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Record W4231926303 · doi:10.22215/etd/2015-11839

A Self-Contained Roadway Kinetic Energy Harvesting System for Energy Recovery

2015· dissertation· en· W4231926303 on OpenAlexaff
Xinghe Wang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsEnergy harvestingTransducerVoltageKinetic energyPiezoelectricityEnergy (signal processing)Finite element methodEngineeringElectrical engineeringElectronic engineeringTopology (electrical circuits)PhysicsStructural engineering

Abstract

fetched live from OpenAlex

Kinetic Energy Harvesting (KEH) systems have been applied to various maintenance-free electronic devices. One of the potential applications is harvesting a decent amount of kinetic energy generated by the road vehicles. Although researches have been conducted on the roadway KEH system, it is worthwhile to improve the system in order to convert more energy without worsening the driving experience. This work consists of design of the piezoelectric KEH transducer (Cymbal transducer), and improvement of the KEH circuit. The optimal geometry of each Cymbal unit is determined by the utilization of Finite Element Analysis. After studying several recently introduced piezoelectric KEH circuit topologies, a preferred topology has been adopted for a novel KEH circuit design. A voltage peak detection function and an energy storage unit voltage hysteresis function have been included in the circuit. The circuit has demonstrated a higher energy harvesting efficiency compared to competitors during tests.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.220
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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