Documents published in Scipedia

• Modeling and Simulation of Soil and Soil-Tool Interaction for Industrial Applications

  M. Harutyunyan, S. Emmerich, S. Steidel, M. Burger, K. Jareteg, J. Quist

  particles2023.

  
  

  Abstract

  Modeling of soil-tool interaction for industrial applications involves the coupling of soil models, mostly based on the Discrete Element Method (DEM), with multibody systems, representing construction machinery such as excavators or wheel loaders. To obtain accurate predictions of reaction forces on tools like wheel loader buckets, it is essential to have an appropriate parametrization procedure, which makes use of data obtained from laboratory tests such as the triaxial compression test or direct shear test for different types of soil. Simulations with suitable DEM-softwares can then be validated against the experimental data to assess the applicability and performance of the numerical methods [2, 3] The DEM software GRAnular Physics Engine (GRAPE) developed at Fraunhofer ITWM in Germany has been successfully used to simulate compression and shear tests and has been proven to yield good predictions of the soil-tool interaction and draft forces [4] for spherical particles such as coarse sand. A year-long collaboration with the Fraunhofer-Chalmers Centre (FCC) in Sweden has successfully resulted in the development of a soil simulation toolbox for FCC’s general purpose DEM solver Demify® incorporating and enhancing the simulation techniques used in GRAPE [1]. Co-simulation is enabled through an FMI interface coupling Demify® with multibody systems modeled e.g., in Simulink to evaluate relevant variables such as forces at critical linkage joints of the construction machines. To model real-life application scenarios, the entire workflow must be considered, from the soil parametrization process, to setting the particulate soil model and performing numerical simulations for the specific problem, to the final post-processing to analyze the load data. We will characterize and discuss the different steps of the workflow and present simulation results obtained with the toolbox Demify® for Heavy Machinery. 

  Abstract
  Modeling of soil-tool interaction for industrial applications involves the coupling of soil models, mostly based on the Discrete Element Method (DEM), with multibody systems, [...]

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• Continuum Granular Flow Modeling and Beyond: Exploiting Meshless Methods

  K. Kamrin

  particles2023.

  
  

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• Particle-based fiber models of woven materials for earth entry thermal protection

  A. Santos, L. Abbot, J. Haskins

  particles2023.

  
  

  Abstract

  Applications requiring materials with layer-to-layer strength, from basketry to thermal protection systems, use interlaced, three-dimensional woven materials. NASA is developing and deploying woven material heat shields for missions, including Artemis I (3D-MAT for compression pads) and potentially Mars Sample Return - Earth Entry System. These materials are complex, hierarchical and must protect from extreme environments and phenomena, such as deformation, impact and high-enthalpy heating. Woven material performance depends on microstructure, damage and weave geometry. Therefore, fiber-specific models are needed to simulate fiber contacts within the weave hierarchical geometry (fiber to tow to yarn to weave) and the inherent directionality of fibers. Explicit fiber models can simulate how weave microstructure evolution affects thermal and mechanical properties. We parameterize a discrete element bonded particle models (DEM-BPM) of fibers to capture thermal and mechanical behavior within and between fibers, with bonded and contact forces, respectively. We study the proportion of heat transfer and stress via the contact network, fiber bonds and the weave geometry, for example, with respect to yarn warp-weft identity (whether it interlaces weave layers). Our results demonstrate the importance of explicit fiber modeling for connecting microstructure with thermal and mechanical properties.

  Abstract
  Applications requiring materials with layer-to-layer strength, from basketry to thermal protection systems, use interlaced, three-dimensional woven materials. NASA is developing [...]

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• GPUs Based Material Point Method for Compressible Flows

  P. Baioni, T. Benacchio, L. Capone, C. de Falco

  particles2023.

  
  

  Abstract

  Particle-In-Cell (PIC) methods such as the Material Point Method (MPM) can be cast in formulations suitable to the requirements of data locality and fine-grained parallelism of modern hardware accelerators such as Graphics Processing Units (GPUs). While continuum mechanics simulations have already shown the capabilities of MPM on a wide range of phenomena, the use of the method in compressible gas dynamics is less frequent. This contribution aims to show the potential of a GPU-based MPM parallel implementation for compressible fluid dynamics, as well as to assess the reliability of this approach in reproducing supersonic gas flows against solid obstacles. The results in the paper represent a stepping stone towards a highly parallel, Multi-GPU, MPM-base solver for M ach > 1 Fluid-Structure Interaction problems.

  Abstract
  Particle-In-Cell (PIC) methods such as the Material Point Method (MPM) can be cast in formulations suitable to the requirements of data locality and fine-grained parallelism [...]

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• Computational Error Estimation for the Material Point Method in 1D and 2D

  M. Berzins

  particles2023.

  
  

  Abstract

  The Material Point Method (MPM) is widely used for challenging applications in engineering, and animation but lags behind some other methods in terms of error analysis and computable error estimates. The complexity and nonlinearity of the equations solved by the method and its reliance both on a mesh and on moving particles makes error estimation challenging. Some preliminary error analysis of a simple MPM method has shown the global error to be first order in space and time for a widely-used variant of the Material Point Method. The overall time dependent nature of MPM also complicates matters as both space and time errors and their evolution must be considered thus leading to the use of explicit error transport equations. The preliminary use of an error estimator based on this transport approach has yielded promising results in the 1D case. One other source of error in MPM is the grid-crossing error that can be problematic for large deformations leading to large errors that are identified by the error estimator used. The extension of the error estimation approach to two space higher dimensions is considered and together with additional algorithmic and theoretical results, shown to give promising results in preliminary computational experiments.

  Abstract
  The Material Point Method (MPM) is widely used for challenging applications in engineering, and animation but lags behind some other methods in terms of error analysis and [...]

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• Determining Coke Moisture Content Through Images Analysis Methods and Machine Learning Models

  M. Li

  particles2023.

  
  

  Abstract

  Coke moisture content plays a crucial role as a quality indicator in ironmaking. Fast and accurate measurement of coke moisture content can effectively ensure operational stability, thereby guaranteeing the quality of pig iron. Coke with different moisture levels exhibits variations in light reflection, refraction, and powder adhesion characteristics. Drawing from this premise, we employed an image analysis approach to analyse the colour and texture features of coke images with varying moisture content. The results indicated that certain specific image features are highly sensitive to changes in coke moisture content. This study also conducted testing and comparison of performances of three common machine learning models in predicting coke moisture content based on image analysis. The Support Vector Machine (SVM) predictive model for coke moisture content based on image analysis showed optimal performance. This demonstrated a close connection between coke image features and moisture content.

  Abstract
  Coke moisture content plays a crucial role as a quality indicator in ironmaking. Fast and accurate measurement of coke moisture content can effectively ensure operational [...]

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• Simulating industrial scenarios: with the open-source software MercuryDPM

  A. Thornton, Q. Nguyen, H. Polman, M. Post, J. Bisschop, R. Weinhart-Mejia, D. Fitzsimmons, M. Vesal, T. Plath, I. Ostanin, T. Weinhart

  particles2023.

  
  

  Abstract

  Creating predictive computer simulations, i.e. virtual prototypes, of complex granular industrial processes has many challenges. In this paper we review recent advances in creating such virtual prototypes. We introduce the open-source code MercuryDPM [1], which is often applied to complex industrial applications via the spin-off company MercuryLab. We briefly discuss how to import complex industrial geometries and how to deal with large numbers of particles and wide size-distributions. Then we focus on how to create a computer representation of an actual granular material, the so-called model calibration. For calibration, we start by reviewing what parameters need to be measured and what experimental characterisation machines are available. We present an industrially practical calibration method, where certain parameters are directly measured and others are indirectly calibrated, using a variety of machine-learning techniques, implemented in the open-source codes GrainLearning [2], TensorFlow [3] and scikit-learn [4]. With GrainLearning, one can find local optima in only two to three iterations, even for complex contact models with many microscopic parameters. On the other hand, TensorFlow and scikit-learn use two popular supervised learning algorithms, Neural Network (NN) and Random Forest (RF) regression, respectivly. After a training period consisting of hundreds of particle simulations, NN and RF are capable of providing a mapping between the micro-parameters and the bulk behaviour, which can be used to find the optimal micro-parameters that correspond to the experimentally observed behaviour.

  Abstract
  Creating predictive computer simulations, i.e. virtual prototypes, of complex granular industrial processes has many challenges. In this paper we review recent advances in [...]

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• Advancing Dynamic Process Modeling of Comminution and Classification Circuits: A Paradigm Shift with GPU-Enabled DEM Solver

  J. Quist, E. Edelvik

  particles2023.

  
  

  Abstract

  The minerals processing and aggregate industry have relied on steady-state population and mass balance simulators for decades. However, accurately modeling new processes remains a critical challenge that hinders innovation and decision-making in the industry. In recent years, time-dynamic simulators have been developed, which offer more accurate predictions of process variability and performance, as well as the ability to introduce regulators and control algorithms. Yet, these still require simplified process models of each unit in the system. The development of high-performance discrete element method (DEM) solvers with advanced particle physics models presents a new opportunity to model complete comminution and classification processes. In this paper, we discuss the potential, challenges, and current limitations of using DEM for advanced dynamic process and equipment evaluation, exemplified by a coarse comminution crushing and screening case. We demonstrate the methodology using a GPU polyhedral DEM implementation with a boundary-volume hierarchy (BVH) collision search algorithm. The results show that the scale of a full-scale two-stage crushing process is possible to simulate. The transition from algebraic process models to DEM would make a significant advancement, bridging the current gap between overly simplified generalized process models and specific equipment design. This approach offers exciting opportunities for the mineral processing and aggregate industry to develop more innovative and efficient circuits.

  Abstract
  The minerals processing and aggregate industry have relied on steady-state population and mass balance simulators for decades. However, accurately modeling new processes remains [...]

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• Physical Process-Based Entrainment Behaviour Modelling of Diluted Debris Flow Using SPH Incorporated with HBP-DP Approach

  Y. Ma, M. Asai, G. Chen, Z. Han

  particles2023.

  
  

  Abstract

  Debris flows overriding steep valleys can cause a significant decrease in bed friction resistance due to undrained excess pore water pressure, leading to an exponential increase in both destructiveness and volume. This study develops a two-phase numerical model based on Smoothed Particle Hydrodynamics to simulate the progressive entrainment behavior of debris flow accurately. The fluid and bed-sediment materials are modeled using the non-Newtonian Bingham-type Herschel-Bulkley-Papanastasiou (HBP) constitutive model. The mass erosion behavior of debris flow is achieved and augmented by incorporating the Drucker-Prager (DP) softening model, which accounts for variations in the pore water pressure ratio across different saturation states. A straightforward phase-change approach is implemented according to the mutation of effective viscosity to prevent any minute displacements of viscoplastic materials when subjected to steep inclinations. The multi-phase model has been compared with the large scale flume experiments conducted by the United States Geological Survey. The 3-D numerical results obtained from the rigid bed, dry and wet erodible bed exhibit a good agreement with the experimental data, encompassing flow momentum feedback and erosion patterns. This paper initially attempts to simulate the entrainment of multiple phases in a steep valley by incorporating viscoplastic flow.

  Abstract
  Debris flows overriding steep valleys can cause a significant decrease in bed friction resistance due to undrained excess pore water pressure, leading to an exponential increase [...]

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• Bottom Boundary-Fitted Free Surface Flow Simulation with Coordinate Transformation Using SPH(2)

  S. Fujioka, N. Mitsune, K. Tsuji, M. Asai

  particles2023.

  
  

  Abstract

  Particle methods such as the SPH and MPS methods have problems because it is difficult to treat curved bottom surfaces such as seabed surfaces accurately. In this study, regarding this problem, the curved bottom surfaces’ treatments have been improved using a coordinate transformation using the high-order second derivative model called SPH(2). Although the theory for the coordinate transformation was established in the MPS method, its accuracy did not give the desired accuracy because of the numerical errors of the second derivative models. Therefore, the numerical errors in these coordinate transformations were overcome by applying the second derivative model of SPH(2) to the coordinate transformation formulas. The superiority and validity of the proposed coordinate transformation using SPH(2) are demonstrated through validation examples such as the hydrostatic pressure and dam-break problems.

  Abstract
  Particle methods such as the SPH and MPS methods have problems because it is difficult to treat curved bottom surfaces such as seabed surfaces accurately. In this study, regarding [...]

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