Numerical Investigation of a Hydrosplitting Fracture and Weak Plane Interaction Using Discrete Element Modeling

Abstract

Water inrush caused by hydrosplitting is an extremely common disaster in the engineering of underground tunnels. In this study, the propagation of fluid-driven fractures based on an improved discrete element fluid-solid coupling method was modeled. First, the interactions between hydrosplitting fractures (HFs) and preexisting weak planes (WPs) with different angles were simulated considering water pressure in the initial fracture. Second, the influence of the in situ stress ratio and the property of WPs were analyzed, and corresponding critical pressure values of different interactions were calculated. Lastly, the maximum principal stress and maximum shear stress variation inside the pieces were reproduced. The following conclusions can be drawn: (1) Five different types of interaction modes between HFs and natural WPs were obtained, prone to crossing the WPs under inclination of 90°. (2) The initiation pressure value decreased with an increased in situ stress ratio, and the confining stress status had an effect on the internal principal stress. (3) During HFs stretching in WPs with a high elastic modulus, the value of the maximum principal stress was low and rose slowly, and the maximum shear stress value was smaller. Through comprehensive analysis, the diversity of the principal stress curves is fundamentally determined by the interaction mode between HFs and WPs, which are influenced by the variants mentioned in the paper. The analysis provides a better guideline for understanding the failure mechanism of water gushing out of deep buried tunnel construction and cracking seepage of high head tunnels.

Behavior Data Analysis of English Learners Based on Discrete Dynamic System Modeling Method

“…J N t(0)t, nCq(0), c(1), ..., cN − 1 􏼁 􏼉. 􏼈 (11) rough the expressions of the above two algorithms, the performance of fuzzy Petri net discrete dynamic modeling is systematically optimized. Based on the optimized system, the performance of the optimized fuzzy Petri net discrete dynamic system and the ordinary Petri net discrete dynamic system is compared by adding English behavior sample data, as shown in Figure 7.…”

“…Fuzzy Petri net discrete dynamic system establishes a fuzzy neural network to express knowledge and self-learning training and finds the internal attributes of the system through fuzzy neural network. After the above "training," the data with adjacent meaning can be neutralized and then changed to the same meaning [11]. e transition conditions of the next data state can be deduced from the existing data state or the State Library set of the next data can be obtained from the known data state [12].…”

Experimental Study on Mechanical Properties and Failure Laws of Granite with Artificial Flaws under Coupled Static and Dynamic Loads

“…A series of core slices were identified using optical microscopy. The rock sample composition is approximately 34% plagioclase, 27% potassium feldspar, 30% granular quartz, and 6% flaky biotite; and accessory minerals, such as calcite, white mica, and brown epidote, and so on, as shown in Figure 1 [ 17 , 18 ]. Rock cores were processed into cuboid samples, with a size of 45 mm × 45 mm × 20 mm (length × height × thickness), were divided into complete samples and crack samples with double parallel joints.…”

Hydraulic Dynamic Calculation and Simulation

“…Paper [8] investigated the water inrush caused by hydrosplitting, an important disaster in the engineering of underground tunnels. The propagation of fluid-driven fractures based on an improved discrete element fluid-solid coupling method was modeled.…”