Contact force distribution in a polydisperse granular assembly under uniaxial compaction is investigated using DEM simulations. In general, the contact force network generated in a compacted granular ...assembly is inhomogeneous. The effect of the relative particle size distribution on the nature of the contact force network in a compacted polydisperse granular assembly is investigated. The probability distribution and cumulative distribution of the normal contact forces for particles of different radii in a polydisperse granular assembly are analyzed. Distribution of the coordination number and the maximum force on each particle in a group of particles of the same size are also investigated. The study reveals that the larger particles have a higher probability of experiencing stronger contact forces than the smaller particles. The smaller particles in the assembly experience a lower maximum force and coordination number when compared to the larger particles. The small particles are observed to escape from the force chains by occupying the voids formed amongst the larger particles in the assemblies with considerable particle size variation. The particle size effect on the contact force distribution reduces as the size difference between the particles reduces. The knowledge of the distribution of the contact forces in a polydisperse assembly helps in estimating the probability of crushing amongst particles of different sizes.
This paper focuses on optimisation of process parameters of the turning operation, using artificial intelligence techniques such as support vector regression (SVR) and artificial neural networks ...(ANN) integrated with genetic algorithm (GA). The model is trained using the turning parameters as the input and corresponding surface roughness, tool wear and power required as the output. Data, obtained from conducting experiments is analysed using support vector machine (SVM) and artificial neural network. SVM, a nonlinear model, is learned by linear learning machine by mapping into high-dimensional kernel-induced feature space. The genetic algorithm is integrated with these to find the optimum from the response surface generated. The results are compared with those obtained by integrating GA with traditional models like response surface methodology (RSM) and regression analysis (RA). This paper illustrates the impact that techniques based on artificial intelligence have on optimising processes.
Packed structures are an essential part of nuclear reactors, food, chemical, transport, and process industries. Since the safety and quality of products in the packed structures is of high priority, ...identifying critical failure spots in packed structures is of utmost importance. The present study aims to identify critical spots in the hexagonally packed structures under mechanical loads in the presence of defects. The role of defects in the formation of force networks is also investigated in this work. The granular mechanics approach is used to analyze the analogous force pattern formation in packed structures. Discrete element method (DEM) is used to simulate the particle interaction in the granular assembly. The hexagonal packing, in X-Y plane, is created by stacking the horizontal contacting particle chains in X-direction, and thus creating inclined contact chains in the Y-direction. Hexagonal packings display two stable force network formations corresponding to compression along X and Y-direction. The effect of point defect and stacking fault on the force network is investigated. The presence of point defect is shown to induce high force concentration near the defect zone. When the assembly is compressed along X-direction, force redistribution at the defect zone increases the force levels in inclined force chains. When the assembly is compressed along Y-direction, the point defect induces zones with lesser force levels. Further, the study explores various levels of force zones induced in the system. The effect of the presence of multiple point defects in the system is analyzed further. The distance between two point defects and their loading direction induces a different set of force chains. Stacking fault is found to induce strong vertical force chains at the defect zone, unlike point defect. However, multiple stacking faults affect only the horizontal force chains near the defect zone. The present study highlights the formation of critical spots as well as lower force zones and also provides useful insights to design efficient packing structures.
Graphical Abstract
Austenitic Stainless Steel grade 304L and 316L are very important alloys used in various high temperature applications, which make it important to study their mechanical properties at elevated ...temperatures. In this work, the mechanical properties such as ultimate tensile strength (UTS), yield strength (YS), % elongation, strain hardening exponent (n) and strength coefficient (K) are evaluated based on the experimental data obtained from the uniaxial isothermal tensile tests performed at an interval of 50°C from 50°C to 650°C and at three different strain rates (0.0001, 0.001 and 0.01s−1). Artificial Neural Networks (ANN) are trained to predict these mechanical properties. The trained ANN model gives an excellent correlation coefficient and the error values are also significantly low, which represents a good accuracy of the model. The accuracy of the developed ANN model also conforms to the results of mean paired t-test, F-test and Levene's test.
Artificial neural network (ANN), a machine learning technique, is employed to predict the effective thermal conductivity of granular assemblies in the presence of a stagnant gas. ANN is trained with ...the help of estimated thermal conductivities calculated through resistor network (RN) model. RN model considers the effect of the presence of stagnant gas and the gas pressure (Smoluchowski effect) for the calculation of effective thermal conductivity. Granular assemblies are generated and compacted through discrete element method (DEM). The ANN is trained to predict the effective thermal conductivity of a granular assembly for a set of measurable experimental parameters (stress and packing fraction) without requiring the knowledge of microstructural details (coordination numbers and overlaps) of the assembly. The predicted effective thermal conductivity values through ANN are in good agreement with the experimental results. Estimation of effective thermal conductivity through the trained ANN is much faster (few seconds compared to few hours required for DEM together with RN approach) with very good accuracy.
Deep drawing is a process of converting sheets into cup-like-shaped components. It is a complex process, and process parameters play an important role. In this paper, two deep drawing processes ...namely warm deep drawing and hydromechanical deep drawing are compared. A 20-t hydraulic press is used to draw the cups. To ensure drawing at a fixed temperature in warm forming, heaters are connected to the lower die. Experimental results are compared with finite element simulations. Coefficient of friction in simulation is calculated by inverse analysis of comparing the load displacement curves. In hydromechanical deep drawing, the process is assisted by a hydraulic counter pressure. The peak load obtained in hydromechanical deep drawing has been found to be significantly higher than the peak load in both warm and conventional deep drawing and is influenced by the clearance between punch and die and the maximum counter pressure in the fluid chamber. Limiting draw ratio (LDR), von Mises stresses, and thickness distribution in the drawn cups by varying the temperature of the blank in case of warm forming and varying the pressure in case of hydromechanical deep drawing are studied using experiments and finite element simulation.
A hierarchical approach for modelling the thermal response of large-scale granular assemblies by coupling the micro-scale particle-level thermal interactions with the macro-scale continuum system is ...proposed. The coupling is done by using a machine learning tool that is trained to replicate the effect of discrete particle nature on the macro-scale system using finite elements. A trained Artificial Neural Network (ANN) tool that can estimate the effective local thermal conductivity for each finite element considering the influence of the presence of stagnant gas in the interstitial voids, gas pressure and the granular microstructure is used. This way of hierarchical coupling using ANN eliminates the need to perform thermal discrete element simulations for each finite element at every increment by directly predicting the effective local conductivity. The proposed hierarchical approach is applied to a breeder blanket of fusion reactor that consists of more than 15 million particles to demonstrate the efficacy of the method. The influence of the drop in gas pressure across the breeder unit and the heat generation on the temperature distribution of the full-scale breeder unit is analysed numerically.
•Effect of pebble size and size-dependent crush energy on the macroscopic response.•New insight into the damage mechanism through the hydrostatic stress in the system.•Effect of pebble size and ...number distribution on the macroscopic damage.
The mechanical response of a granular system is not only influenced by the bulk material properties but also on various factors due to it’s discrete nature. The factors like topology, packing fraction, friction between particles, particle size distribution etc. influence the behavior of granular systems. For a reliable design of such systems like fusion breeder units comprising of pebble beds, it is essential to understand the various factors influencing the response of the system. Mechanical response of a binary assembly consisting of crushable spherical pebbles is studied using Discrete Element Method (DEM) which is based on particle–particle interactions. The influence of above mentioned factors on the macroscopic stress–strain response is investigated using an in-house DEM code. Furthermore, the effect of these factors on the damage in the assembly is investigated. This present investigation helps in understanding the macroscopic response and damage in terms of microscopic factors paving way to develop a unified prediction tool for a binary crushable granular assembly.
•The effective thermal conductivity of ceramic pebble beds is influenced by various parameters with inherent interactions. The packing fraction, hydrostatic stress and the stagnant gas pressure are ...predominantly varying the thermal conductivity.•The patterned regular packing formation of pebbles near the wall is observed to be advantageous in increasing the thermal conductivity (10 to 15% higher than the bulk region) due to the formation of uniformly distributed force network.•A significant increase in the ETC in desired direction by addition of secondary particles with higher thermal conductivity is achieved through layering of secondary particles amongst primary particles.
The knowledge of effective thermal conductivity (ETC) of the pebble beds is vital in designing the breeder units for fusion reactors. The ETC of pebble beds in general depends on various parameters characterizing the bulk material and microstructural properties. Establishing correlations between the ETC and the governing system parameters through experiments is very expensive and not feasible in some cases as the particle scale data is not readily available. Hence, Thermal Discrete Element Method (TDEM) is employed in this paper to investigate the influence of various parameters on the effective thermal conductivity of pebble beds. Influence of parameters such as packing fraction, stress, bed temperature, pebble size and stagnant gas pressure on the ETC is investigated for lithium ceramic pebble beds in stagnant Helium/Air. Further, the variation of the ETC from the wall region to the bulk for pebble beds formed in prismatic containers is presented. Subsequently, dual phase pebble beds have been investigated to identify configurations that will enhance the ETC of the bed.
•Effect of wall and gravity on the pebble packing structure in prismatic containers.•Evolution of packing structures through various filling strategies with an emphasis on extent of wall ...effect.•Identification of islands of regular close-packed structures within the pebble assembly.
The packing structure of the pebbles influences the thermo-mechanical behavior of the pebble beds in fusion breeder blankets. The packing structures at the wall of the container plays an important role in the extraction of heat energy out of the pebble bed to the surroundings. The packing fraction varies from the wall to the bulk region (away from wall). Hence, it is essential to understand the variation of packing fraction across the pebble bed. Packing structures were previously studied through X-ray tomography experiments and computer simulations. Computer simulations of packing were done using random closed packing algorithm, but without considering the gravity effect. Such simulations were able to predict only the wall-effect. In this paper, the packing structures are generated using discrete element method (DEM) by pouring the pebbles into prismatic containers under gravity. DEM simulations reveal that the extent of wall effect on the packing structure is not the same in all the directions. Due to the presence of gravity, the bottom wall (i.e. in the direction of gravity) induces regular packing up to 6 layers while the lateral walls influence is seen only up to 4 layers. Further, it is shown that the packing structure is also influenced by various filling strategies. The present work studies the evolution of packing structure through various filling strategies with an emphasis on the extent of wall effect on the packing structure.
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