Blaze-DEMGPU: Modular high performance DEM framework for the GPU architecture

AbstractBlaze-DEMGPU is a modular GPU based discrete element method (DEM) framework that supports polyhedral shaped particles. The high level performance is attributed to the light weight and Single Instruction Multiple Data (SIMD) that the GPU architecture offers. Blaze-DEMGPU offers suitable algorithms to conduct DEM simulations on the GPU and these algorithms can be extended and modified. Since a large number of scientific simulations are particle based, many of the algorithms and strategies for GPU implementation present in Blaze-DEMGPU can be applied to other fields. Blaze-DEMGPU will make it easier for new researchers to use high performance GPU computing as well as stimulate wider GPU research efforts by the DEM community

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

Potential for interactive design simulations in discrete element modelling

This study investigates the potential for combining lower fidelity models with high performance solution strategies such as efficient graphical processing unit (GPU) based discrete element modelling (DEM) to not only do simulations faster but differently. Specifically this study investigates interactive simulation and design for which the simulation environment BlazeDEM-GPU was developed that allows researchers and engineers to interact with simulations. The initial results prove to be promising and warranting extensive research to be conducted in future which may allow for the development of alternative paradigms. In addition to the design cycle, the role that this interactive simulation and design will play in education is invaluable as an in-house corporate training tool for young engineers to actively train and develop understanding for specific industrial processes. This would also allow engineers to conduct just-in-time (JIT) simulation based assessment of processes before commencing on actual site visits, allowing for shorter and more focussed site excursions

Discrete element model study into effects of particle shape on backfill response to cyclic loading behind an integral bridge abutment

The discrete element method, implemented in a modular GPU based framework that supports polyhedral shaped particles (Blaze-DEM), was used to investigate effects of particle shape on backfill response behind integral bridge abutments during temperature-induced displacement cycles. The rate and magnitude of horizontal stress build-up were found to be strongly related to particle sphericity. The stress build-up in particles of high sphericity was gradual and related to densification extending relatively far from the abutment. With increasing angularities, densification was localised near the abutment, but larger and more rapid stress build-up occurred, supported by particle reorientation and interlock developing further away.https://link.springer.com/journal/100352019-11-01hj2018Civil EngineeringMechanical and Aeronautical Engineerin

Development of a convex polyhedral discrete element simulation framework for NVIDIA Kepler based GPUs

Understanding the dynamical behavior of Granular Media (GM) is extremely important to many industrial processes. Thus simulating the dynamics of GMis critical in the design and optimization of such processes. However, the dynamics of GM is complex in nature and cannot be described by a closed form solution for more than a few particles. A popular and successful approach in simulating the underlying dynamics of GM is by using the Discrete Element Method (DEM). Computational viable simulations are typically restricted to a few particles with realistic complex interactions or a larger number of particles with simplified interactions. This paper introduces a novel DEM based particle simulation code (BLAZEDEM) that is capable of simulating millions of particles on a desktop computer utilizing a NVIDIA Kepler Graphical Processor Unit (GPU) via the CUDA programming model. The GPU framework of BLAZE-DEM is limited to applications that require large numbers of particles with simplified interactions such as hopper flow which exhibits task level parallelism that can be exploited on the GPU. BLAZE-DEM also performs real-time visualization with interactive capabilities. In this paper we discuss our GPU framework and validate our code by comparison between experimental and numerical hopper flow.http://www.elsevier.com/locate/camhb201

Development of a convex polyhedral discrete element simulation framework for NVIDIA Kepler based GPUs

Understanding the dynamical behavior of Granular Media (GM) is extremely important to many industrial processes. Thus simulating the dynamics of GMis critical in the design and optimization of such processes. However, the dynamics of GM is complex in nature and cannot be described by a closed form solution for more than a few particles. A popular and successful approach in simulating the underlying dynamics of GM is by using the Discrete Element Method (DEM). Computational viable simulations are typically restricted to a few particles with realistic complex interactions or a larger number of particles with simplified interactions. This paper introduces a novel DEM based particle simulation code (BLAZEDEM) that is capable of simulating millions of particles on a desktop computer utilizing a NVIDIA Kepler Graphical Processor Unit (GPU) via the CUDA programming model. The GPU framework of BLAZE-DEM is limited to applications that require large numbers of particles with simplified interactions such as hopper flow which exhibits task level parallelism that can be exploited on the GPU. BLAZE-DEM also performs real-time visualization with interactive capabilities. In this paper we discuss our GPU framework and validate our code by comparison between experimental and numerical hopper flow.http://www.elsevier.com/locate/camhb201

Implementation of a Discrete Element Method (DEM) particle simulator for GPU cluster

Orientador: Luiz Otávio Saraiva FerreiraDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Os avanços na tecnologia das GPUs, tanto em hardware quanto em ferramentas de programação, tornaram-nas uma opção viável como processadores de uso geral, sendo adequadas para a realização de processamento paralelo com alta demanda de cálculos. Dentre os problemas que podem fazer uso das GPUs são as simulações que envolvem o fluxo de partículas como o Método dos Elementos Discretos (DEM). Assim, a proposta deste trabalho foi implementar em cluster de GPUs um simulador utilizando o Método dos Elementos Discretos. O simulador foi inicialmente validado para uma única GPU utilizando resultados experimentais disponíveis na literatura, onde foram possíveis obter resultados com erros menores do que 10%. Além disso, os tempos de processamento para uma única GPU foram comparados com outro simulador, também implementado em GPU, resultando em tempos de execução semelhantes aos reportados. Finalmente, o método foi expandido para o cluster de GPUs, utilizando uma abordagem híbrida (MPI + CUDA), e apresentou um ganho de desempenho adequado à medida que o número de GPUs foi aumentadoAbstract: Advances in GPU technology, both in hardware and programming tools, have made them a viable option as general-purpose processors, suitable for performing parallel processing tasks with high computational demand. The Discrete Element Method (DEM) studies a class of problems that can make use of GPUs high computational power. Thus, the proposal of this work was to implement a simulator using the Discrete Element Method in a GPU cluster. The simulator was initially validated for a single GPU using experimental results available in the literature, where it was possible to obtain results with errors less than 10%. Also, processing times for a single GPU were compared with another simulator, also implemented in GPU, resulting in run times similar to those reported. Finally, the method was ported to the GPU cluster, using a hybrid approach (MPI + CUDA), and presented a suitable gain of performance as the number of GPUs was increasedMestradoMecanica dos Sólidos e Projeto MecanicoMestre em Engenharia Mecânic

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