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Record W4415013361 · doi:10.1142/s0219455427500994

A New Pounding Tuned Mass Damper for the Aeolian Vibration Control of Power Transmission Conductors

2025· article· en· W4415013361 on OpenAlexaff
Zhisong Wang, Z.X. Wang R.P. Zhou, Eric Savory

Bibliographic record

VenueInternational Journal of Structural Stability and Dynamics · 2025
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsWestern University
FundersScientific Research Foundation for Returned Scholars of Ministry of EducationNational Natural Science Foundation of China
KeywordsTuned mass damperVibrationVibration controlDamperConductorReduction (mathematics)Transmission (telecommunications)Kinetic energy

Abstract

fetched live from OpenAlex

One of the most prevalent types of wind-induced vibration in transmission lines is aeolian vibration, which can lead to fatigue damage in the conductors. To address this issue, this study proposes a novel Pounding Tuned Mass Damper (PTMD), which integrates the Tuned Mass Damper (TMD) and an impact damper. The PTMD not only absorbs kinetic energy through the tuned mass but also dissipates the absorbed energy via impact. Theoretical analysis and experimental studies were conducted to evaluate the vibration control effectiveness of the PTMD. In the theoretical analysis, the commonly used linear viscoelastic Kelvin–Voigt pounding model was employed to derive the equations of motion for the Conductor-PTMD (CP) system, based on the kinetic method. Both free and forced vibration analyses were performed. The results indicated that a vibration reduction rate of 90% was achieved at an excitation frequency of approximately 3 Hz. In the experimental study, a custom-designed CP system was developed to validate the theoretical model. The PTMD significantly increased the system’s damping ratio from 1.0% to 7.5% in free-vibration experiments. Additionally, in forced vibration tests, the PTMD demonstrated excellent performance in reducing conductor vibrations.

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 score0.764
Threshold uncertainty score0.247

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 head0.239
Teacher spread0.233 · 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
Published2025
Admission routes1
Has abstractyes

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