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Record W2016285896 · doi:10.1115/imece2010-39545

Vibration Energy Harvesting From a Hydraulic Engine Mount via PZT Decoupler

2010· article· en· W2016285896 on OpenAlexaff
Farbod Khameneifar, Siamak Arzanpour, Mehrdad Moallem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEnergy harvestingVibrationBimorphElectric potential energyMechanical energyMaximum power principlePower (physics)VoltageMaterials scienceAcousticsAutomotive engineeringEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Engine is one of the major sources of vibration in a vehicle. An engine mount is the device for isolating the body of vehicles from these vibrations. Harvesting the ambient energy of the vibrating fluid inside a hydraulic engine mount and converting it to the electricity is discussed in this paper. The energy harvester mechanism consists of two piezoelectric bimorph cantilevers with tuning tip masses with the beams covered by a thin layer of rubber. The deflections of the thin rubber layer induce vibrations in the beams which result in electrical power to be generated through the piezoelectric beams. The generated power can be used to recharge the battery for pressure sensors inside the engine mount. This novel harvester is tuned to work in the low frequency, high amplitude excitation environment of the engine. A mathematical model for this energy harvesting application is derived in this paper. Based on this model, the optimum load of the electrical circuit is also obtained. Simulation studies demonstrate performance of the energy harvester and predict the output voltage and maximum power which can be extracted from the energy scavenging device.

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 score0.439
Threshold uncertainty score0.935

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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