A Prediction Model of Effective High-Pressure Water Jet Auxiliary Groove Spacing for Hard-Rock Pick Cutting
摘要
A High-pressure water jet and Pick Combined (HPC) rock-breaking technology is one of the effective ways to solve the rock-breaking efficiency of double-wheel trench cutters in hard rock strata. This paper proposed a prediction model of effective high-pressure water jet auxiliary groove spacing for hard-rock pick cutting. Laboratory tests and continuum–discontinuum element method (CDEM) numerical simulations were carried out to verify the effective auxiliary groove spacing of water jet predicted by the theoretical model. At the same time, the influence of water jet cutting spacing on the mechanical characteristics of pick rock-breaking and rock mass breaking characteristics was studied. The results show that by predicting the disturbance range of stress caused by pick cutting rock mass, the maximum spacing of free surface auxiliary pick cutting in rock mass can be obtained. The prediction model reveals the correlation between the pick-relieved cutting spacing of rock mass and the effective auxiliary cutting spacing of water jets. From the simulation and test results, it can be concluded that with the increase of water jet cutting spacing, the average rock-breaking load and energy consumption of picks continue to increase until they reach the level of the control group without water jet cutting. For granite, 25 mm is the maximum effective auxiliary groove spacing of the water jet. Within the 10–14 mm PRC range, the average load and energy consumption are significantly reduced. Within the maximum effective auxiliary groove spacing, the average load and energy consumption of the pick are significantly lower than those of the control group, which can reduce the difficulty of hard rock breaking and assist the pick to break hard rock mass. At the same time, the effective auxiliary groove spacing predicted by the model is consistent with the numerical simulation and laboratory test verification results, which shows the correctness of the predicted results of this model.