A CPU-GPU cross-platform coupled CFD-DEM approach for complex particle-fluid flows. (21st September 2020)
- Record Type:
- Journal Article
- Title:
- A CPU-GPU cross-platform coupled CFD-DEM approach for complex particle-fluid flows. (21st September 2020)
- Main Title:
- A CPU-GPU cross-platform coupled CFD-DEM approach for complex particle-fluid flows
- Authors:
- He, Yi
Muller, Frans
Hassanpour, Ali
Bayly, Andrew E. - Abstract:
- Highlights: CFD solver is coupled with a stand-alone GPU-based DEM via network communication. Communication overhead is insensitive to the number of particles being simulated. A dual-grid approach is proposed for data mapping between Eulerian and Lagrangian properties. This coupling scheme can handle large-scale, general-purposed industrial applications. DEM calculation is no longer the computational bottleneck for a coupled CFD-DEM simulation. Abstract: High computational cost presents a significant barrier to the general application of coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations, especially so for industrial systems with a large number of particles and complex geometries. In this study, a new cross-platform coupling approach is developed by integrating a CFD solver with a standalone GPU-based DEM solver via network communication. Consequently, the two modelling techniques benefit from the most appropriate hardware architecture. The developed coupling approach shows predictions comparable to experiments on a small-scale fluidized bed. Its computational performance is evaluated on a larger fluidized bed and shows superior performance over the CPU-based parallelization methods, making DEM calculation no longer the computational bottleneck. Its general applicability to handle complex geometrical domains is further demonstrated by simulations of a gas-solid cyclone separator. This work demonstrates the benefits of a novel couplingHighlights: CFD solver is coupled with a stand-alone GPU-based DEM via network communication. Communication overhead is insensitive to the number of particles being simulated. A dual-grid approach is proposed for data mapping between Eulerian and Lagrangian properties. This coupling scheme can handle large-scale, general-purposed industrial applications. DEM calculation is no longer the computational bottleneck for a coupled CFD-DEM simulation. Abstract: High computational cost presents a significant barrier to the general application of coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations, especially so for industrial systems with a large number of particles and complex geometries. In this study, a new cross-platform coupling approach is developed by integrating a CFD solver with a standalone GPU-based DEM solver via network communication. Consequently, the two modelling techniques benefit from the most appropriate hardware architecture. The developed coupling approach shows predictions comparable to experiments on a small-scale fluidized bed. Its computational performance is evaluated on a larger fluidized bed and shows superior performance over the CPU-based parallelization methods, making DEM calculation no longer the computational bottleneck. Its general applicability to handle complex geometrical domains is further demonstrated by simulations of a gas-solid cyclone separator. This work demonstrates the benefits of a novel coupling approach which enables efficient and robust solutions for industrial applications. … (more)
- Is Part Of:
- Chemical engineering science. Volume 223(2020)
- Journal:
- Chemical engineering science
- Issue:
- Volume 223(2020)
- Issue Display:
- Volume 223, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 223
- Issue:
- 2020
- Issue Sort Value:
- 2020-0223-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-09-21
- Subjects:
- CFD -- DEM -- CFD-DEM coupling -- GPU -- Particle-fluid flow -- ANSYS Fluent
Chemical engineering -- Periodicals
Génie chimique -- Périodiques
Chemical engineering
Periodicals
Electronic journals
660 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00092509 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ces.2020.115712 ↗
- Languages:
- English
- ISSNs:
- 0009-2509
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3146.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13928.xml