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Record W2204088127 · doi:10.1115/omae2015-41253

Dynamic Modeling of Towed Cable System Using the Nodal Position Finite Element and Symplectic Integration

2015· article· en· W2204088127 on OpenAlexafffund
Gangqiang Li, Lian Lian, Zheng Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntegratorFinite element methodNewmark-beta methodControl theory (sociology)Computer scienceEngineeringStructural engineeringBandwidth (computing)

Abstract

fetched live from OpenAlex

The cable of towed underwater system have a character of low tension, in order to overcome this singular problem during numerical calculation, the bending stiffness is included in the bar model, or using the beam model. however, we choose an alternative method called nodal position finite element method, it is different from the traditional finite element method, this alternative method is formulated in term of element nodal position that different with the nodal displacement used in traditional finite element. The model equation is derived from the principle of virtual work, consideration of the hydrodynamic drag force, gravity force, buoyancy and internal damping model. The energy conservative time integrator is preferred for the long term simulation, so we build up a simulation program that using the nodal position finite element method and symplectic leapfrog time integrator for the dynamic analysis of the towed body system. Firstly, the robustness of the proposed time integrator is verified by the elastic spring pendulum, and compared with the traditional frequently used time integrators such as fourth-order Runge-Kutta method and Newmark method, the results show that the proposed approach is accurate and preserves the system energy over long term simulation, then the proposed time integrator is applied to the dynamic modeling of the elastic cable towed system, the well agreement with Sea trail experiment date demonstrates that the simulation program is robust and accurate.

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: none
Teacher disagreement score0.557
Threshold uncertainty score0.290

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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