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Record W3126791537 · doi:10.1115/1.4048888

Optimal Geometry for Cable Wrapping to Minimize Dynamic Impacts on Cable-Harnessed Beam Structures

2020· article· en· W3126791537 on OpenAlexafffund
Shilei Cao, Pranav Agrawal, Naiming Qi, Armaghan Salehian

Bibliographic record

VenueJournal of vibration and acoustics · 2020
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBeam (structure)ModalOptimal designHomogenization (climate)Power cableEquivalence (formal languages)Structural engineeringComputer scienceMaterials scienceEngineeringMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract Power and cable carrier systems have been shown to significantly impact the dynamic behavior of lightweight space structures. The goal of this paper is to obtain an optimal cable placement geometry for cable-harnessed beam structures by minimizing the impact of added cables on the system dynamics. The cable is harnessed to the beam in a periodic pattern, which forms several repeating fundamental elements within the structure. An analytical model for the cable-harnessed beam, using an energy-equivalence homogenization method, is employed for the purpose of optimization. The natural frequencies of the cable-harnessed beam are then matched with the bare beam (when no cables attached) using an optimization algorithm to find the optimal cable placement solution for the given system parameters. Subsequently, the system parameters’ effects on the optimal solutions are investigated and discussed. Experimental modal analysis is then performed to further validate the optimal solutions found using the model. The test results further validate findings from the model, and the frequency response functions from the bare and optimally wrapped beams align really well.

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 score0.838
Threshold uncertainty score0.463

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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