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Record W4386303817 · doi:10.6000/2369-3355.2023.10.01

Simulation and Experiment Analysis of Driveshaft

2023· article· en· W4386303817 on OpenAlexvenueno aff
Jiahao Li, Yao Liu, Zhou Yang, You Zhe Wang, Zhan Ling Guo, Bin Shen

Bibliographic record

VenueJournal of Coating Science and Technology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsBending stiffnessMaterials scienceStiffnessBendingNatural frequencyCoil springProtein filamentDisplacement (psychology)Deformation (meteorology)Electromagnetic coilSpring (device)Structural engineeringComposite materialMechanicsAcousticsVibrationPhysicsEngineering

Abstract

fetched live from OpenAlex

A driveshaft is a small spring coil less than 1mm in diameter, composed of several stainless-steel wire filaments. In intervention, the driveshaft is used to transmit force and motion to the inside body through the existing micro channels (such as arteries, veins, and gastrointestinal tract). The performance of the driveshaft determines the efficiency, stability, and accuracy of force and motion transitions, the ability to pass through tortuous microchannels, and the damage to healthy tissues. To determine the influence of fabrication parameters (filament, wire diameter, and outer diameter) on the mechanical properties (such as bending stiffness and natural frequency) of the driveshaft, a simulation was established in ABAQUS to calculate the deformation displacement under 0.0098N and first-order natural frequency. Then, the bending stiffness is calculated. The results show that the bending stiffness and the first-order natural frequency of the driveshaft increase with the increase of the filament number and wire diameter, and with the outer diameter of the driveshaft increases, the bending stiffness increases, while the first-order natural frequency decreases. Finally, the simulation model is verified by measuring the deformation displacement in the experiment. This study provides a methodology for designing and selecting the driveshaft in Interventional therapy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.329
Teacher spread0.316 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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