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Record W4405308632 · doi:10.23977/jemm.2024.090302

Study on design and optimization of Marine mooring system based on statics model

2024· article· en· W4405308632 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldEngineering
TopicIndustrial Technology and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsStaticsMooringMarine engineeringComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, the static characteristics and optimization of mooring system design are studied through mechanical and mathematical analysis. Firstly, based on the principles of static balance and moment balance, the stability of the mooring system and the stress of each component are analyzed when the sea surface wind speed is 12m/s and 24m/s. The relationship between the vertical projection length and the total length of the anchor chain is deduced by using the calculus method, and the critical wind speed is calculated to be 21.92m/s. Under specific conditions, when the wind speed is 12m/s, the tilt Angle of the steel drum is 2.2 degrees, the tilt Angle of each section of the steel pipe gradually increases, the anchor chain part lies flat on the seabed, the remaining part is in a curve shape, the draft depth of the buoy is 0.6816m, and the swimming area is a circle with a radius of 14.676m centered on the anchor. When the wind speed is 24m/s, the tilt Angle of the steel drum increases to 4.584 degrees, the tilt Angle of the steel pipe increases step by step, the Angle between the tangential line at the bottom of the anchor chain and the seabed is 4.4 degrees, the draft depth of the buoy increases to 0.6957m, and the swimming area is a circle with a radius of 17.7918m. The research results of this paper provide theoretical basis and specific parameter reference for rational design and optimization of mooring system

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)0.001
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: none
Teacher disagreement score0.953
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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