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Record W2777708303 · doi:10.1063/1.4993432

A multi-particle crushing apparatus for studying rock fragmentation due to repeated impacts

2017· article· en· W2777708303 on OpenAlexaff
Sheng Huang, B. Mohanty, Kaiwen Xia

Bibliographic record

VenueReview of Scientific Instruments · 2017
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrusherPiston (optics)Split-Hopkinson pressure barMaterials scienceParticle sizeParticle-size distributionParticle (ecology)MechanicsWork (physics)Bar (unit)ComminutionPiston rodGeotechnical engineeringGeologyMechanical engineeringCylinderComposite materialEngineeringMetallurgyStrain ratePhysicsOptics

Abstract

fetched live from OpenAlex

Rock crushing is a common process in mining and related operations. Although a number of particle crushing tests have been proposed in the literature, most of them are concerned with single-particle crushing, i.e., a single rock sample is crushed in each test. Considering the realistic scenario in crushers where many fragments are involved, a laboratory crushing apparatus is developed in this study. This device consists of a Hopkinson pressure bar system and a piston-holder system. The Hopkinson pressure bar system is used to apply calibrated dynamic loads to the piston-holder system, and the piston-holder system is used to hold rock samples and to recover fragments for subsequent particle size analysis. The rock samples are subjected to three to seven impacts under three impact velocities (2.2, 3.8, and 5.0 m/s), with the feed size of the rock particle samples limited between 9.5 and 12.7 mm. Several key parameters are determined from this test, including particle size distribution parameters, impact velocity, loading pressure, and total work. The results show that the total work correlates well with resulting fragmentation size distribution, and the apparatus provides a useful tool for studying the mechanism of crushing, which further provides guidelines for the design of commercial crushers.

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)0.001
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 score0.534
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.056
GPT teacher head0.332
Teacher spread0.276 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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