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The discrete element method (DEM) is widely used to tackle problems associated with granular material processing. Its applications are diverse, ranging from powder mixing to transport and fluidized beds. Computational costs are a major issue with regard to DEM. Long process times combined with a large numbers of particles require simulations that can last months. In this paper we apply an extrapolation method based on pseudo-steady state DEM bulk behavior to allow for the long repetitive process to be completed. The extrapolation method is applied to two processes relevant to the pharmaceutical industry: continuous mixing and a tablet press feed frame. The results of the extrapolation method are validated against full DEM simulations of the complete process in terms of residence time, travel distance, and velocity distributions. The DEM extrapolation for pseudo-steady state processes resulted in an enormous reduction of the simulation time, while retaining residence times, travel distance and velocity distributions.

Tablets can be manufactured using a rotary tablet press, which consists of a feed frame followed by the compression and compaction of the powder and subsequent ejection of the tablets. From Quality by Design (QbD) perspectives the feed frame plays a critical role and effects products critical quality attributes (CQAs). Thus optimizing this stage is of huge interest. It is preferable to achieve narrow residence time distributions of the powder in the feed frames as well as homogeneous tablets with respect to their height, weight and tensile strength. In the present study three design approaches of feed frames are simulated using the Discrete Element Method (DEM). We investigate the influence of operational input parameters (i.e., rotational rates) on the behaviour of a free flowing and a cohesive material. The detailed simulation data allows us to compare the mentioned setups in terms of residence times, tablet masses and occurring deviations. Therefore, we are able to determine the optimal feed frame and process settings for both free-flowing and cohesive powders.

We examine the performance of scientific and engineering kernels on the Fujitsu A64FX processor, both out-of-the-box using various toolchains and with processor-specific optimizations. While nearly all applications port with little to no modification, significant performance variation is observed between the multiple tool chains. This variation depends heavily upon characteristics of the application (most notably its use of mathematical functions) and is also constrained by the most performant toolchains having limited support for recent language standards. As expected, high performance demands that a kernel is vectorized, multi-threaded, and localizes memory references. Detailed optimizations, including use of intrinsics, are also examined to understand performance gaps and what is necessary to attain peak performance. This article employs the Ookami computer technology testbed funded by the American National Science Foundation. The system provides researchers worldwide with access to 176 Fujitsu A64FX compute nodes as well as other state-of the-art technology.

Ookami is a computer technology testbed supported by the United States National Science Foundation. It provides researchers with access to the A64FX processor developed by Fujitsu in collaboration with RIK{\Xi}N for the Japanese path to exascale computing, as deployed in Fugaku, the fastest computer in the world. By focusing on crucial architectural details, the ARM-based, multi-core, 512-bit SIMD-vector processor with ultrahigh-bandwidth memory promises to retain familiar and successful programming models while achieving very high performance for a wide range of applications. We review relevant technology and system details, and the main body of the paper focuses on initial experiences with the hardware and software ecosystem for micro-benchmarks, mini-apps, and full applications, and starts to answer questions about where such technologies fit into the NSF ecosystem., Comment: 14 pages, 7 figures, PEARC '21: Practice and Experience in Advanced Research Computing, July 18--22, 2021, Boston, MA, USA

Continuous powder mixing is an important technology used in the development and manufacturing of solid oral dosage forms. Since critical quality attributes of the final product greatly depend on the performance of the mixing step, an analysis of such a process using the Discrete Element Method (DEM) is of crucial importance. On one hand, the number of expensive experimental runs can be reduced dramatically. On the other hand, numerical simulations can provide information that is very difficult to obtain experimentally. In order to apply such a simulation technology in product development and to replace experimental runs, an intensive model validation step is required. This paper presents a DEM model of the vertical continuous mixing device termed CMT (continuous mixing technology) and an extensive validation workflow. First, a cohesive contact model was calibrated in two small-scale characterization experiments: a compression test with spring-back and a shear cell test. An improved, quicker calibration procedure utilizing the previously calibrated contact models is presented. The calibration procedure is able to differentiate between the blend properties caused by different API particle sizes in the same formulation. Second, DEM simulations of the CMT were carried out to determine the residence time distribution (RTD) of the material inside the mixer. After that, the predicted RTDs were compared with the results of tracer spike experiments conducted with two blend material properties at two mass throughputs of 15 kg/h and 30 kg/h. Additionally, three hold-up masses (500, 730 and 850 g) and three impeller speeds (400, 440 and 650 rpms) were considered. Finally, both RTD datasets from DEM and tracer experiments were used to predict the damping behavior of incoming feeder fluctuations and the funnel of maximum duration and magnitude of incoming deviations that do not require a control action. The results for both tools in terms of enabling a control strategy (the fluctuation damping and the funnel plot) are in excellent agreement, indicating that DEM simulations are well suited to replace process-scale tracer spike experiments to determine the RTD.

The coating of tablets to prevent product degradation or control dissolution is a typical process in its production. Coating uniformity is critical for the quality of final product and batch acceptance. Therefore, the coating process needs to be optimized in order to achieve the desired uniformity and reduce manufacturing costs. Thus, understanding how process parameters such as spray properties, equipment geometry and tablet shape influence the coating process is critical for process optimization and approval by regulatory bodies. However this is a non-trivial task as obtaining information about the detailed processes in a tablet coater via experimental means is limited. Thus, computational modeling is the most feasible option to obtain information about the physical processes affecting the performance of tablet coaters. The most widely used computational method for such numerical modelling is the Discrete Element Method (DEM) where individual particles (tablets) are simulated. However, the computational cost of representing the typical shape of tablets is high for industrially relevant simulations. Thus tablet shape is typically approximated by simpler shapes such as spheres or multi spheres. Even with such simplifications, typical simulations take months to complete making it unfeasible for process optimization and design. In the last decade, the Graphical Processor Unit (GPU) has enabled large-scale simulations of tens of millions of spheres and millions of shaped particles using the XPS code. In this paper, we present an algorithm for modeling accurate bi-convex tablets that is tailored to the GPU. We firstly validate the algorithm and implementation against a number of experiments. Finally we perform a simulation of 20 million tablets in a drum coater to illustrate the usefulness of GPU computing for industrial coating applications. We found that the proposed method yields a good match against the lab scale experiments. For the industrial simulation the proposed method gave a more accurate result compared to the multi sphere approach while being significantly faster.

A vertical in-line continuous powder mixing device (CMT - Continuous Mixing Technology) has been modelled with the discrete element method (DEM) utilizing a calibrated cohesive contact model. The vertical design of the mixing device allows independent control of mean residence time (MRT) and shear rate. The hold-up mass and outlet flow are controlled by an exit valve, located at the bottom of the in-line mixer. A virtual design of experiments (DoE) of DEM simulations has been performed and parameters such as particle velocities, powder bed shape, residence time distribution (RTD), travel distance, and mixing quality are evaluated for the complete operating space. The RTD of the DEM model has been validated with tracer experiments. The resulting RTD has been fitted with an analytical form (generalized cascade of n continuous stirred tank reactors) and utilized to study the downstream response of the continuous mixing device to upstream fluctuations in the inlet material stream. The results indicate a high mixing quality and good filtering properties across the operating space. However, the combination of low hold-up mass and high impeller speeds leads to a reduced filtering capability and wider exit valve openings, indicating a less desirable operating point.

The applicability of the hydrodynamic similarity criteria (scaling law) introduced by Glicksman (1988) was investigated using fully coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) simulations for single-spout fluidized beds. Four test cases were performed to investigate the scaling law in a pseudo-2D spouted-fluidized bed. In addition, the applicability of Glicksman’s scaling law for simulating 3D fluidized beds was studied. In all simulations, characteristic dimensionless groups, i.e. the Reynolds number (Re), Froude number (Fr), particle-to-fluid density, bed initial height to particle diameter and bed width to particle diameter were kept constant for the both base and scaled cases. Comparing the time averaged particle velocities, gas velocities and volume fractions between the base and scaled cases indicated a very good overall hydrodynamic similarity for all test cases. A minor discrepancy observed between the simulation results of the base and scaled cases was explained by a force analysis. An advantage of the scaling approach, i.e., reducing computational time, was also presented in the last four test cases, including a large-scale simulation, showing that this approach can be considered as a promising way to simulate large-scale spouted-fluidized beds.

Powder mixing is one of the most important pharmaceutical manufacturing operations. Computation methods are required to transform mixer design and scale-up from art to science. Due to an increase in the computational power and the development of graphics cards, the discrete element method is becoming a widely-used technique. For an industrial granular mixer, it is demonstrated that a smart simulation strategy together with a highly efficient code can reduce the simulation time. The good agreement between simulation and measurement data confirms that the simulation strategy can be applied to the product development.

In recent decades, three-blade impellers have been well-established in pharmaceutical high-shear granulation. However, three-blade impellers often require high rotation speeds to initiate product circulation and to provide proper granulation performance. With high rotation speeds much energy is introduced, which is indeed favourable for granule consolidation, however it comes with undesirable thermal stressing and increased granule breakage rates. In order to improve the mixing and granulation behaviour for a more robust process, a new impeller design has been developed that works at lower rotation speeds. The impeller consists of two blades with elongated side wings. In this work, the performance of both impeller designs is intensively studied. Firstly, the mixing behaviour is experimentally investigated in a laboratory mixer (10 L in volume) and at production scale (600 L). The mixing homogeneity of coloured sugar pellets is examined by the digital image analysis (DIA) for several impeller rotation speeds. In a second study, discrete element method (DEM) simulations are employed to obtain shear forces and force distributions at a single-particle scale. The third study is the comparison of granulation performance using a placebo formulation in the frame of a full factorial design of experiments (DoEs). The mixing investigations show that the two-blade impeller has great potential for scale-up. The DEM simulation confirms that both impeller types investigated apply almost the same shear forces on particles. The granulation performance in the DoE is proven to be better for the two-blade impeller. Larger drive torque is measured, product temperature increases significantly less with reduced thermal stressing, and larger granules are produced. Additionally, particle growth behaviour is more robust as it depends only on the amount of liquid added and is unaffected by the impeller's rotation speed.

The combination of Computational Fluid Dynamics (CFD) and Discrete Element Model (DEM) is a powerful tool for studying fluidized particulate systems and granular flows. In DEM, the individual interaction forces between particles are treated on a particle–particle pair basis, and therefore, this method is computational expensive. In addition, the CFD-calculation of the fluid flow increases the computational effort. Thus, current CFD–DEM simulations are limited to systems with particle numbers not exceeding 105. In order to simulate realistic systems, the recently available Compute Unified Device Architecture (CUDA) technology can be applied, which can perform massively-parallel DEM-simulations with several million particles on a single desk-side Graphics Processing Unit (GPU). The objective of this work is to present a new hybrid approach to solve CFD–DEM problems in gas–solid fluidized beds systems applying an efficient coupling method suitable for large-scale simulations. We are using the CUDA technology for the particle simulation and introducing a coupling methodology with a commercial CFD-code. The coupling method between a CFD-code, running on the CPU and our CUDA-based DEM-code running on the GPU, is introduced and discussed. The numerical results are compared to the CFD–DEM and the experimental results of Van Buijtenen et al. (2011). A good agreement was achieved. Finally, fluidized system simulations with up to 25 million particles are presented, which is an unprecented number.

Robust and accurate powder micro-feeding ( 100 m g / s ) and micro-dosing ( 5 mg ) are major challenges, especially with regard to regulatory limitations applicable to pharmaceutical development and production. Since known micro-feeders that yield feed rates below 5 m g / s use gravimetric feeding principles, feed rates depend primarily on powder properties. In contrast, volumetric powder feeders do not require regular calibration because their feed rates are primarily determined by the feeder’s characteristic volume replacement. In this paper, we present a volumetric micro-feeder based on a cylinder piston system (i.e., a powder pump), which allows accurate micro-feeding and feed rates of a few grams per hours even for very fine powders. Our experimental studies addressed the influence of cylinder geometries, the initial conditions of bulk powder, and the piston speeds. Additional computational studies via Discrete Element Method simulations offered a better understanding of the feeding process, its possible limitations and ways to overcome them. The powder pump is a simple yet valuable tool for accurate powder feeding at feed rates of several orders of magnitude.