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Record W4386952965 · doi:10.1115/omae2023-104576

A New Method for the Design and Coupled Analysis of Floating Offshore Wind Turbines

2023· article· en· W4386952965 on OpenAlex
Daniel Veen, Spiro J. Pahos, Shawn X. Meng, Simon Dillenburg

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsOffshore wind powerSolverTurbineWind powerMarine engineeringComputer scienceReliability (semiconductor)SoftwareComputational fluid dynamicsSubmarine pipelineEngineeringPower (physics)Aerospace engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Global power generation from floating offshore wind turbines is expected to grow from 132 MW in 2022 to 289 GW by 2035. During the design phase these floating systems will require advanced numerical analysis tools to ensure strucutural integrity and reliability. A new method for the hydrodynamic analysis of these structures is presented. The long standing offshore operations simulation software MOSES has been enhanced with the addition of the AeroDyn aeroelastic wind turbine solver from OpenFAST. This coupled analysis tool is designed to compute the motions and loads on the floating system. An added benefit of using MOSES is that these loads can be readily mapped on to a structural model for assessing code compliance. In this paper the MOSES-AeroDyn solver has been validated against published numerical and model test data of the OC3-Hywind system in regular waves. The coupled MOSES-AeroDyn results were found to be in good agreement with both the experimental and numerical data.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.275
Teacher spread0.244 · 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

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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