Highly efficient spatial data filtering in parallel using the opensource library CPPPO

Abstract CPPPO is a compilation of parallel data processing routines developed with the aim to create a library for “scale bridging” (i.e. connecting different scales by mean of closure models) in a multi-scale approach. CPPPO features a number of parallel filtering algorithms designed for use with structured and unstructured Eulerian meshes, as well as Lagrangian data sets. In addition, data can be processed on the fly, allowing the collection of relevant statistics without saving individual snapshots of the simulation state. Our library is provided with an interface to the widely-used CFD solver OpenFOAM ® , and can be easily connected to any other software package via interface modules. Also, we introduce a novel, extremely efficient approach to parallel data filtering, and show that our algorithms scale super-linearly on multi-core clusters. Furthermore, we provide a guideline for choosing the optimal Eulerian cell selection algorithm depending on the number of CPU cores used. Finally, we demonstrate the accuracy and the parallel scalability of CPPPO in a showcase focusing on heat and mass transfer from a dense bed of particles. Program summary Program title: CPPPO Catalogue identifier: AFAQ_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AFAQ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU Lesser General Public License, version 3 No. of lines in distributed program, including test data, etc.: 1043965 No. of bytes in distributed program, including test data, etc.: 11053655 Distribution format: tar.gz Programming language: C++, MPI, octave. Computer: Linux based clusters for HPC or workstations. Operating system: Linux based. Classification: 4.14, 6.5, 12. External routines: Qt5, hdf5-1.8.15, jsonlab, OpenFOAM/CFDEM, Octave/Matlab Nature of problem: Development of closure models for momentum, species transport and heat transfer in fluid and fluid–particle systems using purely Eulerian or Euler–Lagrange simulators. Solution method: The CPPPO library contains routines to perform on-line (i.e., runtime) filtering and compute statistics on large parallel data sets. Running time: Performing a Favre averaging on a structured mesh of 128 3 cells with a filter size of 64 3 cells using one Intel Xeon(R) E5-2650, requires approximately 4 h of computation.

[1]  Jam Hans Kuipers,et al.  Review of direct numerical simulation of fluid–particle mass, momentum and heat transfer in dense gas–solid flows , 2014 .

[2]  B. Welford.  Note on a Method for Calculating Corrected Sums of Squares and Products , 1962 .

[3]  R. Fox,et al.  Multiphase reacting flows: modelling and simulation , 2007 .

[4]  Tim Kluge,et al.  THE 14TH INTERNATIONAL CONFERENCE ON FLUIDIZATION – FROM FUNDAMENTALS TO PRODUCTS , 2013 .

[5]  S. Tenneti,et al.  Role of fluid heating in dense gas–solid flow as revealed by particle-resolved direct numerical simulation , 2013 .

[6]  P. Sagaut.  Large Eddy Simulation for Incompressible Flows , 2001 .

[7]  Takuya Tsuji,et al.  A new relation of drag force for high Stokes number monodisperse spheres by direct numerical simulation , 2014 .

[9]  Giuliano De Stefano,et al.  Sharp cutoff versus smooth filtering in large eddy simulation , 2002 .

[10]  J. Derksen.  Simulations of solid–liquid scalar transfer for a spherical particle in laminar and turbulent flow , 2014 .

[11]  R. Jackson,et al.  The Dynamics of Fluidized Particles , 2000 .

[12]  Yvonne Freeh.  Studies In Turbulence , 2016 .

[13]  H. Xing,et al.  A DEM study on the effective thermal conductivity of granular assemblies , 2011 .

[14]  Ng Niels Deen,et al.  Numerical Simulation of Dense Gas-Solid Fluidized Beds: A Multiscale Modeling Strategy , 2008 .

[15]  A. Favre,et al.  Review on Space-Time Correlations in Turbulent Fluids , 1965 .

[16]  Stefan Radl,et al.  A drag model for filtered Euler–Lagrange simulations of clustered gas–particle suspensions , 2014 .

[17]  Ng Niels Deen,et al.  Direct Numerical Simulation (DNS) of mass, momentum and heat transfer in dense fluid-particle systems , 2014 .

[18]  Anthony G. Dixon,et al.  Catalyst design by CFD for heat transfer and reaction in steam reforming , 2004 .

[19]  M. A. van der Hoef,et al.  COMPUTATIONAL FLUID DYNAMICS FOR DENSE GAS-SOLID FLUIDIZED BEDS： A MULTI-SCALE MODELING STRATEGY , 2005 .

[20]  Thomas B. Gatski,et al.  Studies in turbulence , 1992 .

[21]  Message Passing Interface Forum.  MPI: A message - passing interface standard , 1994 .

[22]  Josep Torrellas.  2012 International Symposium on Computer Architecture Influential Paper Award , 2012, IEEE Micro.

[23]  M. Kraume,et al.  Detailed numerical simulations of catalytic fixed-bed reactors: Heterogeneous dry reforming of methane , 2015 .

[24]  Songyot Nakariyakul.  Fast spatial averaging: an efficient algorithm for 2D mean filtering , 2011, The Journal of Supercomputing.

[25]  G. Batchelor,et al.  An Introduction to Fluid Dynamics , 1968 .

[26]  Ng Niels Deen,et al.  Direct numerical simulation of flow and heat transfer in dense fluid-particle systems , 2012 .

[27]  Z. Feng,et al.  Direct numerical simulation of heat and mass transfer of spheres in a fluidized bed , 2014 .

[28]  Wei Ge,et al.  From Multiscale Modeling to Meso-Science: A Chemical Engineering Perspective , 2013 .

[29]  George Bosilca,et al.  Kernel-assisted and topology-aware MPI collective communications on multicore/many-core platforms , 2013, J. Parallel Distributed Comput..

[30]  Kimmo Berg,et al.  European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) , 2004 .

[31]  J. Derksen,et al.  Direct numerical simulations of dense suspensions: wave instabilities in liquid-fluidized beds , 2007, Journal of Fluid Mechanics.

[32]  L. Berselli.  Analysis of a Large Eddy Simulation model based on anisotropic filtering , 2012 .

[33]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[34]  Stefan Pirker,et al.  A Coarse-Grained Two-Fluid Model for Gas-Solid Fluidized Beds: , 2014 .

[35]  Deng,et al.  From Multiscale Modeling to Meso-science , 2013 .

[36]  M. Germano.  Differential filters for the large eddy numerical simulation of turbulent flows , 1986 .

[37]  S. Tenneti,et al.  Particle-Resolved Direct Numerical Simulation for Gas-Solid Flow Model Development , 2014 .

[38]  C. Kloss,et al.  Models, algorithms and validation for opensource DEM and CFD-DEM , 2012 .

[39]  J. Kuipers,et al.  Direct numerical simulation of particulate flow with heat transfer , 2013 .

[40]  J. Kuipers,et al.  Direct numerical simulation of fluid–particle heat transfer in fixed random arrays of non-spherical particles , 2015 .