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Record W4407385735 · doi:10.1139/cgj-2024-0421

Investigation of the effects of liquid medium and electrical parameters on hydraulic-electric pulsed discharge rock-breaking

2025· article· en· W4407385735 on OpenAlexvenueno aff
Weiji Liu, Ling He

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
FundersNatural Science Foundation of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsGeotechnical engineeringElectric dischargeWater dischargeGeologyMaterials scienceChemistryElectrode

Abstract

fetched live from OpenAlex

The hydraulic-electric pulsed discharge (HEPD) rock-breaking technology can generate plasma channels in liquid media and shock waves from the plasma channels to break rocks. Since the HEPD rock-breaking technology involves a multi-physical field coupling rock-breaking mechanism that is difficult to describe, the theoretical modeling of this technology is less studied. In this paper, we analyze the HEPD rock-breaking process by combining numerical models and experiments and establish a multi-physics numerical HEPD simulation model, which realizes the whole process of HPED rock-breaking from the five-field coupling. The obtained numerical simulations and indoor experiments show that the HEPD process is divided into three phases: the breakdown channel formation phase, the plasma channel formation phase, and the plasma shockwave bursting phase. With the increase of liquid medium conductivity, the rock’s maximum penetration depth decreases, the rock’s maximum damage depth increases, and the trend of rock crushing pits appears to decrease. The larger the liquid medium breakdown energy consumption, the faster the electric breakdown in the liquid medium is generated, which reduces the breakdown delay. When the liquid medium conductivity increases from 0.0125 to 5 S/m, the liquid medium breakdown energy consumption increases by 13.87% and the breakdown delay decreases by 13.74%.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.351

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 integrity0.0000.001
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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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