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Record W7132946000

A hierarchical theory for layered beams with piezoelectric actuation

2004· dissertation· W7132946000 on OpenAlexfundno aff
Clayton McLean

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsPiezoelectricitySuperposition principleComposite numberFinite element methodDisplacement (psychology)Timoshenko beam theoryStress (linguistics)Beam (structure)
DOInot available

Abstract

fetched live from OpenAlex

In order to realize the full compatibility of advanced composite and sandwich structures an internally consistent and accurate modeling process is needed. Hansen and de Almeida developed a unified hierarchical theory for layered beams which has the ability to accurately predict through-the-thickness stress and strain distributions, as well as displacement moments of all combinations of symmetric laminates and sandwich structures. This theory asserts that these predictions can be developed as a superposition of sets of well chosen fundamental states, where a hierarchical sequence of bending states occurs. Fundamental states are described as numerical experiments performed on an infinitesimal segment of the physical beam. Here, piezoelectric actuation effects are considered and a new fundamental state is developed modeling the actuation within the composite or sandwich structure through the application of a uniform electric field. The capabilities of this fundamental state are shown within the confines of the hierarchical beam theory by solving three problems: a purely actuated system, a system subjected to both electrical and mechanical loads and a system of piezoelectric patches that are embedded within a composite structure. The calculated through-the-thickness stress and strain distributions and the displacement moments yield comparable results to 2-D ANSYS finite element predictions.

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 score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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
Published2004
Admission routes1
Has abstractyes

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