Stochastic modelling of rock fragment flow in caving mines

The movement pattern of rock fragments under gravity was simulated with a stochastic model which was developed based on Litwiniszyn’s stochastic theory. The downward rock fragment flow is represented in terms of an equivalent upward-biased random flight of voids originating at the draw point. In a steady state, the upward-biased random flight of voids is described with a differential equation which is formally identical to the diffusion equation. By introducing a density change from no-flow zone to flow zone, the model delineates the nonsteady state where the flow zone is being developed during the drawing process. A linear function was introduced in the model to compensate draw shape discrepancy. The parameters in the linear function are material specific. The model was implemented in a computer program employing the finite difference method. The agreement between the stochastic model and a large scale physical model was excellent.

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Published
2026-02-18
DOI
https://doi.org/10.1201/9781003761365-101
Primary Topic
Hydraulic Fracturing and Reservoir Analysis
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article
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article

Stochastic modelling of rock fragment flow in caving mines

G. Chen
Hydraulic Fracturing and Reservoir Analysis
article

Stochastic modelling of rock fragment flow in caving mines

G. Chen
article en

Abstract

The movement pattern of rock fragments under gravity was simulated with a stochastic model which was developed based on Litwiniszyn’s stochastic theory. The downward rock fragment flow is represented in terms of an equivalent upward-biased random flight of voids originating at the draw point. In a steady state, the upward-biased random flight of voids is described with a differential equation which is formally identical to the diffusion equation. By introducing a density change from no-flow zone to flow zone, the model delineates the nonsteady state where the flow zone is being developed during the drawing process. A linear function was introduced in the model to compensate draw shape discrepancy. The parameters in the linear function are material specific. The model was implemented in a computer program employing the finite difference method. The agreement between the stochastic model and a large scale physical model was excellent.

University of Alaska Fairbanks (US)
Openalex Percentile: Top 98%
Hydraulic Fracturing and Reservoir Analysis
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