Validation of the gpu based blaze-dem framework for hopper discharge

Understanding the dynamical behavior of particulate materials is extremely important to many industrial processes, with typical applications that range from hopper flows in agriculture to tumbling mills in the mining industry. The discrete element method (DEM) has become the defacto standard to simulate particulate materials. The DEM is a compu- tationally intensive numerical approach that is limited to a moderate amount (thousands) of particles when considering fully coupled densely packed systems modeled by realistic par- ticle shape and history dependent constitutive relationships. A large number (millions) of particles can be simulated when the coupling between particles is relaxed to still accurately simulated lesser dense systems. Massively large scale simulations (tens of millions) are possi- ble when particle shapes are simplified, however this may lead to oversimplification when an accurate representation of the particle shape is essential to capture the macroscopic transport of particulates. Polyhedra represent the geometry of most convex particulate materials well and when combined with appropriate contact models predicts realistic mechanical behavior to that of the actual system. Detecting collisions between polyhedra is computationally ex- pensive often limiting simulations to only hundreds of thousands of particles. However, the computational architecture e.g. CPU and GPU plays a significant role on the performance that can be realized. The parallel nature of the GPU allows for a large number of simple independent processes to be executed in parallel. This results in a significant speed up over conventional implementations utilizing the Central Processing Unit (CPU) architecture, when algorithms are well aligned and optimized for the threading model of the GPU. We recently introduced the BLAZE-DEM framework for the GPU architecture that can model millions of pherical and polyhedral particles in a realistic time frame using a single GPU. In this paper we validate BLAZE-DEM for hopper discharge simulations. We firstly compare the flow-rates and patterns of polyhedra and spheres obtained with experiment to that of DEM. We then compare flow-rates between spheres and polyhedra to gauge the effect of particle shape. Finally we perform a large scale DEM simulation using 16 million articles to illustrate the capability of BLAZE-DEM to predict bulk flow in realistic hoppers

Parallel Multiscale Contact Dynamics for Rigid Non-spherical Bodies

The simulation of large numbers of rigid bodies of non-analytical shapes or vastly varying sizes which collide with each other is computationally challenging. The fundamental problem is the identification of all contact points between all particles at every time step. In the Discrete Element Method (DEM), this is particularly difficult for particles of arbitrary geometry that exhibit sharp features (e.g. rock granulates). While most codes avoid non-spherical or non-analytical shapes due to the computational complexity, we introduce an iterative-based contact detection method for triangulated geometries. The new method is an improvement over a naive brute force approach which checks all possible geometric constellations of contact and thus exhibits a lot of execution branching. Our iterative approach has limited branching and high floating point operations per processed byte. It thus is suitable for modern Single Instruction Multiple Data (SIMD) CPU hardware. As only the naive brute force approach is robust and always yields a correct solution, we propose a hybrid solution that combines the best of the two worlds to produce fast and robust contacts. In terms of the DEM workflow, we furthermore propose a multilevel tree-based data structure strategy that holds all particles in the domain on multiple scales in grids. Grids reduce the total computational complexity of the simulation. The data structure is combined with the DEM phases to form a single touch tree-based traversal that identifies both contact points between particle pairs and introduces concurrency to the system during particle comparisons in one multiscale grid sweep. Finally, a reluctant adaptivity variant is introduced which enables us to realise an improved time stepping scheme with larger time steps than standard adaptivity while we still minimise the grid administration overhead. Four different parallelisation strategies that exploit multicore architectures are discussed for the triad of methodological ingredients. Each parallelisation scheme exhibits unique behaviour depending on the grid and particle geometry at hand. The fusion of them into a task-based parallelisation workflow yields promising speedups. Our work shows that new computer architecture can push the boundary of DEM computability but this is only possible if the right data structures and algorithms are chosen

Modeling, Characterizing and Reconstructing Mesoscale Microstructural Evolution in Particulate Processing and Solid-State Sintering

abstract: In material science, microstructure plays a key role in determining properties, which further determine utility of the material. However, effectively measuring microstructure evolution in real time remains an challenge. To date, a wide range of advanced experimental techniques have been developed and applied to characterize material microstructure and structural evolution on different length and time scales. Most of these methods can only resolve 2D structural features within a narrow range of length scale and for a single or a series of snapshots. The currently available 3D microstructure characterization techniques are usually destructive and require slicing and polishing the samples each time a picture is taken. Simulation methods, on the other hand, are cheap, sample-free and versatile without the special necessity of taking care of the physical limitations, such as extreme temperature or pressure, which are prominent issues for experimental methods. Yet the majority of simulation methods are limited to specific circumstances, for example, first principle computation can only handle several thousands of atoms, molecular dynamics can only efficiently simulate a few seconds of evolution of a system with several millions particles, and finite element method can only be used in continuous medium, etc. Such limitations make these individual methods far from satisfaction to simulate macroscopic processes that a material sample undergoes up to experimental level accuracy. Therefore, it is highly desirable to develop a framework that integrate different simulation schemes from various scales to model complicated microstructure evolution and corresponding properties. Guided by such an objective, we have made our efforts towards incorporating a collection of simulation methods, including finite element method (FEM), cellular automata (CA), kinetic Monte Carlo (kMC), stochastic reconstruction method, Discrete Element Method (DEM), etc, to generate an integrated computational material engineering platform (ICMEP), which could enable us to effectively model microstructure evolution and use the simulated microstructure to do subsequent performance analysis. In this thesis, we will introduce some cases of building coupled modeling schemes and present the preliminary results in solid-state sintering. For example, we use coupled DEM and kinetic Monte Carlo method to simulate solid state sintering, and use coupled FEM and cellular automata method to model microstrucutre evolution during selective laser sintering of titanium alloy. Current results indicate that joining models from different length and time scales is fruitful in terms of understanding and describing microstructure evolution of a macroscopic physical process from various perspectives.Dissertation/ThesisDoctoral Dissertation Materials Science and Engineering 201

Applications of discrete element method in modeling of grain postharvest operations

Grain kernels are finite and discrete materials. Although flowing grain can behave like a continuum fluid at times, the discontinuous behavior exhibited by grain kernels cannot be simulated solely with conventional continuum-based computer modeling such as finite-element or finite-difference methods. The discrete element method (DEM) is a proven numerical method that can model discrete particles like grain kernels by tracking the motion of individual particles. DEM has been used extensively in the field of rock mechanics. Its application is gaining popularity in grain postharvest operations, but it has not been applied widely. This paper reviews existing applications of DEM in grain postharvest operations. Published literature that uses DEM to simulate postharvest processing is reviewed, as are applications in handling and processing of grain such as soybean, corn, wheat, rice, rapeseed, and the grain coproduct distillers dried grains with solubles (DDGS). Simulations of grain drying that involve particles in both free-flowing and confined-flow conditions are also included. Review of existing literature indicates that DEM is a promising approach in the study of the behavior of deformable soft particulates such as grain and coproducts and it could benefit from the development of improved particle models for these complex-shaped particles

A strategy to determine DEM parameters for spherical and non-spherical particles

In Discrete element method (DEM) simulations the choice of appropriate contact parameters is significant to obtain reasonable results. Particularly, for the determination of DEM parameters for non-spherical particles a general straightforward procedure is not available. Therefore, in a first step of the investigation here, methods to obtain the friction and restitution coefficients experimentally for single particles [Polyoxymethylene (POM) spheres and quartz gravel] will be introduced. In the following, these predetermined DEM coefficients are used as initial values for the adjustment of bulk simulations to respective experiments. In the DEM simulations, the quartz gravel particles are represented by non-spherical particles approximated by clustered spheres. The best fit approximation of the non-spherical particles is performed automatically by a genetic algorithm. In order to optimize the sliding and rolling friction coefficients for DEM simulations, the static and dynamic angle of repose are determined from granular piles obtained by slump tests and rotating drum experiments, respectively. Additionally, a vibrating plate is used to obtain the dynamic bed height which is mainly influenced by the coefficient of restitution. The adjustment of the results of the bulk simulations to the experiments is conducted automatically by an optimization tool based on a genetic algorithm. The obtained contact parameters are later used to perform batch-screening DEM simulations and lead to accurate results. This underlines the applicability of the in parts automated strategy to obtain DEM parameters for particulate processes like screening.DFG, SPP 1679, Dynamische Simulation vernetzter Feststoffprozess

Constitutive modeling of dense granular flow based on discrete element method simulations

The vision of this research study is to exploit physical insights obtained through microscale simulations to develop better and accurate macroscale constitutive models in different regimes of granular flow. Development of these constitutive models at macroscale that incorporates microscale particle interactions, need tools such as, DEM (discrete element method) simulations, to probe microscale behavior. These DEM simulations are helpful in understanding the granular physics and mesoscale descriptors that link microscale particle interaction to macroscopic constitutive behavior. In order to attain the primary goal of development of constitutive models, DEM simulations are validated with the experiments in a Couette shear device. It is found that DEM simulations are capable of capturing the regime transition from quasi-static to the intermediate behavior as observed in the experiments. Influence of microscale parameters on granular rheology is demonstrated using comprehensive regime map established using DEM data. Existence of a third stable granular phase is discovered that is neither completely solid-like nor completely fluid-like. A new modified form of the free energy density function is proposed to capture this third stable granular phase observed in the DEM simulations. Further, a constitutive model based on the order parameter (OP) framework is refined, and a linear model with new model coefficient extracted from data of 3D DEM simulations of homogeneously sheared granular flows is proposed, which is denoted as refined order parameter (ROP) model. Performance of this ROP model along with other existing constitutive models is assessed in the different regimes of granular flow. It is found that the intermediate regime poses significant challenge to predictive capability of the constitutive models. In order to capture this complex rheological behavior of the intermediate regime a constitutive model based on mesoscale descriptors (such as the coordination number and the fabric tensor) that links microscale particle interactions to the macroscale behavior is developed. It is shown that the proposed contact stress model is capable of capturing the correct scaling of the stress with the shear rate even in the intermediate and dense regime of granular flow

Dynamic Response of Silo Supporting Structure under Pulsating Loads

Silo quaking is a time varying mass structural dynamic problem and the existence of a silo quake spectrum is confirmed in this research. The outcomes confirm that silo quaking can be prevented by providing the silo structure with sufficient mass, stiffness and damping to counterbalance the effects of pulsating forces and mass losses. Furthermore, dynamic structural analysis algorithms and software need to be developed to solve time varying mass structural dynamic problems

Discrete element simulations and constitutive modeling of dense granular flows

The aim of this study is to understand and explore the rheology of dense granular flow, in particular the phenomenon of regime transition, using both microscale DEM (discrete element method) simulations and macroscale modeling methods. The rheology of dense sheared granular flow in a Couette device is simulated using DEM. It is found that DEM simulations are capable of capturing the regime transition from quasi-static to intermediate behavior. A constitutive model based on the order parameter (OP) framework is refined, and a linear model with new model coefficients extracted from data of 3D DEM simulations of homogeneously sheared granular flows is proposed. The performance of different constitutive models including the refined OP model is tested in the intermediate regime of granular flows. None of these models captures the correct scaling of shear stress with shear rate in the intermediate regime, leading to the conclusion that further development of constitutive models is needed for dense granular flow in the intermediate regime

Examining the Relationship Between Lignocellulosic Biomass Structural Constituents and Its Flow Behavior

Lignocellulosic biomass material sourced from plants and herbaceous sources is a promising substrate of inexpensive, abundant, and potentially carbon-neutral energy. One of the leading limitations of using lignocellulosic biomass as a feedstock for bioenergy products is the flow issues encountered during biomass conveyance in biorefineries. In the biorefining process, the biomass feedstock undergoes flow through a variety of conveyance systems. The inherent variability of the feedstock materials, as evidenced by their complex microstructural composition and non-uniform morphology, coupled with the varying flow conditions in the conveyance systems, gives rise to flow issues such as bridging, ratholing, and clogging. These issues slow down the conveyance process, affect machine life, and potentially lead to partial or even complete shutdown of the biorefinery. Hence, we need to improve our fundamental understanding of biomass feedstock flow physics and mechanics to address the flow issues and improve biorefinery economics. This dissertation research examines the fundamental relationship between structural constituents of diverse lignocellulosic biomass materials, i.e., cellulose, hemicellulose, and lignin, their morphology, and the impact of the structural composition and morphology on their flow behavior. First, we prepared and characterized biomass feedstocks of different chemical compositions and morphologies. Then, we conducted our fundamental investigation experimentally, through physical flow characterization tests, and computationally through high-fidelity discrete element modeling. Finally, we statistically analyzed the relative influence of the properties of lignocellulosic biomass assemblies on flow behavior to determine the most critical properties and the optimum values of flow parameters. Our research provides an experimental and computational framework to generalize findings to a wider portfolio of biomass materials. It will help the bioenergy community to design more efficient biorefining machinery and equipment, reduce the risk of failure, and improve the overall commercial viability of the bioenergy industry

Modelling, safety verification and design of discrete/continuous processing systems.

Imperial Users onl