Investigation of the effects of liquid medium and electrical parameters on hydraulic-electric pulsed discharge rock-breaking
Bibliographic record
Abstract
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%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".