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

2010· article· en· W2132447661 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.432

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 head0.015
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