Abstract. Crystalline rocks are inherently more or less densely permeated by fractures at very different scales. They often represent the hydraulically dominant flow paths compared to low-permeable matrix. Consequently, finding potential sites for high-level waste repositories in crystalline host rock is challenging. Fracture inter-connectivity significantly affects geomechanical integrity and ...

Fracture network modelling and a hydrological evaluation were performed in well more than 900 m deep that penetrated the Boda Claystone Formation, potential host rock for high-level nuclear waste disposal facilities Hungary. The fracture geometry was generated with discrete algorithm, which permeability porosity of system can be calculated if aperture fractures is known. estimated via an calibr...

Analysis of the stability slopes in fractured rock masses is not trivial and fraught with uncertainty risk. A DFN-DEC model (Discrete Fracture Network-Distinct Element Code) based on MATLAB platform developed to evaluate random fractures. Then, influences mean values geometric characteristics (i.e., trace length T, dip D, spacing S) for both horizontal (denoted 1) inclined 2) fractures slope ar...

The Doyle-Fuller-Newman framework is the most popular physics-based continuum-level description of chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range battery designs chemistries, provides an effective balance between detail, needed capture key microscopic mechanisms, simplicity, solve governing equations at rel...

Abstract Understanding the natural fracture network is essential for geothermal-related investigations. However, geometrical attributes depend on scale of observation. Therefore, a multiscale characterization to ensure that heat and flow simulations are based stochastically generated discrete models representative system observed. The objective this project was understand effect data performanc...

Scanline observation is known to introduce an angular bias into the probability distribution of orientation in three-dimensional space. In this paper, numerical solutions expressing the functional relationship between the scanline-observational distribution (in one-dimensional space) and the inherent distribution (in three-dimensional space) are derived using probability theory and calculus und...

We present Dynamic Sampling Convolutional Neural Networks (DSCNN), where the position-specific kernels learn from not only the current position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. And the kernels are further factorized to reduce parameters. Th...

This paper examines the identification of the parameters of the Doyle-Fuller-Newman electrochemistry-based Lithium-ion battery model from voltage and current cycling data. The battery used in this study has a lithium iron phosphate cathode chemistry intended for high-power applications such as plug-in hybrid electric vehicles. The variables optimized for model identification include parameteriz...