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Record W2514091195 · doi:10.1063/1.4961232

Distributed parameter model and experimental validation of a compressive-mode energy harvester under harmonic excitations

2016· article· en· W2514091195 on OpenAlexafffund
Haitao Li, Zhengbao Yang, Jean W. Zu, Weiyang Qin

Bibliographic record

VenueAIP Advances · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Toronto
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsParametric statisticsNonlinear systemExcitationEnergy harvestingAmplitudeVoltageBandwidth (computing)PhysicsHarmonicMechanicsFrequency responseParametric oscillatorAcousticsStructural engineeringEnergy (signal processing)Computer scienceEngineeringMathematicsOpticsElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents the modeling and parametric analysis of the recently proposed nonlinear compressive-mode energy harvester (HC-PEH) under harmonic excitation. Both theoretical and experimental investigations are performed in this study over a range of excitation frequencies. Specially, a distributed parameter electro-elastic model is analytically developed by means of the energy-based method and the extended Hamilton’s principle. An analytical formulation of bending and stretching forces are derived to gain insight on the source of nonlinearity. Furthermore, the analytical model is validated against with experimental data and a good agreement is achieved. Both numerical simulations and experiment illustrate that the harvester exhibits a hardening nonlinearity and hence a broad frequency bandwidth, multiple coexisting solutions and a large-amplitude voltage response. Using the derived model, a parametric study is carried out to examine the effect of various parameters on the harvester voltage response. It is also shown from parametric analysis that the harvester’s performance can be further improved by selecting the proper length of elastic beams, proof mass and reducing the mechanical damping.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.368

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 communication0.0000.001
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 head0.022
GPT teacher head0.271
Teacher spread0.249 · 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 designBench or experimental
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

Citations8
Published2016
Admission routes2
Has abstractyes

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