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Record W2806278873 · doi:10.3968/10124

Vibration Characteristics of Rock Under Harmonic Impact

2017· article· en· W2806278873 on OpenAlexvenueno aff
Guodong Ji, Haige Wang, Zhijian Hu, Siqi Li, Tie Yan, Bing Li

Bibliographic record

VenueAdvances in petroleum exploration and development · 2017
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationAmplitudeAcousticsHarmonicModal analysisHarmonic analysisStructural engineeringGeologyEngineeringPhysicsOpticsElectronic engineering

Abstract

fetched live from OpenAlex

Modal analysis of rock is done in this study, and the results of numerical analysis are presented. Meanwhile, the amplitude-frequency characteristic curve of rock in steady state response is investigated based on the principle of vibration. In addition, indoor experiments are carried out to further analyze the vibration characteristics of rock under harmonic impact. Three main control parameters are considered, including drilling way, excitation frequency and impacting amplitude. Our investigations confirm that the rock has different resonant frequencies and vibration modes in different orders for free vibration system, and there is only one resonant frequency for a rock with one degree of freedom. Based on theoretical analysis and indoor experiments, it can be concluded that the vibration amplitude under resonant frequency of rock is significantly higher than that under non-resonant frequency and in conventional drilling. Also, the vibration response of rock is in the harmonic form by the harmonic impact, and increases with the increase of the impacting amplitude. The vibration characteristics of rock by harmonic impact are validated by numerical analysis and experimental results. Harmonic vibration impact drilling can greatly enhance the vibration amplitude of rock, and further improve the rate of penetration.

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 score0.787
Threshold uncertainty score0.359

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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
Published2017
Admission routes1
Has abstractyes

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