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Record W3107931354 · doi:10.3390/en13236431

Numerical Analysis of the Deformation Performance of Monopile under Wave and Current Load

2020· article· en· W3107931354 on OpenAlexaff
Libo Chen, Xiaoyan Yang, Lichen Li, Wenbing Wu, M. Hesham El Naggar, Kuihua Wang, Jinyong Chen

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
FundersNatural Science Foundation of Zhejiang ProvinceChina University of GeosciencesSystematic Project of Guangxi Key Laboratory of Disaster Prevention and Structural SafetyGuangxi Key Laboratory of Disaster Prevention and Engineering SafetyChina Scholarship CouncilNatural Science Foundation of Guangxi ProvinceNational Natural Science Foundation of China
KeywordsEmbedmentPileGeotechnical engineeringOffshore wind powerFinite element methodFoundation (evidence)GeologySlip (aerodynamics)Deflection (physics)Structural engineeringEngineeringWind powerPhysics

Abstract

fetched live from OpenAlex

The research on the deformation mechanism of monopile foundation supporting offshore wind turbines is significant to optimize the design of a monopile foundation under wave and current load. In this paper, a three-dimensional wave-pile-soil coupling finite element model is proposed to investigate the deformation mechanism of monopile undercurrent and fifth-order Stokes wave. Different from the conventional assumption that there is no slip at the pile-soil interface, Frictional contact is set to simulate the relative movement between monopile and soil. Numerical results indicate that under extreme environmental conditions, the monopile foundation sways within a certain range and the maximum displacement in the loading direction is 1.3 times the displacement in the reverse direction. A further investigation has been made for a large-diameter pipe pile with various design parameters. The finite element analyses reveal that the most efficient way to reduce the deflection of the pile head is by increasing the embedment depth of the monopile. When the embedment depth is limited, increasing the pile diameter is a more effective way to strengthen the foundation than increasing the wall thickness.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.187
Teacher spread0.178 · 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 teacher head, 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

Citations34
Published2020
Admission routes1
Has abstractyes

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