Discrete simulation based on EMMS paradigm and its applications in chemical engineering

doi:10.11949/j.issn.0438-1157.20151468

Abstract

Abstract:

Chemical engineering systems typically involve three levels of chemistry, chemical engineering and process system engineering. Each level consists of micro-, meso-and macro-scales, such as the particle, particle cluster and reactor scales in the chemical engineering level. The evolution of the micro-scale elements is naturally described by discrete simulation. However, due to the large number of elements existing in these systems, engineering simulations are mostly based on continuum methods which describe their statistically averaged behaviors and overlook the effects of meso-scale structures, leading to low accuracy and predictivity. This review will explain how this problem can be tackled in a systematic approach that keeps the structural consistency of the problem, physical model, software and hardware, and will demonstrate how discrete simulations with high accuracy, capability and efficiency can be carried out for engineering systems, such as complex molecules, granular flow, and gas-solid fluidization. Realization of virtual process engineering (VPE) is prospected finally.

Key words: discrete simulation, EMMS paradigm, multiphase flow, multiscale, particle, virtual process engineering

摘要：

化工过程通常涉及化学、化工、过程系统工程3个层次,而每个层次又包含微尺度、介尺度和宏尺度,如化工层次的颗粒、颗粒团和反应器尺度。每个层次中的微尺度单元都自然适用离散模型,即通过跟踪每个单元的运动获得整个体系演化的宏观规律。但由于单元数量巨大,工程模拟往往依赖经过统计平均的连续介质模型。由此带来的精度问题,特别是忽略了介尺度结构的问题,随着对化工过程效率和绿色度等要求的提高而日渐突出。介绍了通过问题、模型、软件和硬件结构的一致性提升离散模拟的精度、能力和效率的方法、进展及其在复杂分子体系、颗粒流、气固流态化等方面的应用,展示了通过离散模拟实现虚拟过程工程的可能性。

关键词: 离散模拟, EMMS范式, 多相流, 多尺度, 颗粒, 虚拟过程工程

CLC Number:

TQ018

XU Ji, LU Liqiang, GE Wei, LI Jinghai. Discrete simulation based on EMMS paradigm and its applications in chemical engineering[J]. CIESC Journal, 2016, 67(1): 14-26.

徐骥, 卢利强, 葛蔚, 李静海. 基于EMMS范式的离散模拟及其化工应用[J]. 化工学报, 2016, 67(1): 14-26.

References 82

[1]	LI J H, GE W, KWAUK M. Meso-scale phenomena from compromise—a common challenge, not only for chemical engineering [J/OL]. http://arxiv.org/abs/0912.5407.
[2]	GE W, WANG W, YANG N, et al. Meso-scale oriented simulation towards virtual process engineering (VPE)—the EMMS Paradigm [J]. Chemical Engineering Science, 2011, 66(19): 4426-4458.
[3]	李静海, 黄文来. 探索介科学:竞争中的协调原理[M]. 北京: 科学出版社, 2014.LI J H, HUANG W L. Towards Mesoscience: the Principle of Compromise in Competition[M]. Beijing: Science Press, 2014.
[4]	LI J H, HUANG W L. Towards Mesoscience: the Principle of Compromise in Competition[M]. Berlin: Springer, 2014.
[5]	LI J H, TUNG Y, KWAUK M. Method of energy minimization in multi-scale modeling of particle-fluid two-phase flow[M]//BASU P, LARGE J F. Circulating Fluidized Bed Technology. Pergamon, 1988: 89-103.
[6]	李静海. 两相流多尺度作用模型和能量最小方法[D]. 北京: 中国科学院化工冶金研究所, 1987.LI J H. Multi-scale modeling and method of energy minimization in two-phase flow [D]. Beijing: Institute of Chemical Metallurgy, Chinese Academy of Sciences, 1987.
[7]	LI J H, ZHANG J Y, GE W, et al. A simple variational criterion for turbulent flow in pipe [J]. Chemical Engineering Science, 1999, 54(8): 1151-1154.
[8]	GE W, CHEN F G, GAO J, et al. Analytical multi-scale method for multi-phase complex systems in process engineering—bridging reductionism and holism [J]. Chemical Engineering Science, 2007, 62(13): 3346-3377.
[9]	LI J H, GE W, WANG W, et al. Focusing on the meso-scales of multi-scale phenomena—in search for a new paradigm in chemical engineering [J]. Particuology, 2010, 8(6): 634-639.
[10]	GE W, LI J H. Pseudo-particle approach to hydrodynamics of gas/solid two-phase flow[C]//Proceedings of the 5th International Conference on Circulating Fluidized Bed. Beijing: Science Press, 1996: 260-265.
[11]	GE W, LI J H. Macro-scale phenomena reproduced in microscopic systems—pseudo-particle modeling of fluidization [J]. Chemical Engineering Science, 2003, 58(8): 1565-1585.
[12]	GE W, LI J H. Simulation of particle-fluid systems with macro-scale pseudo-particle modeling [J]. Powder Technology, 2003, 137(1/2): 99-108.
[13]	GE W, LI J H. Conceptual model for massive parallel computing of discrete systems with local interactions [J]. Computers and Applied Chemistry, 2000, 17(5): 385-388.
[14]	GE W, MA J S, ZHANG J Y, et al. Particle methods for multiscale simulation of complex flows [J]. Chinese Science Bulletin, 2005, 50(11): 1057-1069.
[15]	唐德祥. 粒子模拟并行计算通用平台的设计与初步应用[D]. 北京: 中国科学院过程工程研究所, 2005.TANG D X. A general method of parallel computation for particle methods and its preliminary applications[D]. Beijing: Institue of Process Engineering, Chinese Academy of Sciences, 2005.
[16]	ALDER B J, WAINWRIGHT T E. Molecular Dynamics by Electronic Computers[M]. New York: Wiley, 1956.
[17]	CUNDALL P A, STRACK O D L. A discrete numerical-model for granular assemblies [J]. Geotechnique, 1979, 29(1): 47-65.
[18]	HOOGERBRUGGE P J, KOELMAN J. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics [J]. EPL (Europhysics Letters), 1992, 19(3): 155-160.
[19]	BIRD G A. Approach to translational equilibrium in a rigid sphere gas [J]. Physics of Fluids, 1963, 6(10): 1518-1519.
[20]	TSUJI Y, TANAKA T, ISHIDA T. Lagrangian numerical-simulation of plug flow of cohesionless particles in a horizontal pipe [J]. Powder Technology, 1992, 71(3): 239-250.
[21]	LU L Q, XU J, GE W, et al. EMMS-based discrete particle method (EMMS-DPM) for simulation of gas-solid flows [J]. Chemical Engineering Science, 2014, 120: 67-87.
[22]	ANDREWS M J, OROURKE P J. The multiphase particle-in-cell (MP-PIC) method for dense particulate flows [J]. International Journal of Multiphase Flow, 1996, 22(2): 379-402.
[23]	GE W, LU L Q, LIU S W, et al. Multiscale discrete supercomputing-a game changer for process simulation? [J]. Chemical Engineering & Technology, 2015, 38(4): 575-584.
[24]	XU J, REN Y, GE W, et al. Molecular dynamics simulation of macromolecules using graphics processing unit [J]. Molecular Simulation, 2010, 36(14): 1131-1140.
[25]	XU J, HAN M Z, REN Y, et al. The principle of compromise in competition: exploring stability condition of protein folding [J]. Science Bulletin, 2015, 60(1): 76-85.
[26]	PERUTZ M. Electrostatic effects in proteins [J]. Science, 1978, 201(4362): 1187-1191.
[27]	PLIMPTON S. Fast parallel algorithms for short-range molecular dynamics [J]. Journal of Computational Physics, 1995, 117(1): 1-19.
[28]	GE W, LI J H. General approach for discrete simulation of complex systems [J]. Chinese Science Bulletin, 2002, 47(14): 1172-1175.
[29]	王小伟. 可叠加近程作用粒子系统模拟的并行计算框架及通用化研究[D]. 北京: 中国科学院过程工程研究所, 2008.WANG X W. A framework for parallel simulation of particle systems with pair-additive local interactions — toward a general approach[D]. Beijing: Institute of Process Engineering, Chinese Academy of Sciences, 2008.
[30]	RYCKAERT J-P, CICCOTTI G, BERENDSEN H J C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes [J]. Journal of Computational Physics, 1977, 23(3): 327-341.
[31]	HESS B, BEKKER H, BERENDSEN H J C, et al. LINCS: a linear constraint solver for molecular simulations [J]. Journal of Computational Chemistry, 1997, 18(12): 1463-1472.
[32]	DARDEN T, YORK D, PEDERSEN L. Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems [J]. The Journal of Chemical Physics, 1993, 98(12): 10089-10092.
[33]	ESSMANN U, PERERA L, BERKOWITZ M, et al. A smooth particle mesh Ewald method [J]. The Journal of Chemical Physics, 1995, 103(19): 8577-8593.
[34]	LENNARD-JONES J E. Cohesion [J]. Proceedings of the Physical Society, 1931, 43(5): 461-482.
[35]	HERTZ H. Miscellaneous Papers [M]. JONES D E, SCHOTT G A, trans. London, UK: Macmillan, 1896.
[36]	LI B, ZHOU G Z, GE W, et al. A multi-scale architecture for multi-scale simulation and its application to gas-solid flows [J]. Particuology, 2014, 15(0): 160-169.
[37]	WANG X W, GE W. The Mole-8.5 Supercomputing System[M]. Chapman & Hall/CRC, 2013.
[38]	WANG X W, GE W, HE X F, et al. Development and application of a HPC system for multi-scale discrete simulation-Mole-8.5[C]//International Supercomputing Conference. Germany, 2010.
[39]	GE W, XU J, XIONG Q G, et al. Multi-scale Continuum-Particle Simulation on CPU-GPU Hybrid Supercomputer[M]. Heidelberg, Berlin: Springer, 2013.
[40]	Intel. Molecular dynamics optimization on Intel® many integrated core archi-tecture (Intel® MIC)[OL]. 2013. http://software. intel.com/en-us/articles/molecular-dynamics-optimization-on-intel-many-integrated-core-architecture-intel-mic
[41]	NVIDIA CUDA. Compute Unified Device Architecture-CUDA Programming Guide.[M]. Santa Clara, CA: 2006.
[42]	HOU C F, XU J, WANG P, et al. Petascale molecular dynamics simulation of crystalline silicon on Tianhe-1A [J]. International Journal of High Performance Computing Applications, 2013, 27(3): 307-317.
[43]	徐骥. GPU加速度的大分子体系分子动力学方法——实现与应用[D]. 北京: 中国科学院过程工程研究所, 2012.XU J. GPU accelerated MD simulation for macromolecular systems[D]. Beijing: Institute of Process Engineering, Chinese Academy of Sciences, 2012.
[44]	QI H B, XU J, ZHOU G Z, et al. Numerical investigation of granular flow similarity in rotating drums [J]. Particuology, 2015, 22(0): 119-127.
[45]	DI S B, GE W. Simulation of dynamic fluid-solid interactions with an improved direct-forcing immersed boundary method [J]. Particuology, 2015, 18: 22-34.
[46]	XU J, QI H B, FANG X J, et al. Quasi-real-time simulation of rotating drum using discrete element method with parallel GPU computing [J]. Particuology, 2011, 9(4): 446-450.
[47]	XIONG Q G, LI B, ZHOU G Z, et al. Large-scale DNS of gas-solid flows on Mole-8.5 [J]. Chemical Engineering Science, 2012, 71: 422-430.
[48]	XIONG Q G, LI B, XU J. GPU-accelerated adaptive particle splitting and merging in SPH [J]. Computer Physics Communications, 2013, 184(7): 1701-1707.
[49]	KRUGGEL-EMDEN H, RICKELT S, WIRTZ S, et al. A study on the validity of the multi-sphere Discrete Element Method [J]. Powder Technology, 2008, 188(2): 153-165.
[50]	戚华彪. 基于GPU的离散模拟在颗粒流动与混合机理研究中的应用[D]. 北京: 中国科学院过程工程研究所, 2014.QI H B. Application of GPU-based discrete simulation to the study of flow and mixing mechanisms of granular materials[D]. Beijing: University of Chinese Academy of Sciences, 2014.
[51]	XU J, REN Y, GE W, et al. Mole-MD V1.0[CP]. 2010SRBJ0465, Chinese Software register, 2010.
[52]	XU J, QI H B, GE W, et al. DEMMS V2.0[CP]. 2013SRBJ0125, Chinese Software register, 2013.
[53]	XU J, QI H B, GE W, et al. DEMMS V3.0[CP]. 2014SRBJ0786, Chinese Software register, 2014.
[54]	XU J, WANG X W, HE X F, et al. Application of the Mole-8.5 supercomputer: probing the whole influenza virion at the atomic level [J]. Chinese Science Bulletin, 2011, 56(20): 2114-2118.
[55]	REN X X, XU J, QI H B, et al. GPU-based discrete element simulation on a tote blender for performance improvement [J]. Powder Technology, 2013, 239: 348-357.
[56]	YUAN F-W, TUAN H-Y. Supercritical fluid-solid growth of single-crystalline silicon nanowires: an example of metal-free growth in an organic solvent [J]. Crystal Growth & Design, 2010, 10(11): 4741-4745.
[57]	GUO Y, CURTIS J S. Discrete element method simulations for complex granular flows [J]. Annual Review of Fluid Mechanics, 2015, 47(1): 21-46.
[58]	LACEY P M C. Developments in the theory of particle mixing [J]. Journal of Applied Chemistry, 1954, 4(5): 257-268.
[59]	LEVENSPIEL O. Chemical Reaction Engineering[M]. New York: Wiley, 1998.
[60]	VARGAS-ESCOBAR W L. Discrete Modeling of Heat Conduction in Granular Media[M]. Pittsburgh: University of Pittsburgh, 2002.
[61]	WANG L M, ZHOU G Z, WANG X W, et al. Direct numerical simulation of particle-fluid systems by combining time-driven hard-sphere model and lattice Boltzmann method [J]. Particuology, 2010, 8(4): 379-382.
[62]	XIONG Q G, LI B, CHEN F G, et al. Direct numerical simulation of sub-grid structures in gas-solid flow—GPU implementation of macro-scale pseudo-particle modeling [J]. Chemical Engineering Science, 2010, 65(19): 5356-5365.
[63]	LADD A J C. Numerical simulations of particulate suspensions via a discretized Boltzmann equation(Ⅰ): Theoretical foundation [J]. Journal of Fluid Mechanics, 1994, 271: 285-309.
[64]	狄升斌. 基于浸入边界法的复杂流动多尺度模拟[D]. 北京:中国科学院大学, 2015.DI S B. Multi-scale modeling and numerical simulation of complex flows based on immersed boundary method[D]. Beijing: University of Chinese Academy of Sciences, 2015.
[65]	DEEN N G, KRIEBITZSCH S H L, VAN DER HOEF M A, et al. Direct numerical simulation of flow and heat transfer in dense fluid-particle systems [J]. Chemical Engineering Science, 2012, 81: 329-344.
[66]	MA J S, GE W, WANG X W, et al. High-resolution simulation of gas-solid suspension using macro-scale particle methods [J]. Chemical Engineering Science, 2006, 61(21): 7096-7106.
[67]	MA J S, GE W, XIONG Q G, et al. Direct numerical simulation of particle clustering in gas-solid flow with a macro-scale particle method [J]. Chemical Engineering Science, 2009, 64(1): 43-51.
[68]	PESKIN C S. Flow patterns around heart valves: a numerical method [J]. Journal of Computational Physics, 1972, 10(2): 252-271.
[69]	SMAGORINSKY J. General circulation experiments with the primitive equations [J]. Monthly Weather Review, 1963, 91(3): 99-164.
[70]	GIDASPOW D. Multiphase Flow and Fluidization: Continuum and Kinetic Theory Description[M]. New York: Academic Press, 1994.
[71]	ANDERSON T B, JACKSON R. Fluid mechanical description of fluidized beds. Equations of motion [J]. Industrial & Engineering Chemistry Fundamentals, 1967, 6(4): 527-539.
[72]	XU M, CHEN F G, LIU X H, et al. Discrete particle simulation of gas-solid two-phase flows with multi-scale CPU-GPU hybrid computation [J]. Chemical Engineering Journal, 2012, 207: 746-757.
[73]	XU M, GE W, LI J H. A discrete particle model for particle-fluid flow with considerations of sub-grid structures [J]. Chemical Engineering Science, 2007, 62(8): 2302-2308.
[74]	XU B H, YU A B. Numerical simulation of the gas-solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics [J]. Chemical Engineering Science, 1997, 52(16): 2785-2809.
[75]	SYAMLAL M, PANNALA S. Multiphase Continuum Formulation for Gas-Solids Reacting Flows[M]. Hershey, New York: Engineering Science Reference, 2011.
[76]	KUNII D, LEVENSPIEL O. Fluidization Engineering[M]. 2nd ed. Stoneham, MA (United States): Butterworth Publishers, 1991.
[77]	LIU X H, GUO L, XIA Z J, et al. Harnessing the power of virtual reality [J]. Chemical Engineering Progress, 2012, (7): 28-32.
[78]	XU J, LI X X, HOU C F, et al. Engineering molecular dynamics simulation in chemical engineering [J]. Chemical Engineering Science, 2015, 121: 200-216.
[79]	CHEN W C, GAO Y. The effect of reducing coal slurry particle size on operation of multi-nozzle oppositely placed coal-water slurry gasification system [J]. Chemical Fertilizer Industry, 2015, 42(2): 36-38.
[80]	SAKAI M, KOSHIZUKA S. Large-scale discrete element modeling in pneumatic conveying [J]. Chemical Engineering Science, 2009, 64(3): 533-539.
[81]	SHAW D E, DENEROFF M M, DROR R O, et al. Anton, a special-purpose machine for molecular dynamics simulation [J]. SIGARCH Comput. Archit. News, 2007, 35(2): 1-12.
[82]	SHAW D E, GROSSMAN J P, BANK J A, et al. Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer[C]//Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC14). Piscataway, NJ, USA: IEEE Press, 2014: 41-53