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Record W3217627954 · doi:10.1115/1.4052103

Improved Design and Dynamics Characteristics Research of a Composite Percussion Drilling Tool

2021· article· en· W3217627954 on OpenAlexaff
Jialin Tian, Haili Yang, Liming Dai, Yinglin Yang, Haolin Song

Bibliographic record

VenueJournal of Energy Resources Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsHammerDrillingAccelerationSlip (aerodynamics)Dynamics (music)Composite numberMechanicsStructural engineeringMaterials scienceEngineeringMechanical engineeringAcousticsPhysicsAerospace engineeringComposite materialClassical mechanics

Abstract

fetched live from OpenAlex

Abstract Internal motion and dynamics mechanism studies of a new composite percussion drilling tool aim at reducing stick-slip phenomenon and improving rock breaking efficiency. In this study, experiments are performed using composite percussion drilling tools to investigate its torsional and axial composite impact performance. According to the experimental results, a six degrees-of-freedom (6DOF) rigid body motion model was established to study the passive motion of a torsional hammer. The obtained results, including the tangential acceleration, were verified with experimental data, and the small pressure differences between the high and low pressure areas, which mainly determined by the inlet structure, is the main reason for the poor torsional impact effects. Based on these discoveries, the improved design increases the inlet flow to 17.2% of the total, the pressure differences to 0.05 MPa, and the instantaneous tangential acceleration to 0.198 m/s2, which results in increased tangential acceleration fluctuation amplitude by 1137.5% and greatly improved torsional impact performance. This research can provide a baseline for stick-slip reduction technology optimization.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.364

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 integrity0.0000.001
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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