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Record W2004856131 · doi:10.1115/imece2006-14842

Power Harvesting for Rotating Structure Using Piezoelectric Generators

2006· article· en· W2004856131 on OpenAlexaff
Saurav Gupta, Grant Warner

Bibliographic record

VenueDesign Engineering and Computers and Information in Engineering, Parts A and B · 2006
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsPTC (Canada)
Fundersnot available
KeywordsSlip ringEnergy harvestingCantileverVibrationElectrical engineeringMechanical energyPiezoelectricityMicroelectromechanical systemsPower (physics)BimorphSlip (aerodynamics)Electricity generationMechanical engineeringEngineeringComputer scienceAcousticsMaterials scienceStructural engineeringAerospace engineeringPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Conventional method of measuring the mechanical properties of rotating machinery is to couple sensors on the machine through a slip ring, which is a non-trivial, expensive, lengthy and manpower intensive process. An alternative to this is to use a contactless RF slip ring which has no physical wear and hence no maintenance. But application of contactless RF slip ring is possible only if these services are low powered and sensor signals can be multiplexed. With the advance in low powered MEMS sensors, contactless slip ring system can be used. But providing power to these sensors is an issue. One approach would be to harness power from the untapped surrounding energy which could be used to recharge and/or replace battery powered connections. One method to accomplish this is to use piezoelectric materials (PZT) to capture energy lost due to vibration and rotation of the test equipment. This captured energy can then be used to provide uninterrupted power to the appropriate sensors. Focusing our attention on blades, rotating structures will be modeled as cantilever beam. Piezoelectric bimorph attached to the rotating cantilever beam will provide an estimate for the available power that can be used for harvesting.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.424
Threshold uncertainty score0.985

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 head0.006
GPT teacher head0.172
Teacher spread0.165 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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Same venueDesign Engineering and Computers and Information in Engineering, Parts A and BSame topicInnovative Energy Harvesting TechnologiesFrench-language works237,207