F8
Discrete Element Method

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15:35
conference time (CEST, Berlin)
Packaging Line Virtual Testing
26/10/2021 15:35 conference time (CEST, Berlin)
Room: F
C. Shiau, A. Bhat, L. Zhao (Pepsi Co, USA)
C. Shiau, A. Bhat, L. Zhao (Pepsi Co, USA)
De-risking line instability for a new bottle design relies heavily on physical line trials in the beverage industry. Multiple factors contribute to this line instability, including the bottle's geometry design, the bottle's material, surface characteristics of the bottle and conveyor belt, the dynamics and kinematics of the bottle. However, this is often an extensive trial and error process in practice as the detailed bottle motion capturing and analysis is lacking. This measure leads to business hurdles, particularly if the line is already in production. The time and resources spent testing the new bottle design translate into the loss in the existing production line. Numerical tools are used in this study to understand the possible bottle motions in the line convey process. This study explores and evaluates the capability of the virtual packaging line model to pre-screen for the most likely successful solutions for the final physical trials' validation. Models of a virtual packaging line were developed based on a section of the representative plant layout. Both the Finite Element Analysis (FEA) and Discrete Element Method (DEM) modeling tools are used to identify an approach that minimizes the computational cost while still capturing the essential physics. Several parameters, including the bottle geometry, coefficient of friction between bottle and conveyor belt, levelness of the belt, conveyor speed, were varied to test the bottle stability under different conditions to identify the potential line instability (bottle fall overs, denting, and stoppages) spots and causes. Detailed time-dependent bottle behaviors were captured and analyzed. The DEM model achieves an outstanding balance between reasonable computational resources and sufficient fidelity of the bottle characteristic and its motion on the line. Videos from different plant configurations are serving as model inputs to fine-tune and validate the model results. This model is found to have great potential to replicate the desired bottle physical behavior in various line conditions accurately.
CPG, Beverages, Packaging, DEM, FEA
15:55
conference time (CEST, Berlin)
CFD-DEM and DEM Modeling for Key Unit Operations of Process Industries and Their Integration Into Workflows and Simulation Platforms
26/10/2021 15:55 conference time (CEST, Berlin)
Room: F
C. Kloss, C. Goniva, A. Mayrhofer, A. Hager, R. Togni (DCS Computing, AUT)
C. Kloss, C. Goniva, A. Mayrhofer, A. Hager, R. Togni (DCS Computing, AUT)
Discrete Element Method (DEM) and DEM coupled to Computational Fluid Dynamics (CFD-DEM) are powerful techniques for optimization and design of particle processes. Macroscopic granular particles, the flow involving fluids and granular particles are everywhere - in industry, environment and everyday life. Sugar, sand, ores, tablets, chemicals, biomass, detergents, plastics, crops, fruits need to be harvested, produced, processed, transported and stored. We highlight a couple use cases where DEM and CFD-DEM have can have major impact on the product or process development as well as remaining challenges for DEM and CFD-DEM modelling in fields such as steel industry, chemical industry, pharmaceutical industry, consumer goods industry, agricultural machinery production, food production, powder metallurgy and plastics production. We will then give a detailed description of modelling for traditional catalyst (and other powder-based green bodies) design and manufacturing routes as well as modelling of manufacturing of 3D printed catalysts. We will showcase how multi-scale modelling helps for modelling catalysis process routes by coupling surrogate models with detailed 3D flow models. Moreover, we give an overview regarding the integration of such models into a simulation platforms ("Marketplace") for the purpose of Materials Modelling. The concept of this Marketplace is to leverage recent software engineering and ICT advances to collect, adapt and integrate all scattered modelling components from all fragmented materials modelling and industrial communities and provide a single point of access - an on-line gateway - to all materials modelling activities in Europe. We present an overview of the platform architecture and how different software packages and workflows can be integrated into the platform. This work is partially supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 814548, project ZEOCAT-3D. This work is partially supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 760173, project Marketplace.
CFD, DEM, particles, granular media, particle simulations, coupling,
16:15
conference time (CEST, Berlin)
Machine Learning for the Rapid Generation of a Discrete Element Model Database
26/10/2021 16:15 conference time (CEST, Berlin)
Room: F
S. Pantaleev (Altair Engineering, GBR)
S. Pantaleev (Altair Engineering, GBR)
Particulate materials are ubiquitous in nature (soils, ores, grains) and constitute a large portion of industrial feedstock (chemicals, plastics, pharmaceuticals). The Discrete Element Method (DEM), in which individual particles and their interactions are modelled, is a high-fidelity approach for modelling particulate materials in a wide range of applications in the mining[1,2], metallurgy[3], agriculture[4], additive manufacturing [5], batteries [6], pharmaceuticals [7], chemicals [8] and defence industries [9]. Producing fit for purpose DEM models requires the determination of appropriate values for the micro-mechanical parameters that describe particle interactions. The physical measurement of these parameters is challenging, and an indirect method of determination, whereby the model parameters are optimised to reproduce a bulk measurement such as the angle of repose of a granular pile, is commonly adopted [10]. This model optimisation process can be time consuming, resource intensive and may require DEM modelling expertise. Material model databases can significantly minimise these requirements and therefore have considerable utility. However, their generation using traditional methods is expensive and time consuming. In this work, machine learning methods are investigated as a means of significantly improving the efficiency of generation of a DEM material model database. Artificial Neural Network (ANN) based methods and regression-based methods are evaluated and compared in terms of their accuracy for predicting the commonly used static angle of repose and bulk density calibration responses. A dataset of thousands of material models in the commercial DEM code EDEM is used for this purpose. It is found that machine learning methods have good predictive accuracy for the angle of repose and bulk density responses and a systematic approach for the rapid generation of a material model database using these methods is proposed. [1] Gröger, T., & Katterfeld, A. (2007). Application of the discrete element method in materials handling. Bulk Solids Handling, 27(1), 17–22. [2] Rodriguez, V. A., de Carvalho, R. M., & Tavares, L. M. (2018). Insights into advanced ball mill modelling through discrete element simulations. Minerals Engineering, 127(May), 48–60. [3] Mio, H., Kadowaki, M., Matsuzaki, S., & Kunitomo, K. (2012). Development of particle flow simulator in charging process of blast furnace by discrete element method. Minerals Engineering, 33, 27–33. [4] Horabik, J., & Molenda, M. (2016). Parameters and contact models for DEM simulations of agricultural granular materials: A review. Biosystems Engineering, 147, 206–225. [5] Fouda, Y. M., & Bayly, A. E. (2020). A DEM study of powder spreading in additive layer manufacturing. Granular Matter, 22(1) [6] Schreiner, D., Klinger, A., & Reinhart, G. (2020). Modeling of the calendering process for lithium-ion batteries with DEM simulation. Procedia CIRP, 93, 149–155. https://doi.org/10.1016/j.procir.2020.05.158 [7] Yeom, S. Bin, Ha, E., Kim, M., Jeong, S. H., Hwang, S. J., & Choi, D. H. (2019). Application of the discrete element method for manufacturing process simulation in the pharmaceutical industry. Pharmaceutics, 11(8). [8] Guo, Y., & Curtis, J. S. (2015). Discrete element method simulations for complex granular flows. Annual Review of Fluid Mechanics, 47, 21–46. [9] Wasfy, T. M., Mechergui, D., & Jayakumar, P. (2019). Understanding the Effects of a Discrete Element Soil Model’s Parameters on Ground Vehicle Mobility. Journal of Computational and Nonlinear Dynamics, 14(7). https://doi.org/10.1115/1.4043084 [10] Coetzee, C. J. (2017). Review: Calibration of the discrete element method. Powder Technology, 310, 104–142. https://doi.org/10.1016/j.powtec.2017.01.015
Machine learning, discrete element method modelling
16:35
conference time (CEST, Berlin)
A CFD Approach to Analysing Rock Armour Coastal Protection
26/10/2021 16:35 conference time (CEST, Berlin)
Room: F
A. Salmon (WSP, GBR)
A. Salmon (WSP, GBR)
This work demonstrates a full scale numerical approach to simulating large aggregate erosion protection (rock armour) on coastlines; specifically, the relative performance of armoured beach faces to dissipate wave energy and limit wave attack overtopping. The flow within the interstices of piled Dn-50 masonry rock armour was simulated using Volume of Fluid (VOF) CFD modelling techniques with a RANS approach. The geometry of the rock armour was constructed using DEM particles to achieve a realistic stacking environment and meet a target void fraction. An adaptive mesh was used to track the movement of the fluid free surface. An irregular JONSWAP wave spectra was imposed at the domain boundaries to simulate sea conditions with the resulting overtopping discharge monitored at the rock armour crest. The simulation was run until the mean discharge rate reached asymptotic convergence. An analytical estimate of the mean overtopping discharge was calculated in parallel using an Artificial Neural Network trained on the EurOtop database of wave overtopping tests, which showed close agreement to the results of the CFD model. The forces on each element of rock armour were tracked and plotted to demonstrate peak loads for different wave energies, with the motion and displacement of top layer rocks modelled using DEM particles. The CFD approach developed in this work demonstrates the potential for full scale numerical modelling to be more frequently used as a tool in coastal engineering. High geometry flexibility, the ability to resolve complex filtration hydrodynamics and the ease of recording forces and moments at arbitrary locations are an advantage in the early design stage, and can be delivered within useful timescales. As a practical design tool, the efficiency of the model is paramount; symmetry and strategic meshing must be exploited to reduce CPU time. The primary benefits come from allowing unusual geometries to be modelled where standard formulae are ineffective, and as a cost-effective prototyping tool to develop designs before physical modelling.
CFD, Coastal, Water, Multi-physics, Waves
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