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Record W4417088102 · doi:10.1093/jom/ufaf046

Development of 3D infinite element method coupled with a cylindrical mesh technique for analyzing bending of plate with interacting holes

2025· article· en· W4417088102 on OpenAlexaff
De‐Shin Liu, Wen-Hsuan Chang, Chih-Ming Chen, Jui‐Ming Sun, Y. B. Chen, Yuwei Chen, Pei‐Chen Huang, Zhen-Wei Zhuang

Bibliographic record

VenueJournal of Mechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsEducation and Early Childhood Development
FundersMinistry of Education, IndiaDitmanson Medical Foundation Chia-Yi Christian HospitalNational Science and Technology Council
KeywordsFinite element methodMATLABCoupling (piping)Stiffness matrixBendingSoftwareMatrix (chemical analysis)Development (topology)Stiffness

Abstract

fetched live from OpenAlex

ABSTRACT A three-dimensional infinite element method (IEM) for modeling a multihole plate is presented. Additionally, a new concept based on cylindrical coordinates to create a similar mesh layer is proposed. A specific element, embedded with a through-hole of various radii, is formulated using the conventional finite element method (FEM) based on the similarity stiffness of isoparametric eight-node hexagonal elements and matrix condensation operations. An IEM–FEM coupling scheme is developed and implemented in the commercial software MATLAB to conduct the bending analysis of perforated plate. Four numerical examples involving complicated geometries and multiple holes are considered to examine the applicability of the proposed approach. The available solutions computed using the commercial FEM software ABAQUS are used for comparison. The proposed approach demonstrates higher accuracy and efficiency compared with conventional FEM approaches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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