Evidence map›Paper›PMID 42294183›Full record

ArticleACS omega2026

Experimental Investigation on the Rock-Breaking Efficiency of High-Pressure Jet Drill Rod Perforation in Sandstone Roof.

Haoze Li, Peiheng Yan, Qingying Cheng, Jieke Chen, Shuliang Chen, Tuo Dong, Binghong Li, Bingxiang Huang

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Haoze LiSchool of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.ORCID https://orcid.org/0000-0001-8454-3480
Peiheng YanSchool of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Qingying ChengSchool of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Jieke ChenSchool of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Shuliang ChenSchool of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Tuo DongSchool of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Binghong LiState Key Laboratory for Fire Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Bingxiang HuangState Key Laboratory for Fire Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.ORCID https://orcid.org/0000-0003-3399-0285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Against the backdrop of global energy transition and carbon neutrality goals, the demand for efficient, safe, and sustainable exploitation of coal resources has grown increasingly urgent. High-pressure jet rock-breaking technology has emerged as a key enabling solution for directional rock mass weakening, gob-side entry retention, and other critical coal mine engineering projects. However, existing theoretical and experimental studies on jet rock breaking have struggled to fully reveal the evolutionary mechanism of rock fragmentation during down-the-hole jet drilling operations. To directly observe the rock-breaking effect of the jet drill rod, this study established a similarity simulation test system to investigate the rock-breaking efficiency of high-pressure jet drill rods in sandstone roof strata under simulated in situ conditions. The results indicate that the fluid transport process during jet rock breaking can be divided into four distinct stages: initial jet formation, early rock breaking, intermediate rock breaking, and stable rock breaking. Correspondingly, the sandstone surface damage morphology evolves through five sequential phases: surface particle detachment, microfracturing of the borehole wall, steady penetration depth increase, positive feedback of energy accumulation for rock fragmentation, and large-scale surface instability. As jet pressure increases, the damage mode transitions from localized minor damage to large-scale disintegration, with a critical rock-breaking pressure of approximately 3.25 MPa. A longer duration of pure water jet impact enables cumulative energy to drive damage propagation, expanding from localized boreholes to full-surface collapse. The influence of target standoff distance exhibits a nonmonotonic trend, first increasing and then decreasing, with an optimal value of 2 mm; beyond this threshold, jet diffusion leads to diminished damage efficiency. The nozzle diameter exerts a strong initial influence on sandstone surface damage that gradually weakens, with 1.2 mm identified as the optimal size; larger diameters cause rapid dissipation of jet energy. A more oblique incident angle results in more intense and extensive shear damage. The employment of high-abrasion-resistant abrasives triggers large-scale crushing, with prolonged abrasive impact time exacerbating surface damage and achieving a drilling efficiency more than 2.5 times that of pure water jets. Compared with multiple linear regression, neural network models demonstrate superior performance in capturing the nonlinearity of sandstone surface damage, providing more accurate predictions with smaller deviations between the predicted and experimental values. This study offers important theoretical guidance for the green and efficient exploitation of coal resources.

Identifiers

PMID42294183
PMCPMC13261444

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.