This paper discusses the implementation of particle based numerical methods on multi-core machines. In contrast to cluster computing, where memory is distributed across machines, multi-core machine can share memory across all cores. Here general strategies are developed for spatial management of particles and sub-domains that optimize computation on shared memory machines. In particular, we extend cell hashing so that cells bundle particles into orthogonal tasks that can be safely distributed across cores avoiding the use of "memory locks" while still protecting against race conditions. Adjusting task size provides for optimal load balancing and maximizing cache hits. Additionally, the way in which tasks are mapped to execution threads has a significant influence on the memory footprint and it is shown that minimizing memory usage is one of the most important factors in achieving execution speed and performance on multi-core. A novel algorithm called H-Dispatch is used to pipeline tasks to processing cores. The performance is demonstrated in speed-up and efficiency tests on a smooth particle hydrodynamics (SPH) flow simulator. An efficiency of over 90% is achieved on a 24-core machine

Holmes, David W.

Tilke, Peter G.

Williams, John R.

English

Williams, John

Holmes, David

Tilke, Peter

Queensland University of Technology ePrints Archive

Multi-core Strategies For Particle Methods John R. Williams, David Holmes and Peter Tilke* Massachusetts Institute of Technology, *Schlumberger-Doll Research Laboratory  This paper discusses the implementation of particle based numerical methods on multi-core machines. In contrast to cluster computing, where memory is dis-tributed across machines, multi-core machine can share memory across all cores. Here general strategies are developed for spatial management of particles and sub-domains that optimize computation on shared memory machines. In particular, we extend cell hashing so that cells bundle particles into orthogonal tasks that can be safely distributed across cores avoiding the use of “memory locks” while still protecting against race conditions. Adjusting task size pro-vides for optimal load balancing and maximizing cache hits. Additionally, the way in which tasks are mapped to execution threads has a significant influence on the memory footprint and it is shown that minimizing memory usage is one of the most important factors in achieving execution speed and performance on multi-core. A novel algorithm called H-Dispatch is used to pipeline tasks to processing cores. The performance is demonstrated in speed-up and efficiency tests on a smooth particle hydrodynamics (SPH) flow simulator. An efficiency of over 90% is achieved on a 24-core machine.  INTRODUCTION Recent trends in computational physics suggest a rapidly growing interest in par-ticle based numerical techniques. While particle based methods allow robust han-dling of mass advection, this comes at the computational cost of managing the spatial interactions of particles. For problems involving millions of particles, it is necessary to develop efficient strategies for parallel implementation. A variety of authors have reported parallel particle codes on clusters of machines (see for ex-ample Nelson et al [1], Morris et al [2], Sbalzarini et al [3], Walther and Sbalza-rini [4], Ferrari et al [5]). The key difference between parallel computing on clus-ters and on multi-core is that memory can be shared across all cores on today’s multi-core machines. However, there are well known challenges of thread safety and limited memory bandwidth. A variety of approaches to programming on multi-core have been proposed to-date. Concurrency tools from traditional cluster DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 11computing, like MPI [6] and OpenMP [7] have been used to achieve fast take-up of the new technology. If one runs an MPI process on each core, the operating system guarantees memory isolation across processes.  Communication of infor-mation across process boundaries requires MPI messages to be sent, which is rela-tively slow compared with direct memory sharing.  In response to this, Chrysanthakopoulos and co-workers [7, 8] have imple-mented multi-core concurrency libraries using Port based abstractions. These mimic the functionality of a message passing library like MPI, but use shared memory as the medium for data exchange, rather than exchanging serialized packets over TCP/IP as is the case for MPI. Such an approach provides extensibil-ity in program structure, while still capitalizing on the speed advantages of shared memory. In an earlier paper, Holmes et al. [9] have shown that a programming model developed using such port based techniques provides significant perform-ance advantages over MPI and OpenMP. In this paper, we apply the programming model proposed in [9] to the parallelization of particle based numerical methods on multi-core.   PARTICLE BASED NUMERICAL METHODS  Mesh based numerical methods have been the cornerstone of computational phys-ics for decades. Here, integration points are positioned according to some topo-logical connectivity or mesh to ensure compatibility of the numerical interpola-tion. Examples of Eulerian mesh based methods include finite difference (FD)  and the lattice Boltzmann method (LBM), while Lagrangian examples include the finite element method (FEM). While powerful for a wide range of problems, mesh limitations for problems involving large deformation and complex material inter-faces has led to significant developments in meshless and particle based method-ologies. For such methods, integration points are positioned freely in space, capa-ble of advecting with material in a Lagrangian sense. For methods like molecular dynamics (MD)  and the discrete element method (DEM), such points represent literal particles (atoms and molecules for MD and discrete grains for DEM), while for methods like dissipative particle dynamics (DPD) [10], smooth particle hydro-dynamics (SPH) [11], wavelets [12], and the reproducing kernel particle method (RKPM) [13], the particle analogy is largely figurative. For such methods, parti-DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 12cles provide positions at which to enforce a partition of unity. By partitioning unity across the particles, continuity can be imposed without a defined mesh, al-lowing such methods to represent a continuum in a generalized way. In Eulerian mesh based methods, such as FD and LBM, continuity is inherently provided by the static mesh, while for Lagrangian mesh based approaches like FEM, continu-ity is enforced through the use of element shape functions. The partition of unity imposed on mesh-free particle methods can be seen to be a generalization of shape functions for arbitrary integration point arrangements. From Li and Liu [14] ...meshfree methods are the natural extension of finite element methods, they pro-vide a perfect habitat for a more general and more appealing computational paradigm - the partition of unity.  The advantage of partition of unity methods is that any expression related to a field quantity can be imposed on the continuum. Where, for a bounded domain - in Eucledean space, a set of nonnegative compactly supported functions, sums to unity. Correspondingly, the value of some field function can be determined from its value at all other points via nkk kxaxf...2,1,0)()(   The function f (x) can be related to any physical field expression; hydrodynamic, mechanical, electrical, chemical, magnetic etc. Such versatility is a key advantage of particle methods. Strategies for Programming on Multi-Core By eliminating the need for ghost regions and high volume to surface ratio sub-domains, shared memory enables a near arbitrary selection of spatial divisions by which to distribute a numerical task. Correspondingly, it becomes convenient to repurpose a spatial division strat-egy already in use in one form or other in most particle based numerical codes. Spatial hashing techniques have been used widely to achieve algorithmically effi-cient interaction detection in particle codes (see for example DEM [15, 16]).  SPATIAL HASHING IN PARTICLE METHODS  The central idea behind spatial hashing is to overlay some regular spatial structure over the randomly positioned particles e.g. an array of equally sized cells. We can then perform spatial reasoning on the cells rather than on the particles themselves. DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 13Spatial hashing assigns particles to cells or ‘bins’ based on a hash of particle co-ordinates. The numerical expense of such an algorithm is O (N) where N is parti-cle number [15,16]. A variety of programmatic implementations of hash cell methods have been used (for example linked list). In this work a dictionary hash table is used where a generic list of particles is stored for each cell and indexed based on an integer unique key to that cell, i.e. inxjnykkey  )(  where nx and ny are the total number of cells in the x and y dimensions and i, j and k are the integer cell coordinates in the x, y and z dimensions.   Managing Thread Safety The cells also provide a means for defining task pack-ages to the cores. By assigning a number of cells to each core we assure “task or-thogonality” in that each core is operating on different set of particles.  Traditional software applications for shared memory parallel architectures have utilized locks to avoid thread contention. We note that a core may “read” the memory of parti-cles belonging to surrounding cells but may not update them.  We execute a single loop in which global memory for both previous and current field values (  and ) is stored for each particle. Gradient terms can then be calculated as func-tions of values in previous memory, while updates are written to the current value memory, in the same loop. In parallel, minimizing the frequency of so called syn-chronization points has advantages for performance and we utilize this “rolling memory algorithm”. This allows the previous and updated terms to be maintained without needing to replace the former with the latter at the end of each step and thus, ensuring a single synchronization per step.  ntv1ntv Counter Intuitive Design In a typical SPH simulator, two operations must be done on particles in each cell per step, separated by a synchronization, the first to determine the particle number density of each particle, and the second to perform the field variable updates. Interacting particles must be known for each of these two stages. Using a standard SPH formulation, the performance of two structure variations are compared in this case study: A. Interacting particles are determined in the first stage for all cells and stored in a global list for use in the second, and B. Interacting particles are determined as needed in each stage for each cell, i.e. twice per cell per time step. Recalculation of interacting particles means they need DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 14only be kept in a local thread list that is overwritten with each newly dispatched cell. While differences in execution memory are to be expected of the two code versions, the differences in execution time are more surprising. For low core counts (< 10 cores), as would be expected, the single search variant (A) solves more quickly than the double (B) due to less computations. After this point, how-ever, the double search (B) is shown to provide marked improvements in speed over (A) (up to 50%). This can be attributed to better cache blocking of the second approach and the significantly smaller amount of data experiencing latency when being loaded from RAM to cache. The fact that such performance gains only manifest when more than 10 cores are used, suggests that for less than 10 cores, RAM pipeline bandwidth is sufficient to handle a global interaction list.  Fig 1. (a) memory vs. Number of Particles (b) Speed-up vs. Number of Cores  The MIT SPH simulator (see http://geonumerics.mit.edu ) has been validated for single and multi-phase flow. Figure 2 shows results for two phase Rayleigh-Taylor instability [17]. DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 15 Fig 2. Rayleigh-Taylor instability example showing extremely complex fluid phase interface geometries; clear liquid is water, green liquid is oil. [9]. REFERENCES 1. M. T. Nelson, W. Humphrey, A. Gursoy, A. Dalke, L. V. Kale, R. D. Skeel, K. Schulten, NAMD: a parallel, object-oriented molecular dynamics program, Inter-national Journal of High Performance Comp. Applications 10 (1996) 251-268. 2. J. P. Morris, Y. Zhu, P. J. Fox, Parallel simulations of pore-scale flow through porous media, Computers and Geotechnics 25 (1999) 227-246. 3. I. F. Sbalzarini, J. H. Walther, M. Bergdorf, S. E. Hieber, E. M. Kotsalis, P. Koumoutsakos, PPM - a highly efficient parallel particle-mesh library for the simulation of continuum systems, Journal of Computational Physics 215 (2006) 566-588. 4.  J. H. Walther, I. F. Sbalzarini, Large-scale parallel discrete element simula-tions of granular flow, Engineering Computations 26 (2009) 688{697. DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 16DEM5-2010, London, 25-26 August 2010  Page 7  5. A. Ferrari, M. Dumbser, E. F. Toro, A. Armanini, A new 3D parallel SPH scheme for free surface flows, Computers and Fluids 38 (2009) 1203-1217.  6. W. Gropp, E. Lusk, A. Skjellum, Using MPI: Portable Parallel Programming With the Message-Passing Interface, MIT Press, Cambridge, 1999.  7. X. Qiu, G. C. Fox, H. Yuan, S.-H. Bae, G. Chrysanthakopoulos, H. F. Nielsen, Parallel data mining on multicore clusters, in: 7th International Conference on Grid and Cooperative Computing GCC2008.  8.  G. Chrysanthakopoulos, S. Singh, An asynchronous messaging library for C#, in: Proceedings of the Workshop on Synchronization and Concurrency in Object-Oriented Languages, OOPSLA 2005, San Diego, 2005, pp. 89-97. 9. D. W. Holmes, J. R. Williams, P. Tilke, An events based algorithm for distrib-uting concurrent tasks on multi-core architectures, Computer Physics Communi-cations 181 (2010) 341-354.  10.  M. Liu, P. Meakin, H. Huang, Dissipative particle dynamics simulation of pore-scale multiphase fluid flow, Water Resources Research 43 (2007) W04411.  11.  J. J. Monaghan, Smoothed particle hydrodynamics, Annual Review of As-tronomy and Astrophysics 30 (1992) 543{574. 12.  W. K. Liu, Y. Chen, Wavelet and multiple scale reproducing kernel methods, International Journal for Numerical Methods in Fluids 21 (1995) 901{931. 13. W. K. Liu, S. Hao, T. Belytschko, S. Li, C. T. Chang, Multi-scale methods, International Journal for Numerical Methods 14.  S. Li, W. K. Liu, Meshfree Particle Methods, Springer-Verlag, Berlin Heidel-berg, 2004. 14. A. Munjiza, K. R. F. Andrews, NBS contact detection algorithm for bodies of similar size, International Journal for Numerical Methods in Engineering 43 (1998) 131-149. 16.  J. R. Williams, E. Perkins, B. Cook, A contact algorithm for partitioning N arbitrary sized objects, Engineering Computations 21 (2004) 235-248. 17.  A. M. Tartakovsky, P. Meakin, A smoothed particle hydrodynamics model for miscible flow in three-dimensional fractures and the two-dimensional Rayleigh-Taylor instability, Journal of Computational Physics 207 (2005) 610-624. DISCRETE ELEMENT METHODSSimulations of Discontinua – Theory and Applications 17

Multi-core strategies for particle methods

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DEM5 Conference Programme Wednesday 25th August 2010 08.00 Late Registration and Coffee Late Registration Desk is opened 08.00-18.00 08.20 OPENING LECTURE: From Nanoscience to Astrophysics-Towards Discrete Populations Based Virtual Experimentation A. Munjiza Room A 08.50 PLENARY LECTURE: Multi-core Strategies for particle methods John R. Williams, David Holmes and Peter Tilke Room A  Room A  Room B  ALGORITHMS AND SOLVERS – PART 1 Session Chair: G. G. Schiava D’Albano  PARALLELISATION, GPU, OPTIMISATION Session Chair: Tomas Lukas 09.20 MRCK_3D Contact Detection Algorithm E. Rougier and A. Munjiza  Parallelization of an Open-Source FEM/DEM Code Y2D Tomas Lukas, Antonio Munjiza 09.40 Polyhedra on the Cheap Mark A. Hopkins  Wet-Mixing of Powders, a Large-Scale GPU Implementation Radeke, A.C., Khinast, J.G. 10.00 An Event Driven Element Modelling Approach Y. T. Feng, K. Han and D. R. J. Owen  Grid Parallel Computing System for Calibration of DEM Material Models Elson Mourao, David Curry, Richard LaRoche 10.20 Development of a Rotational Resistance Model in the Discrete Element Method Yoshinori Yamada, Mikio Sakai, Masao Tsuchiya and Shuichi Hirayama  A GPU Accelerated Continuous-Based Discrete Element Method for Elastodynamics Analysis Zhaosong Ma, Chun Feng, Tianping Liu, Shihai Li 10.40 Description of Rotation in the Movable Cellular Automaton Method Aleksey Yu. Smolin, Nikita V. Roman, Serguei G. Psakhie  Large Scale GPGPU Implementation of the Discrete Element Method Applied to Modelling the Environment in the Positron Emission Particle Tracking Experiment Marius Hromnik, Indresan Govender 11.00 A Bounding Box Search Algorithm for DEM Simulation Laura E. Walizer and John F. Peters  Calibration of DEM Material Models Using Optimisation Method in a Grid Parallel System David Curry, Elson Mourao, Richard LaRoche    GRANULAR MATERIALS, POWDERS AND NANOPOWDERS – PART 1 Session Chair: Tomas Lukas 11.20 A Spring System Equivalent to Continuum Model Shihai Li, Yanan Zhang, Chun Feng  The 3D Splash Behavior for the Impacting of One Particle on a Particulate Packing Mao Xing, Chuanyu Wu, Michael J. Adams 11.40 Resolving the Indeterminacy of Vertex-Vertex Contact in the 2D Discontinuous Deformation Analysis Zhiye Zhao, Huirong Bao   Fast and Stable Simulation of Granular Matter and Machines Claude Lacoursière, Martin Servin, and Anders Backman 12.00 Lunch 13.00 KEYNOTE LECTURE: DEM Validation – a Simple Problem Colin Thornton, Sharen Cummins, Paul Cleary  ALGORITHMS AND SOLVERS – PART 2 Session Chair: G. G. Schiava D’Albano  GRANULAR MATERIALS, POWDERS AND NANOPOWDERS – PART 2 Session Chair: Tomas Lukas 13.40 LIGGGHTS – a New Open Source Discrete Element Simulation Software Christoph Kloss, and Christoph Goniva  Evolution of Structure in Granular Materials John F. Peters, David A. Horner, Laura E. Walizer, Raju Kala  VERIFICATION AND VALIDATION – PART 1 Session Chair: G. G. Schiava D’Albano   14.00 Simulating and Optimizing of a Ball Mill by Comparison between Numerical Discrete Element Method (DEM) and Experimental Method B. Arabzadeh, V. Hasanzadeh, A. Farzanegan  DEM Simulation of Ball Indentation on Cohesive Powders Massih Pasha, Ali Hassanpour, Mojtaba Ghadiri 14.20 Numerical and Experimental Investigation of Progressive Development of a Granular Pile of Binary Size Pellets Yao wei Yu and Henrik Saxén  Statistics of Internal Structure of a Granular Pile Generated by the Particle Expansion Method Chi-Yan Lo, Malcolm Bolton, Yi-Pik Cheng 14.40 Particle Shape Effects in Medical Syringe Needles – Experiments and Simulations for Polymer Microparticle Injection Mark Whitaker, Paul Langston, Steve Howdle, Barry Azzopardi  Simulating Granular Material Behaviour to General Loading Paths with DEM Xia Li, Hai-Sui Yu 15.00 Experimental Validation of Polyhedral Discrete Element Model Stuart Mack, Paul Langston, Colin Webb, Trevor York  Enhanced Vibrational Granular Mixing J. E. Hilton and P. W. Cleary 15.20 Tee and coffee  VERIFICATION AND VALIDATION – PART 2 Session Chair: G. G. Schiava D’Albano  GRANULAR MATERIALS, POWDERS AND NANOPOWDERS – PART 3 Session Chair: Tomas Lukas 15.40 Modelling the Trajectory of 3D Sensor-Shapes in Discharging Hoppers Filled with Monosized and Binary Mixtures: an Experimental and DEM Comparison Stuart Mack, Paul Langston, Colin Webb, Trevor York  Discrete Element Modelling of Cylindrical Cavity Expansion of Granular Materials Yan Geng, Hai-Sui Yu, Glenn McDowell 16.00 Comparing Experimental Measurements of Mill Lifter Deflections with 2D and 3D DEM Predictions Johanna Alatalo, Bertil I. Pålsson, Kent Tano  Effect of Gradation on the Constant Volume Cyclic Behaviour of Granular Media Abbas Soroush, Behrooz Ferdowsi 16.20 Numerical Analysis of Cold-Formed Sigma Steel Beams Qiang Liu, Jian Yang and Long Yuan Li  Applying DEM to Understanding Jamming in Systems of Non-Spherical Grains Gary W. Delaney, Paul W. Cleary and Anish A. Bhuta  FRACTURE AND FRAGMENTATION, CRACKS Session Chair: G. G. Schiava D’Albano   16.40 Development of Discrete Element Method for Simulation of Deformation and Fracture of Heterogeneous Elastoplastic Materials Sergey G. Psakhie, Evgeny V. Shilko, Alexey Yu. Smolin, Sergey V. Astafurov, Artem Yu. Panchenko  Combining DEM and 3D Imaging of Real Granular Systems Gary W. Delaney, T. Di Matteo and Tomaso Aste 17.00 Fracture Behaviour of Highly Porous Ceramics Xiao-xing Liu and Christophe L. Martin  Discrete Element Modelling of Compaction Behaviour of Agglomerated and Aggregated Nanopowder Avinash Balakrishnan, Patrick Pizette and Christophe L. Martin 17.20 Numerical Strategy for Discrete Fine Cracking Description with Continuous Model Arnaud Delaplace, Benjamin Richard, Cécile Oliver, Frédéric Ragueneau  Agglomeration Dynamics of Magnetic Nanoparticles in Simple Shear Flow Eldin Wee Chuan Lim 17.40 Implementation of 6-Node Element in DFEM for Quasi-Brittle Materials Xu Chunhui, Li Mingrui, Yuan Li  Modelling Non-Spherical Particle Breakage in DEM Simulations Gary W. Delaney, Paul W. Cleary, Matt D. Sinnott and Rob D. Morrison 18.00 Rock Impact Modelling Using FEM/DEM Andrea Lisjak, Giovanni Grasselli   18.20 Implementation of a rock joint shear strength criterion inside a combined finite-discrete element method (FEM/DEM) code Omid K. Mahabadi, Giovanni Grasselli   18.40    19.30 - 23.30  Conference Banquet        Thursday 26th August 2010  Late Registration Desk is opened 08.00-12.00 08.00 KEYNOTE LECTURE: Industrial Case Studies in Mixing, Comminution, Chutes and Screens P. W. Cleary  SOILS, ICE, ROCK, LANDSLIDES, EARTHQUAKES – PART 1 Session Chair: G. G. Schiava D’Albano  COUPLED SOLUTIONS – PART 1 Session Chair: Tomas Lukas 08.40 Coupled DEM-LBM Simulation Of a Soil Fluidisation Problem Xilin Cui, Jun Li, Andrew H.C. Chan, David N. Chapman  Discrete Thermal Element Modelling of Heat Conduction in Spherical Particle Systems Y. T. Feng, C. F. Li, K. Han, D.R.J. Owen 09.00 Fold Development in Compressed Multi-Layers Modelled with FEMDEM  John-Paul Latham, Jiansheng Xiang and Adrian Shelley  Implementation of Combined Single and Smeared Crack Model in 3D Combined Finite-Discrete Element Analysis Zhou Lei, Mengyan Zang, Antonio Munjiza 09.20 Discrete Modelling of Geomaterials Under Extreme Loading T. Tran, P. Marin, L. Scholtes, F.-V. Donzé  Modelling of Reacting Discrete Particles in Continuous Fluid Flow: an Energy Technology Perspective H. Kruggel-Emden, S. Wirtz, V. Scherer, A. Munjiza 09.40 Modelling the Effect of Narrow Blade Geometry on Soil Failure and Draught Force Using Discrete Element Method Gholamhossein Shahgoli, Naser Shahi, Wiria Soltanpoor, Corne J. Coetzee  Implementation of Tangential Force in 3D Discrete Element and Combined Finite-Discrete Element Methods Jiansheng Xiang, John-Paul Latham, Ante Munjiza 10.00 The Application of the Hybrid Stress Blasting Model to Improving the Understanding of Wall Control Blasting Ewan J. Sellers  Development of Virtual Geoscience Simulation Tools, VGeST Using the Combined Finite Discrete Element Method, FEMDEM John-Paul Latham, Jiansheng Xiang, Antonio Munjiza 10.20 Discrete Element Simulation of Rock Cutting with Evaluation of Tool Wear Jerzy Rojek, Carlos Labra, Eugenio Oñate  Coupled Gas-Particulate Discharge from Bucket Elevators Matt Sinnott, James Hilton, William McBride and Paul Cleary 10.40 Modelling Punch-Through Tests with 2D FEM-DEM Arttu Polojärvi and Jukka Tuhkuri  Large Scale Discrete Element Modelling of Fine Particles in a Fluidized Bed M. Sakai, Y. Yamada, Y. Shigeto, K. Shibata, S. Koshizuka 11.00 Combined Finite Discrete Element Simulations of a Floating Ice Sheet Failing Against an Inclined Structure Jani Paavilainen, Jukka Tuhkuri and Arttu Polojärvi  A Preliminary Study on Modelling Liquid-Solid Interaction Using Smoothed Particle Hydrodynamics Jihoe Kwon, Heechan Cho, Hoon Lee 11.20 One Dimensional Compression of Sand-Silt Mixtures Using 2D DEM Nguyen Hop Minh, Yi Pik Cheng    A Coupled DEM/CFD Study of Suction Filling Yu Guo, Chuan-Yu Wu, Colin Thornton 11.40 Using Discrete Element Methods to Model Soil – Machine Interaction Mustafa I. Alsaleh  A Combined Contact Model in CDEM and its Application in Blasting Engineering Chun Feng, Shihai Li, Xiaoyu Liu 12.00 Lunch 13.00 KEYNOTE LECTURE: Modelling of Coupled Multi-Physics in Discrete Systems Y. T. Feng, K. Han, D. R. J. Owen  SOILS, ICE, ROCK, LANDSLIDES, EARTHQUAKES – PART 2 Session Chair: G. G. Schiava D’Albano  COUPLED SOLUTIONS – PART 2 Session Chair: Tomas Lukas 13.40 The Development and Application of Stochastic Block Shape Model in Continuum-Based Discrete Element Method Tianping Liu, Jingjing Lu, Shihai Li, Baojuan Qiao  Computational Investigation of the Dispersion of Cohesive Aggregates Graham Calvert, Ali Hassanpour and Mojtaba Ghadiri    OTHER APPLICATIONS – PART 1 Session Chair: Tomas Lukas 14.00 Adaptive Discrete/Finite Element Coupling for Rock Cutting Process Simulations Carlos Labra, Jerzy Rojek, Eugenio Oñate  A Case Study of Impact on Glass Using the Combined Finite-Discrete Element Method Xudong Chen, Andrew HC Chan and Jian Yang 14.20 Failure Mechanism about RSA under Equal Stress Boundary Conditions Fan Yongbo, Li Shihai, Feng Chun, Hou Yuefeng  3-Distinct Element Analysis of Head Top Coal’s Stability Control during Mechanized Top-Coal Caving in Steep Thick Seam Li Kai-qing 14.40 Using DEM for the Assessment of K0 in Soils Daniel Barreto  DEM Modelling of Particle Flow in a Turbula Mixer M Marigo, D L Cairns, M Davies, A Ingram, E H Stitt 15.00 Theoretical Investigation of Regularities of Model Fault Zone Mechanical Response to Low-Amplitude Dynamic Mechanical Actions Sergey V. Astafurov, Evgeny V. Shilko, Alexandr S. Grigoriev, Artem Yu. Panchenko, Sergey G. Psakhie  Modelling Breakage Environment in Tumbling Mills Using DEM and Analyzing the Outputs N. S. Weerasekara, M. S. Powell, S. Cole, J. Favier 15.20 Tee and coffee  SOILS, ICE, ROCK, LANDSLIDES, EARTHQUAKES – PART 3 Session Chair: G. G. Schiava D’Albano  OTHER APPLICATIONS – PART 2 Session Chair: Tomas Lukas 15.40 The Use of Discrete Element Methods on the Dynamic Analysis of Multi-Drum Ancient Structures Loizos Papaloizou, Petros Komodromos  Vehicle Dynamics on Off-Road Terrain Matt Sinnott and Paul Cleary  16.00 Simulating P-Wave Propagation in DEM G. Marketos  Charge and Structure Behaviour in a Tumbling Mill Pär Jonsén, Bertil I. Pålsson, Kent Tano, Andreas Berggren     OTHER APPLICATIONS – PART 3 Session Chair: G. G. Schiava D’Albano   16.20 On Application of Symbiotic Cellular Automaton Method for Simulation of the Mechanical Response of Lignite A.V. Dimaki, E.V. Shilko, A.I. Dmitriev, S. Zavsek, J. Pezdic, S.G. Psakhie  Analysis of Particle Charging Behaviour via Rotating Chute of Blast Furnace by Using Discrete Element Method Hiroshi Mio, Shinroku Matsuzaki, Kazuya Kunitomo, Jusuke Hidaka 16.40 Modelling Flexible Material Using EDEM: Calibration and Industrial Applications Stephen Cole  Study on Cutting Forces of SiC Machining Process with Pre-Stressed Using DEM Simulation Shengqiang Jiang, Yuanqiang Tan, Dongmin Yang, Yong Sheng 17.00 DEM Analysis on Particle Behaviour in the Course of Sinter Mixture Charging Tsukasa Abe, Junya Kano, Masanori Nakano  DEM Simulation of Particle Motion in a Paddle Mixer Ali Hassanpour, Hongsing Tan, Andrew Bayly, Prasad Gopalkrishnan, Boonho Ng and Mojtaba Ghadiri 17.20 Modelling Emergency Egress from a Public Facility Gary A. Geissinger, Member, IEEE  Prediction of Particle Breakage in Agitated Dryers: a Combined DEM and Experimental Approach Colin Hare, Mojtaba Ghadiri 17.40 Coupled Discrete Element and Fluid Modelling with Non-Spherical Grains J. E. Hilton and P. W. Cleary  Simulation of the Confined Compression Test of Iron Ore Pellets Using Random Distributed 3D Multi Particle Finite Elements Gustaf Gustafsson, Hans-Åke Häggblad 18.00     Directions East Gate of the Mile End Campus is located just 3 minutes walk from the Mile End Tube Station. Residential guests may collect their keys from the reception located in the France House (see the map) in the student village. The reception is open 24 hrs and rooms will be available after midday. Room A – Mason Lecture Theatre Room B – Clinical Medical Lecture Theatre Both rooms are located in the Francis Bancroft Building, see the map. Lunch and dinner will be held in the Octagon, which is located in the Queen’s Building.     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