MétaCan
Menu
Back to cohort
Record W4389584805 · doi:10.17118/11143/20918

Finite element analysis of bimorph piezoelectric actuators consideringthermopiezoelectricity

2023· article· en· W4389584805 on OpenAlexaff
Rafael Toledo, Sascha Eisenträger, Ryan Orszulik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsYork University
Fundersnot available
KeywordsBimorphFinite element methodActuatorPiezoelectricityComputer scienceStructural engineeringAcousticsMaterials scienceEngineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract: Piezoelectricity is a phenomenon in which an electric field is generated by a material when mechanical pressure is applied, or conversely, mechanical strain is produced when an electric field is applied. The application of piezoelectric actuators in smart structures is a rapidly developing field, especially in aerospace environments, and it has received significant attention in recent years. The performance of the actuators must be also ensured when large temperature variations are considered, which is the case particularly in aerospace environments and therefore, the theory of thermopiezoelectricity must be applied. Thermopiezoelectricity takes into account the thermal field in addition to the mechanical and electrical fields. Consequently, the coupling effects among these three fields must be considered, including the pyroelectric (electric potential change when temperature is applied) and electrocaloric (temperature change when electric field is applied) effects. This work considers a fully-coupled three field approach and implements a finite element code to examine how the coupling affects piezoelectric bending actuators. Specifically, this work generates both static and dynamic models to quantify the influence that the pyroelectric and electrocaloric effects exert on the positioning and dynamic performance of bimorph actuators.

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 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 score0.850
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicAeroelasticity and Vibration ControlFrench-language works237,207