Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs
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, namel...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/475478 |
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| author | Anuj V. Prakash Anwesha Chaudhury Rohit Ramachandran |
| author_facet | Anuj V. Prakash Anwesha Chaudhury Rohit Ramachandran |
| author_sort | Anuj V. Prakash |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ac642afbf84f47f993eac645fe255928 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-ac642afbf84f47f993eac645fe2559282025-02-03T06:13:51ZengWileyModelling and Simulation in Engineering1687-55911687-56052013-01-01201310.1155/2013/475478475478Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUsAnuj V. Prakash0Anwesha Chaudhury1Rohit Ramachandran2Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USAComputer-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.http://dx.doi.org/10.1155/2013/475478 |
| spellingShingle | Anuj V. Prakash Anwesha Chaudhury Rohit Ramachandran Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs Modelling and Simulation in Engineering |
| title | Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs |
| title_full | Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs |
| title_fullStr | Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs |
| title_full_unstemmed | Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs |
| title_short | Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs |
| title_sort | parallel simulation of population balance model based particulate processes using multicore cpus and gpus |
| url | http://dx.doi.org/10.1155/2013/475478 |
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