim of the present work is to compare the dynamic response of the floating offshore wind turbine system adopting two different mooring dynamics approaches. Two versions of the in-house aero-hydro-mooring coupled model of dynamics for floating vertical axis wind turbine (FloVAWT) have been used, employing a mooring quasi-static model, which solves the equations using an energetic approach, and a modified version of floating vertical axis wind turbine, which instead couples with the lumped mass mooring line model MoorDyn. The results, in terms of mooring line tension, fatigue and response in frequency have been obtained and analysed, based on a 5 MW Darrieus type rotor supported by the OC4-DeepCwind semisubmersible.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.241
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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