Parallel high‐resolution finite volume simulation of particulate processes

Gunawan, Rudiyanto; Fusman, Irene; Braatz, Richard D.

doi:10.1002/aic.11484

Cited by 46 publications

(35 citation statements)

References 35 publications

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“…Parallel computing has been studied for several years, but its application to particulate processes described by population balance models has been limited to a few studies in crystallization [24,25]. The procedure followed herein for parallelizing PBMs involved three steps: locating portions of the code that are most time consuming with tools like MATLAB profiler; applying one of the two approaches for parallelism as appropriate; and finally optimizing for minimal variable transfer overhead.…”

Section: Discussionmentioning

confidence: 99%

“…There has been some recent work on the parallelization of PBM simulations prior to this study. Gunawan et al formulated an efficient way of parallelizing High-Resolution Finite Volume-(HRFV-) solved PBEs by assigning the operations on particles in the first half of the size range that were more computationally intensive to processors of greater rank and operations on the other size range half of decreasing load intensity to higher ranked processors [24]. Their strategy enabled efficient load distribution and resulted in a near linear speedup.…”

Section: Introductionmentioning

confidence: 99%

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Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Prakash

Chaudhury

Ramachandran

2013

Modelling and Simulation in Engineering

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Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB's Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Prakash

Chaudhury

Ramachandran

2013

Modelling and Simulation in Engineering

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“…It should be noted that the current PBM simulation takes 30 min in a serial simulation. Parallel simulation of PBMs using multicore CPU computing has shown to be efficient in further reducing the computational time of simulating a PBM thus enhancing its utility in control and optimization [52,53].…”

Section: Comparison Of Simulation Time Between Dem and Pbmmentioning

confidence: 99%

Mathematical Development and Comparison of a Hybrid PBM-DEM Description of a Continuous Powder Mixing Process

Sen

Dubey

Singh

et al. 2013

Journal of Powder Technology

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is paper describes the development of a multidimensional population balance model (PBM) which can account for the dynamics of a continuous powder mixing/blending process. e PBM can incorporate the important design and process conditions and determine their effects on the various critical quality attributes (CQAs) accordingly. e important parameters considered in this study are blender dimensions and presence of noise in the inlet streams. e blender dynamics have been captured in terms of composition of the ingredients, (relative standard deviation) RSD, and (residence time distribution) RTD. PBM interacts with discrete element modeling (DEM) via one-way coupling which forms a basic framework for hybrid modeling. e results thus obtained have been compared against a full DEM simulation which is a more fundamental particle-level model that elucidates the dynamics of the mixing process. Results show good qualitative agreement which lends credence to the use of coupled PBM as an effective tool in control and optimization of mixing process due to its relatively fewer computational requirements compared to DEM.

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“…Handling of the coupling conditions between the high-dimensional PBE and the 3D flow and species concentration equations will be more challenging when the PBE is split into a system of 1D equations. Further, a special technique is needed for the load balancing in parallel computations of the system of operator-split 1D equations [11].…”

Section: Introductionmentioning

confidence: 99%

An operator-splitting Galerkin/SUPG finite element method for population balance equations : stability and convergence

Ganesan

2012

ESAIM: M2AN

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Abstract. We present a heterogeneous finite element method for the solution of a high-dimensional population balance equation, which depends both the physical and the internal property coordinates. The proposed scheme tackles the two main difficulties in the finite element solution of population balance equation: (i) spatial discretization with the standard finite elements, when the dimension of the equation is more than three, (ii) spurious oscillations in the solution induced by standard Galerkin approximation due to pure advection in the internal property coordinates. The key idea is to split the high-dimensional population balance equation into two low-dimensional equations, and discretize the low-dimensional equations separately. In the proposed splitting scheme, the shape of the physical domain can be arbitrary, and different discretizations can be applied to the low-dimensional equations. In particular, we discretize the physical and internal spaces with the standard Galerkin and Streamline Upwind Petrov Galerkin (SUPG) finite elements, respectively. The stability and error estimates of the Galerkin/SUPG finite element discretization of the population balance equation are derived. It is shown that a slightly more regularity, i.e. the mixed partial derivatives of the solution has to be bounded, is necessary for the optimal order of convergence. Numerical results are presented to support the analysis.Mathematics Subject Classification. 35K20, 65M60, 65M12.

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Parallel high‐resolution finite volume simulation of particulate processes

Cited by 46 publications

References 35 publications

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Mathematical Development and Comparison of a Hybrid PBM-DEM Description of a Continuous Powder Mixing Process

An operator-splitting Galerkin/SUPG finite element method for population balance equations : stability and convergence

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