Documents published in Scipedia

• 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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• Partitioned Coupling Approaches for the Simulation of Natural Hazards Impacting Protective Structures

  V. Singer, K. Sautter, A. Larese, R. Wüchner, K. Bletzinger

  particles2023.

  
  

  Abstract

  In recent years, the intensity and frequency of natural hazards such as landslides, debris flow and avalanches have increased significantly due to climate change and global warming. These catastrophic events are responsible for numerous destructions of infrastructures with high economic losses and, even worse, often claim human lives. Therefore, in addition to the prediction, the design and installation of protective structures are of tremendous importance. Due to its hybrid approach of an Eulerian background grid in combination with Lagrangian moving material points, the Material Point Method (MPM) is particularly suited to capture the flow process of those mass movement hazards. For the numerical simulation of protective structures, however, other numerical methods are often preferable. Considering highly flexible structures, which are often utilized due to their high energy absorption capacity classical Finite Element Method (FEM) is best suited to model cable, beam, and membrane elements, while a retaining wall consisting of a few discrete blocks may be preferable modeled by Discrete Element Method (DEM). Therefore, we are proposing partitioned coupling approaches to combine the advantages of different numerical methods so that the protective structures can be appropriately designed to withstand the impact of those mass movement hazards.

  Abstract
  In recent years, the intensity and frequency of natural hazards such as landslides, debris flow and avalanches have increased significantly due to climate change and global [...]

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• Comparison of continuum (PFEM) and discrete (DEM) approaches for large insertion BVPs in soft rocks

  M. Ciantia, J. Zheng, M. Previtali, J. Knappett

  particles2023.

  
  

  Abstract

  In recent years, significant advancements in computational efficiency have enabled the application of advanced numerical models to solve boundary value problems (BVPs) in geotechnics, including those related to large-displacement problems. However, challenging problems, such as those involving open-ended piles (OEs) in soft rocks, require specialized approaches due to material and geometrical non linearities combined to the large deformation soil-structure interaction. This paper presents a comparison of two approaches for modeling OE pile installation in soft rocks. The first approach employs the Discrete Element Method (DEM), which represents the rock as separate particles bonded together, and introduces a new contact model for highly porous rocks. The second approach uses the Geotechnical Particle Finite Element Method (GPFEM) and investigates the coupled hydromechanical effects during pile installation using a robust and mesh-independent implementation of an elastic-plastic constitutive model at large strains. The DEM approach explores the micromechanical features of pile plugging and unveils the mechanisms behind radial stress distributions inside and outside the plug. The study highlights the strengths and limitations of each modeling approach, providing insights into the behavior of OE piles in soft rocks.

  Abstract
  In recent years, significant advancements in computational efficiency have enabled the application of advanced numerical models to solve boundary value problems (BVPs) in [...]

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• Numerical Simulation of a Laboratory-Scale Free Fall Cone Penetrometer Test in Marine Clay with the Material Point Method

  D. Mohapatra, M. SARESMA, J. Virtalaso, W. Solowski

  particles2023.

  
  

  Abstract

  This paper shows a numerical replication of a laboratory-scale free fall cone penetrometer test of marine clay. The numerical simulation involves large deformations and considers the destructuration of clay, strain rate effects, and non-linear material behaviour. The numerical simulation well replicates the laboratory experiment captured on a high-speed camera. The penetration process is replicated accurately in time, and the depth of the penetration corresponds to that obtained in an experiment. The simulation results indicate that the numerical framework implemented in Uintah software, consisting of an advanced soil model and the Generalized Interpolation Material Point Method, is well-suited for replication of the dynamic penetration process in soft and sensitive marine clay.

  Abstract
  This paper shows a numerical replication of a laboratory-scale free fall cone penetrometer test of marine clay. The numerical simulation involves large deformations and considers [...]

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• Particle Morphological Effects on the Behaviour of Dry Granular Flow Against Rigid Obstacles

  D. Bhattacharya

  particles2023.

  
  

  Abstract

  Geohazards such as rockfall, catastrophic landslides, and debris flow pose a significant risk due to the rapid movement of the vast amount of granular material carrying tremendous destructive potential and energy. Experimental and numerical studies on channelized flumes have been prevalent in analyzing the kinematics and dynamics of the flow and their interaction with various mitigation measures along the projected flow path. Continuum, discontinuum, and hybrid numerical methods have been successfully employed in the past to comprehend the complex material behaviour of granular mass flows. Although the numerical schemes within a continuum setting offer some insights into critical factors like flow velocity, flow depth, runout distance, etc., the granular interaction within the particle ensemble and the impact force on the barrier system for a better estimate of the force-transmission paths cannot be accounted for. The present study employs the Discrete Element Method to investigate the underlying physics of the micromechanical interaction of the granular assembly with the rigid barrier. Although past studies have explored granular flow-like events within a discrete setting, such studies did not incorporate particle morphology. This paper explores the effect of particle shape on kinematics and impact dynamics against a rigid obstacle. First, the numerical results have been benchmarked against the experimental studies for conventional spherical particles, and then we explore the effect of particle morphology. The present findings indicate that the particle shape significantly influences the flow kinematics and leads to a reduction in impact force on the barrier due to the higher angularity of particles with different morphological features than spherical particles, generally considered in the existing literature. A more significant implication of this study is to better understand and design mitigation measures against geohazards.

  Abstract
  Geohazards such as rockfall, catastrophic landslides, and debris flow pose a significant risk due to the rapid movement of the vast amount of granular material carrying tremendous [...]

  • 117
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