Understanding the influence of shear displacement on heat transport in rock fractures is important for evaluating and optimizing heat extraction in enhanced geothermal systems. This study presents quantitative characterization of the heat transfer evolution in single fractures subject to shear displacement, aiming to demonstrate the impact of shear displacement on heat transport in natural rock fractures. The direct shear of rock fractures is directly simulated using the finite element method and the Mohr-Coulomb yield criterion. The shear simulation method is validated against laboratory shear test data from the literature. Shear simulations under different mechanical conditions, including different normal stresses and shear displacements, are conducted. The sheared fractures are then used to simulate fluid flow and heat transfer processes by directly solving the Navier-Stokes equations and the heat transport equation. The results show that shear displacements can cause significant changes in fracture aperture and subsequently enhance the heterogeneity of flow fields and temperature fields in the fracture. The heat transfer coefficient increases with the increasing of normal stress and Peclet number, while it decreases with the increase of shear displacement. The plastic deformation of fracture surfaces can significantly affect the heat transfer rate. The findings can help understand the heat transfer characteristics in natural rock fractures.
Key Points PDE formulation of a free‐surface seepage problem on the entire fracture network Variational inequality ( VI ) formulation for unconfined seepage problem The numerical solution procedure based on the discrete VI formulation
The aim of this study was to investigate the optimization of cut-blasting methods for deep rock masses under high in situ stress. Several plane fluid–structure interaction (FSI) models were established, and the blasting effects using common cut-blasting methods were analyzed. Considering the damage range of the rock mass and the blast-induced vibration after blasting, the cut-blasting method was selected as suitable for deep rock masses under high in situ stress. Subsequently, the cut-blasting parameters, including blasthole spacing, blasthole diameter, and the distance between the empty hole and the first cut-blasting hole, were optimized through the crack connectedness between the blastholes and the fractal dimension of the damaged rock mass. The results showed that the pentagonal cut-blasting method is more suitable for deep rock masses compared with other methods and the blasthole spacing should be reduced to resist any inhibitory effects from the increasing in situ stress. For in situ nonhydrostatic stress conditions, it is reasonable to choose a wider blasthole spacing in the direction of the major principal stress and a narrower one in the direction of the minor principal stress. Under high in situ stress, the joint optimization of blasthole spacing and blasthole diameter is recommended in order to avoid the poor cutting effect caused by too-narrow spacing. In addition, the formula for calculating the location of empty holes in shallow rock-mass blasting is also applicable to deep rock masses. The partially optimized results were preliminarily verified through comparison with existing field tests. These findings offer a new approach for enhancing the blasting effect on deep rock masses and may provide valuable guidance for the design and construction of cut blasting in deep rock masses.
Most literature related to landslide susceptibility prediction only considers a single type of landslide, such as colluvial landslide, rock fall or debris flow, rather than different landslide types, which greatly affects susceptibility prediction performance. To construct efficient susceptibility prediction considering different landslide types, Huichang County in China is taken as example. Firstly, 105 rock falls, 350 colluvial landslides and 11 related environmental factors are identified. Then four machine learning models, namely logistic regression, multi-layer perception, support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide. Thirdly, three different landslide susceptibility prediction (LSP) models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility: (i) united method, which combines all landslide types directly; (ii) probability statistical method, which couples analyses of susceptibility indices under different landslide types based on probability formula; and (iii) maximum comparison method, which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides. Finally, uncertainties of landslide susceptibility are assessed by prediction accuracy, mean value and standard deviation. It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County. The united method has the best susceptibility prediction performance, followed by the probability method and maximum susceptibility method. More cases are needed to verify this result in-depth. LSP considering different landslide types is superior to that taking only a single type of landslide into account.
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