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Record W2800821854 · doi:10.1139/tcsme-2016-0034

ANALYSIS OF ENERGY ABSORBING DEVICE BASED ON DAMPED DYNAMIC VIBRATION ABSORBER

2016· article· en· W2800821854 on OpenAlexvenueno aff
Zhongqiang Zheng, Tao Yao, Peng Huang, Zongyu Chang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsnot available
FundersProgram for New Century Excellent Talents in UniversityHigher Education Discipline Innovation ProjectNational Science Foundation
KeywordsVibrationDamping ratioDynamic Vibration AbsorberPower (physics)Dimensionless quantityEnergy (signal processing)AcousticsTransducerExcitationFrequency domainEnergy harvestingMechanical energyDamping torqueEngineeringPhysicsMechanicsComputer scienceElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Vibrations exist widely in tall buildings, vehicle systems, and ocean platform suffering from environmental loading such as wind, wave or earthquake. With the global concern on energy and environmental issues, energy absorbing from large-scale vibrations becomes a research frontier. A type of damped dynamics vibration energy absorber is built and analyzed in this paper, in which an added mass is connected to the vibration system with the electromagnetic transducer and spring. The relationships between electrical damping ratio, excitation frequency ratio and dimensionless power are analyzed in frequency domain. The optimal parameters for maximizing the power output are discussed in analytical form while taking the inherent mechanical damping of the system into account. The results indicate that when the system has suitable optimal excitation frequency ratio and damping ratio and so on, more power can be obtained. It is helpful for the design of energy absorber devices.

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 score0.954
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.202
Teacher spread0.192 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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