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Record W3027719969 · doi:10.1080/19475411.2020.1751743

Performance enhancement of cantilever piezoelectric energy harvesters by sizing analysis

2020· article· en· W3027719969 on OpenAlexafffund
Peyman Hajheidari, Ion Stiharu, Rama Bhat

Bibliographic record

VenueInternational Journal of Smart and Nano Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsConcordia University
FundersConcordia UniversityCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsSizingPiezoelectricityCantileverEnergy harvestingMaterials scienceAcousticsEnergy (signal processing)Composite materialPhysicsChemistryMathematicsStatistics

Abstract

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This paper presents the results of the performance of piezoelectric cantilever beams in relation to their size. The total produced power represents the main indicator of performance of a piezoelectric harvesting system while the area of the beams stays constant. Lightweight design is an important aspect in any industry, mainly in the aerospace. In this study, the effects of non-uniformity on the efficiency and power output are studied. Finite element method (FEM) with the application of superconvergent element (SCE) is adopted here to solve the equations. It is observed that the trapezoidal geometry (converging beam) provides a higher output power while the efficiency decreases. Moreover, in order to prove that the power enhancement is achievable while the amount of piezoelectric material consumed is constant the new configuration is proposed. In the configuration, an array of uniform beams connected in series is used instead of one single rectangular beam. The proposed setting generates an output power of 1.817 mW at a resonant frequency of 284.6 Hz when excited by an input acceleration of 1 g. The only challenge is the fundamental frequency difference which is met with the application of proof mass and thinner substrate and piezoelectric layers. Abbreviations: $$a\left(t \right)$$: Generalized coordinates vector; $${A_p}$$: The electrode area; A: The amplitude vector of generalized coordinates; $$b\left(x \right)$$: Width; $${b_0}$$: Initial width; C: Damping matrix; $${C_p}$$: Capacitance of the one piezoelectric layer; $${\bar C_p}$$: Effective (equivalent) capacitance of the piezoelectric layers; $${d_{31}}$$: Piezoelectric strain coefficient; $${D_3}$$: The electric displacement; $$E$$: Young’s modulus; $${E_3}$$: The electrical field along the thickness direction; F: Dynamic force vector; $$g\left(t \right)$$: The translation part of base motion; $$G$$: Shear modulus; $${h_p}$$: Piezoelectric layers’ thickness; $${h_0}$$: Initial thickness of substrate layer; $$i$$: Imaginary number; $$I\left(t \right)$$: Current output; $${k_s}$$: Shear correction factor; K: Global stiffness matrix; $$L$$: Length of the beam; $${l_e}$$: The length of one element; M: Global mass matrix; $$n$$: Polynomial’s degree; $$ \boldsymbol{N_w}, {N_\phi }$$: Matrices of shape functions (1×4 for one element); $${P_{in}}$$: The input mechanical power; $${P_{out}}$$: The output electrical power; $$Q\left(t \right)$$: Electric charge output; R: Displacement vectors of the generic point S (3×1); $${R_l}$$: External load resistance; $${\dot R}$$: Velocity vector of the generic point S (3×1); $$t$$: Time; $$T$$: Kinetic energy; $$u$$: Axial displacement; $$U$$: Potential energy; $$v, w$$: Transversal displacements; $$V$$: Volume; $$v\left(t \right)$$: The generated piezoelectric voltage$${\ }{v_0},{\ }{w_0}$$: Displacements on the middle-plane; $$x,{\ }y,{\ }z$$: Positions of a general point in the relative coordinates system; $$X$$: The amplitude of the effective displacement, $${w_b}$$; $$\varepsilon _{33}^T$$: The dielectric permittivity of piezoelectric layer at constant stress; $$\varepsilon _{33}^s$$: The dielectric permittivity of piezoelectric layer at constant strain; $$\boldsymbol {\theta}$$: The electromechanical coupling vector (3×1 for one element); $${\phi _x}$$: The binding rotation of the cross section; $$\gamma $$: Transverse shear strain; $$\varepsilon $$: Axial strain; $$\sigma $$: Axial stress; $$\tau $$: Transverse shear stress$${\ }\psi _j^e,{\ }\varphi _j^e$$: Shape functions of superconvergent element, $$j = 1,{\ } \ldots {\ },4$$; $${\ }W_i^e,{\ }\phi _i^e, W_{\rm{i}}^{"e}, \break{\ }\phi _{\rm{i}}^{"e}\left(t \right)$$: Generalized coordinate elements, $$i = 1,{\ }2$$; $$\rho $$: Mass density; $${\rm{\Gamma }}$$: The efficiency of harvesting; $$\omega $$: Excitation frequency; $$\mu $$: Constant of mass proportionality; Constant of stiffness proportionality; $$\zeta $$: Damping ratio; $${\alpha _b}$$: Width taper ratio; $${\alpha _h}$$: Height taper ratio; $$\delta $$: The symbol of virtual work; $${W_{IE}}$$: The internal electrical energy; $${W_{nc}}$$: The non-conservative mechanical force; RD: Relative difference; PEHs: Piezoelectric energy harvesters; FEM: Finite element method; FRFs: Frequency response functions; DOF: Degree-of–freedom; MEMS: Micro-electromechanical system; MPGs: Micro-power generators; SCE: Superconvergent element; $${()_p}$$: Piezoelectric layer properties; $${()_s}$$: Substrate layer properties; $$\mathop {()}\limits"$$ : Partial differentiation with respect to x; $$\mathop {()}\limits"$$: Partial differentiation with respect to t.

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 score0.006
Threshold uncertainty score0.348

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 head0.010
GPT teacher head0.203
Teacher spread0.194 · 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".

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Citations12
Published2020
Admission routes2
Has abstractyes

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