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To study the influence of cyclic stress on the nonlinear behavior of saturated sandstone, the residual strain properties and energy dissipation characteristics of the sandstone under tiered cyclic loading were experimentally investigated. The axial/radial residual deformation and energy dissipation characteristics of sandstone at different cyclic stress stages were analyzed in detail. By combining the mathematical statistics, fluctuation coefficients of the residual strain and energy dissipation, and correlation coefficients of axial/radial residual strain and energy dissipation were defined to describe the process. It was determined that these newly defined physical variables were closely related to the elastic-plastic state (or instability failure state) of the rock. [ABSTRACT FROM AUTHOR]

Effective thermal conductivity (ETC) is an important parameter in packed-bed systems and is affected significantly by packing structure. Heat flow through a packed bed can be divided into three parallel paths: the solid-fluid-solid path, the solid-solid path and the fluid path. The models to evaluate ETC in packed beds for the first two paths were previously developed. In the present work, a comprehensive model to consider the heat transfer through the voids of a randomly-packed bed is further developed using the Delaunay tessellation. The results show that heat conduction through the fluid-filled voids becomes significant when the solid-to-fluid conductivity ratio decreases to a certain level (<5). The effect of packing structure on the ETC of packed beds is then investigated based on the packing generated by the discrete element method (DEM). Four cases with different heat transfer mechanisms or paths are considered. The results indicate that the probability density distribution of heat flow is generally dependent on the packing structure, represented by porosity. With an increase in porosity, the heat conduction and radiation via the solid path decrease, while the heat conduction via the fluid-filled voids increases. Unlabelled Image • Delaunay network model is extended to calculate heat flow through fluid voids. • Heat conduction through voids becomes significant when k s /k f is smaller than 5. • Probability density distribution of heat flow is dependent on the packing structure. • With an increase in porosity, the heat conduction via the fluid-filled voids increases. [ABSTRACT FROM AUTHOR]

Abstract: The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification. [Copyright &y& Elsevier]

This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization. [ABSTRACT FROM AUTHOR]

Based on Fritzke's GCS (Growing Cell Structures), we present here a new incremental self-organising neural network, the Externally Growing Cell Structures (EGCS). Our goals are to speed up the convergence and to improve the generalisation performance. The mechanism of internally growing cells in EGCS is the same as in GCS. However, when the Maximum Resource Vertex (MRV) or the Maximum Error Vertex (MEV) is a boundary node, the new cell is grown externally. Simulation results on neural network benchmarks, two-spiral problem and sonar mine/rock separation, indicate that EGCS performs better than the original GCS, measured by classification rate and the required number of epochs. As a new classification and regression method, the EGCS for Data Evaluation of Chemical Gas Sensors is introduced. [ABSTRACT FROM AUTHOR]

• Wear behaviour in a lab-scale HPGR is investigated. • Wear model and particle breakage are included in the DEM model. • Concave wear pattern was formed during the dynamic wear evolution. • Worn rolls reduce power draw of the HPGR mill, no effect on throughput. • Particle breakage and fracture energy with worn roll are analysed. Wear due to compression and abrasion from particles is a critical issue for high pressure grinding rolls (HPGR) mills. Understanding the evolution of wear provides useful insight on its mechanisms and helps to mitigate the issue so mills are operated at their optimal states. This work presented numerical simulations based on the discrete element method (DEM) to analyse the formation and evolution of wear of a lab-scale HPGR mill and to investigate how wear affects mill performance and particle breakage. By coupling the DEM model with the Archard wear model and surface re-meshing, the simulated wear patterns were comparable with those observed in practice, showing a parabolic wear profile wear along the rolls with more severe wear in the centre. In addition, the rear part of the stud, compared with the middle and front parts, experienced more significant wear due to the combined effect of compression and abrasion. While increasing wear had no visible effect on throughput, it reduced mill power draw due to weaker compressive force from the rolls to particles. Furthermore, analysis on particle breakage showed that increasing wear reduced particle breakage and produced coarser products. Increasing wear also caused less damage to particles, suggesting more cycles are required to achieve targeted products. [ABSTRACT FROM AUTHOR]

The feeding hopper is frequently used in grinding cement clinker to ensure a stable and homogeneous feed of particles. However, due to the large variances in size, the clinker particles can segregate significantly and cause undesirable fluctuations in the subsequent grinding process. This work investigates the flow and segregation of particles in mill-feeding hoppers using the experimental PIV technique and the FEM simulation. The results demonstrate the different zones of fine and coarse particles, corresponding to the flow patterns of the hopper, are affected by the material parameters. Particularly, it is found that the geometric design of the hopper also has a great influence on the flow and segregation. An optimized hopper design using FEM is shown to effectively alleviate the segregation problem in the mill feeding hopper. [Display omitted] • Using sensors, PIV and Screening to collect data from scale-model experiment. • FEM slumlation can effectively predict the hopper flow and segregation. • Bond of small particles divides the stagnant the flow zone around outlet. • Changes in the geometric of the hopper have a greater impact on segregation. [ABSTRACT FROM AUTHOR]