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Record W3215323766 · doi:10.32920/ryerson.14663334.v1

Dynamic Finite Element Modelling and Free Vibration Analysis of Delaminated Composite Beams

2021· preprint· en· W3215323766 on OpenAlexaff
Nicholas H. Erdelyi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFinite element methodDelamination (geology)VibrationStiffnessKinematicsStructural engineeringStiffness matrixComputer scienceConstraint (computer-aided design)Convergence (economics)Path (computing)Mechanical engineeringEngineeringAcousticsPhysicsGeology

Abstract

fetched live from OpenAlex

The requirement for accurate analysis tools to predict the behaviour of delaminated composites has grown and will continue to grow into the future, due to the high demand of these materials on major structural components. In the following, a detailed analysis of single- and double-delaminated beams is made, using traditional finite element techniques, as well as two dynamic element-based techniques. The Dynamic Stiffness Matrix (DSM) and Dynamic Finite Element (DFE) techniques introduce the concept of frequency-dependent stiffness matrices and shape functions, respectively, and have been documented to exhibit excellent convergence qualities when compared to traditional finite elements. Current trends in the literature are critically examined, and insight into different types of modeling techniques and constraint types are introduced. In particular, the continuity (both kinematic and force) conditions at delamination tips plays a large role in each model’s formulation. In addition, the data previously available from a commercial finite element suite are also utilized to validate the natural frequencies of the systems analyzed here. Beam element-based techniques are used and the results are compared to those obtained using the dynamic element techniques and data from the literature. In each case excellent agreement between different techniques was observed. Finally, general concluding remarks are made on the usefulness of the presented theories, and some comments are made on the future work of this research path.

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 categoriesMeta-epidemiology (narrow)
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 score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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.

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
Published2021
Admission routes1
Has abstractyes

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