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Finite Element Model Updating of a Thin Wall Enclosure under Impact Excitation

2010· article· en· W2132447661 on OpenAlexaff
Opukuro David-West, Jianliang Wang, Richard K. Cooper

Bibliographic record

VenueApplied Mechanics and Materials · 2010
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsFinite element methodEnclosureDiscretizationExcitationLanczos resamplingStructural engineeringConstant (computer programming)MechanicsShell (structure)Natural frequencyEngineeringMathematical analysisMathematicsComputer sciencePhysicsMechanical engineeringAcousticsVibrationEigenvalues and eigenvectors

Abstract

fetched live from OpenAlex

Simulation result of a structural dynamics problem is dependent on the techniques used in the finite element model and the major task in model updating is determination of the changes to be made to the numerical model so that dynamic properties are comparable to the experimental result. In this paper, the dynamic analysis of a thin wall structure ( approx. 1.5±0.1 mm thick) was realized using the Lanczos tool to extract the modes between 0 and 200 Hz, but the interest was to achieve a good aggreement between the first ten natural frequencies. A shell element with mid size nodes was used to improve the finite element result and the model was tunned using the damping constant, material properties and discretization. The correlation of the results from the impact excitation response test and the finite element was significantly improved. A correlation coefficient of 0.99 was achieved after tunning the model.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.273
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
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

Citations6
Published2010
Admission routes1
Has abstractyes

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