Massively parallel numerical simulation using up to 36,000 CPU cores of an industrial-scale polydispersed reactive pressurized fluidized bed with a mesh of one billion cells
For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability o...
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| Vydáno v: | Powder technology Ročník 366; s. 906 - 924 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Lausanne
Elsevier B.V
15.04.2020
Elsevier BV Elsevier |
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| ISSN: | 0032-5910, 1873-328X |
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| Abstract | For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of raw data for a simulated physical time of 25s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities. Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon.
[Display omitted]
•An industrial-scale reactive polydispersed gas-solid fluidized bed is simulated.•The mesh reaches a size of a billion cells; the simulation ran on 36,000 CPU cores.•Focus is made on HPC challenges at all steps: simulation setup, run, post-processing.•Massively parallel performances of NEPTUNE_CFD are evaluated on two supercomputers.•Sub-grid models are developed from the highly resolved simulation. |
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| AbstractList | For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerianmulti-fluid approach and relies on a billion cellsmeshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of rawdata for a simulated physical time of 25s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities.Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon. For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of raw data for a simulated physical time of 25s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities. Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon. For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of raw data for a simulated physical time of 25s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities. Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon. [Display omitted] •An industrial-scale reactive polydispersed gas-solid fluidized bed is simulated.•The mesh reaches a size of a billion cells; the simulation ran on 36,000 CPU cores.•Focus is made on HPC challenges at all steps: simulation setup, run, post-processing.•Massively parallel performances of NEPTUNE_CFD are evaluated on two supercomputers.•Sub-grid models are developed from the highly resolved simulation. For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of raw data for a simulated physical time of 25 s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities. Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon. |
| Author | Fournier, Yvan Pigou, Maxime Ansart, Renaud Renon, Nicolas Simonin, Olivier Fede, Pascal Neau, Hervé Baudry, Cyril Mérigoux, Nicolas Laviéville, Jérome |
| Author_xml | – sequence: 1 givenname: Hervé surname: Neau fullname: Neau, Hervé email: neau@imft.fr organization: Institut de Mécanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS, Toulouse, France – sequence: 2 givenname: Maxime orcidid: 0000-0001-6997-2500 surname: Pigou fullname: Pigou, Maxime organization: Institut de Mécanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS, Toulouse, France – sequence: 3 givenname: Pascal orcidid: 0000-0002-0368-4829 surname: Fede fullname: Fede, Pascal organization: Institut de Mécanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS, Toulouse, France – sequence: 4 givenname: Renaud orcidid: 0000-0003-3224-9321 surname: Ansart fullname: Ansart, Renaud organization: Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France – sequence: 5 givenname: Cyril surname: Baudry fullname: Baudry, Cyril organization: Délégation technologies et syst` emes d'information, EDF R&D, Palaiseau, France – sequence: 6 givenname: Nicolas surname: Mérigoux fullname: Mérigoux, Nicolas organization: Fluid Mechanics, Energy and Environment Dpt., EDF R&D, Chatou, France – sequence: 7 givenname: Jérome surname: Laviéville fullname: Laviéville, Jérome organization: Fluid Mechanics, Energy and Environment Dpt., EDF R&D, Chatou, France – sequence: 8 givenname: Yvan surname: Fournier fullname: Fournier, Yvan organization: Fluid Mechanics, Energy and Environment Dpt., EDF R&D, Chatou, France – sequence: 9 givenname: Nicolas surname: Renon fullname: Renon, Nicolas organization: UMS CALMIP 3667 Université de Toulouse, CNRS, INPT, INSA, ISAE, UPS, Toulouse, France – sequence: 10 givenname: Olivier surname: Simonin fullname: Simonin, Olivier organization: Institut de Mécanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS, Toulouse, France |
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| Copyright | 2020 Elsevier B.V. Copyright Elsevier BV Apr 15, 2020 Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | Computation fluid dynamics Sub-grid model High performance computing Polydispersed MPI Industrial scale HPC Olefin polymerization reactor Massively parallel Heat transfer Fluidized bed |
| Language | English |
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| SubjectTerms | Chemical and Process Engineering Chemical engineering Chemical Sciences Computation fluid dynamics Computational fluid dynamics Computer applications Computer simulation computer software computers Cores Engineering Sciences Finite element method Fluid dynamics fluid mechanics Fluidized bed Fluidized bed reactors Fluidized beds Fluids mechanics Heat transfer High performance computing HPC Hydrodynamics Industrial scale Massively parallel Mathematical models Mechanics MPI olefin Olefin polymerization reactor Polydispersed polymerization porous media Post-production processing Reactors Simulation Sub-grid model Supercomputers |
| Title | Massively parallel numerical simulation using up to 36,000 CPU cores of an industrial-scale polydispersed reactive pressurized fluidized bed with a mesh of one billion cells |
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