Large-scale powder mixer simulations using massively parallel GPUarchitectures

Radeke, Charles; Glasser, Benjamin J.; Khinast, Johannes G.

doi:10.1016/j.ces.2010.09.035

Cited by 154 publications

(84 citation statements)

References 17 publications

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“…The DEM is however computationally expensive as all particles in the system have to be checked for contact at each time step. This involves a considerable number of calculations depending on particle geometry and number [7]. To reduce computational cost, particle shape is often approximated using spheres, for which contact detection is trivial.…”

Section: Background and Motivationmentioning

confidence: 99%

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

Govender

Wilke

Kok

et al. 2014

Journal of Computational and Applied Mathematics

105

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Understanding the dynamical behavior of Granular Media (GM) is extremely important to many industrial processes. Thus simulating the dynamics of GM is 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 (BLAZE-DEM) 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.

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Section: Background and Motivationmentioning

confidence: 99%

“…Radeke and Glasser [7] utilize the GPU to simulate powder mixing taking into account detailed particle interactions between spherical particles. They report that a one minute simulation of one million spherical particles requires 96 hours computing time using a single GPU.…”

Section: Detailed Physics Interaction Between Particlesmentioning

confidence: 99%

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

Govender

Wilke

Kok

et al. 2014

Journal of Computational and Applied Mathematics

105

View full text Add to dashboard Cite

show abstract

“…The focus here will not be on developing specialized parallel codes from the ground up that will eventually be application and/or hardware limited, but rather provide the modelling and research community at large with the aforesaid tools to parallelize their codes with minimum effort. GPU computing has also been applied to mixing processes described by DEM as seen in the work of Radeke et al [26] thus confirming its usefulness in mitigating computational times of complex process models.…”

Section: Introductionmentioning

confidence: 86%

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Prakash

Chaudhury

Ramachandran

2013

Modelling and Simulation in Engineering

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Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB's Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes.

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“…These techniques are very helpful during any investigations or for resolving difficulties during drug development or observed during scale-up from the laboratory scale to the commercial scale. A review of current literature provides a description of the granulation process, 2) as well as various reports related to monitoring 3,4) the granulation process and investigations related to scaleup [5][6][7][8] that have been published. The lubrication process is also discussed in the literature and has been studied by various approaches.…”

mentioning

confidence: 99%

New Approach to Evaluate the Lubrication Process in Various Granule Filling Levels and Rotating Mixer Sizes Using a Thermal Effusivity Sensor

Uchiyama

Aoki

Uemoto

2015

CHEMICAL & PHARMACEUTICAL BULLETIN

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The principles of thermal effusivity are applied to an understanding of the detailed mechanisms of the lubrication process in a rotating mixer. The relationships and impact of the lubrication process by the pattern of powder flow, the filling level, and the rotating mixer size were investigated. Thermal effusivity profiles of the lubrication process, as obtained, indicate that lubrication is a two-phase process. The intersection point of the first and second phases (IPFS) is influenced by changing the filling level, thus changing the resulting number of avalanche flows created. The slope of the second phase (SSP) is influenced by the relationship between the number and the length of avalanche flows. Understanding this difference between the first and second phases is important to successfully evaluate the impact of proposed changes in the lubrication process. From this knowledge, a predictive model of the lubrication profile can be generated to allow an evaluation of proposed changes to the lubrication process. This model allows estimation of the lubrication profile at different filling levels and in different rotating mixer sizes. In this study, the actual lubrication profile almost coincides with the model predicted lubrication profile. Based on these findings, it is assumed that lubrication profiles at a commercial scale can be predicted from data generated at the laboratory scale. Further, it is assumed that changes in the filling level can also be estimated from the laboratory or current data.

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Large-scale powder mixer simulations using massively parallel GPUarchitectures

Cited by 154 publications

References 17 publications

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

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

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

New Approach to Evaluate the Lubrication Process in Various Granule Filling Levels and Rotating Mixer Sizes Using a Thermal Effusivity Sensor

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