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Improving the performance of a piezoelectric energy harvester using a variable thickness beam

2010· article· en· W2092102156 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

VenueSmart Materials and Structures · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBimorphPiezoelectricityCantileverBeam (structure)Finite element methodAcousticsStructural engineeringMaterials scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

In recent years, researchers have shown a growing interest in the possibility of harvesting mechanical energy from vibrating structures. A common way to proceed consists of using the direct piezoelectric effect of a bimorph cantilever beam with integrated piezoelectric elements. Several studies focused on the development of analytical models describing the electromechanical coupling. Historically, most of these models have been limited to simple structures such as a constant cross-section cantilever beam harvester. This paper studies the effect of a variable thickness beam harvester on its electromechanical performance. A semi-analytical mechanical model was developed using Rayleigh–Ritz approximations with a trigonometric functions set. The model was next validated by a finite element (FE) modeling. Numerical simulations were then performed for different beam slope angles in order to find the optimum for a given maximal strain across the piezoelectric elements. For the case under study, it is shown that tapered beams lead to a more uniform strain distribution across the piezoelectric material and increase the harvesting performance by a factor of 3.6.

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 score0.020
Threshold uncertainty score0.403

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 head0.007
GPT teacher head0.188
Teacher spread0.182 · 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