Assessment of model parameters in MFiX particle-in-cell approach

Abstract The limitations in numerical treatment of solids-phase in conventional methods like Discrete Element Model and Two-Fluid Model have facilitated the development of alternative techniques such as Particle-In-Cell (PIC). However, a number of parameters are involved in PIC due to its empiricism. In this work, global sensitivity analysis of PIC model parameters is performed under three distinct operating regimes common in chemical engineering applications, viz. settling bed, bubbling fluidized bed and circulating fluidized bed. Simulations were performed using the PIC method in Multiphase Flow with Interphase eXchanges (MFiX) developed by National Energy Technology Laboratory (NETL). A non-intrusive uncertainty quantication (UQ) based approach is applied using Nodeworks to first construct an adequate surrogate model and then identify the most influential parameters in each case. This knowledge will aid in developing an effective design of experiments and determine optimal parameters through techniques such as deterministic or statistical calibration.

[1]  William A. Rogers,et al.  Numerical Simulation and Experimental Study of the Gas–Solid Flow Behavior Inside a Full-Loop Circulating Fluidized Bed: Evaluation of Different Drag Models , 2018 .

[2]  W. Wang,et al.  Impacts of solid stress model on MP-PIC simulation of a CFB riser with EMMS drag , 2019, Powder Technology.

[3]  F. Harlow,et al.  Numerical Calculation of Time‐Dependent Viscous Incompressible Flow of Fluid with Free Surface , 1965 .

[4]  A. Sudjianto,et al.  An Efficient Algorithm for Constructing Optimal Design of Computer Experiments , 2005, DAC 2003.

[5]  Kenneth W. Desmond,et al.  Influence of particle size distribution on random close packing of spheres. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Huiping Tang,et al.  DEM simulation of the packing of rodlike particles , 2015 .

[7]  A. Shimosaka,et al.  Effects of mixing ratio and order of admixed particles with two diameters on improvement of compacted packing fraction , 2020 .

[8]  M. Shahnam,et al.  Bridging particle and reactor scales in the simulation of biomass fast pyrolysis by coupling particle resolved simulation and coarse grained CFD-DEM , 2020 .

[9]  M. Shahnam,et al.  Using a proper orthogonal decomposition to elucidate features in granular flows , 2020, Granular Matter.

[10]  Albert P. Philipse,et al.  Liquid Permeation of Bidisperse Colloidal Hard-Sphere Packings and the Kozeny-Carman Scaling Relation , 1994 .

[11]  Liqiang Lu,et al.  Extension of a coarse grained particle method to simulate heat transfer in fluidized beds , 2017 .

[12]  D. Snider.  An incompressible three-dimensional multiphase particle-in-cell model for dense particle flows , 2001 .

[13]  Aibing Yu,et al.  Influence of Operating Parameters on Raceway Properties in a Model Blast Furnace Using a Two-Fluid Model , 2014 .

[14]  Christopher J. Freitas,et al.  Standards and Methods for Verification, Validation, and Uncertainty Assessments in Modeling and Simulation , 2020 .

[15]  W. Ge,et al.  EMMS-based solid stress model for the multiphase particle-in-cell method , 2020 .

[16]  Oscar Antepara,et al.  MFIX-Exa: A path toward exascale CFD-DEM simulations , 2021, Int. J. High Perform. Comput. Appl..

[17]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[18]  Mary Ann Clarke,et al.  Verification and Validation of MFiX-PIC , 2020 .

[19]  P. J. O'rourke,et al.  An improved collision damping time for MP-PIC calculations of dense particle flows with applications to polydisperse sedimenting beds and colliding particle jets , 2010 .

[20]  M. Clarke,et al.  Deterministic Calibration of MFiX-PIC, Part 1: Settling Bed , 2021 .

[21]  Wei Wang,et al.  MP-PIC simulation of CFB riser with EMMS-based drag model , 2012 .

[22]  P. J. O'rourke,et al.  The multiphase particle-in-cell (MP-PIC) method for dense particulate flows , 1996 .

[23]  Jinghai Li,et al.  EMMS-based discrete particle method (EMMS–DPM) for simulation of gas–solid flows , 2014 .

[24]  W. Rogers,et al.  Sensitivity Analysis of Particle-In-Cell Modeling Parameters in Settling Bed, Bubbling Fluidized Bed and Circulating Fluidized Bed , 2021 .

[25]  P. J. ORourke,et al.  An incompressible two-dimensional multiphase particle-in-cell model for dense particle flows , 1997 .

[26]  P. J. O'rourke,et al.  A model for collisional exchange in gas/liquid/solid fluidized beds , 2009 .

[27]  Dale M. Snider,et al.  A new blended acceleration model for the particle contact forces induced by an interstitial fluid in dense particle/fluid flows , 2014 .

[28]  Francesco Di Maio,et al.  Coarse-Grain DEM Modelling in Fluidized Bed Simulation: A Review , 2021, Processes.

[29]  Aytekin Gel,et al.  Toward the Development of a Verification, Validation, and Uncertainty Quantification Framework for Granular and Multiphase Flows—Part 1: Screening Study and Sensitivity Analysis , 2018, Journal of Verification, Validation and Uncertainty Quantification.

[30]  S. Benyahia,et al.  TRIBUTE TO FOUNDERS : NEAL R . AMUNDSON . PARTICLE TECHNOLOGY AND FLUIDIZATION Continuum Model Validation of Gas Jet Plume Injection into a Gas – Solid Bubbling Fluidized Bed , 2013 .

[31]  Dale M. Snider,et al.  Inclusion of collisional return-to-isotropy in the MP-PIC method , 2012 .

[32]  D. W. Tanner,et al.  Packing Densities of Mixtures of Spheres with Log-normal Size Distributions , 1972 .

[33]  A. Vaidheeswaran,et al.  Chaos and recurrence analyses of pressure signals from bubbling fluidized beds , 2020 .