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Accelerating Population Balance Model - based particulate process simulations via parallel computing
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Prakash, Anuj Varghese. Accelerating Population Balance Model - based particulate process simulations via parallel computing. Retrieved from https://doi.org/doi:10.7282/T3C53JKM

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TitleAccelerating Population Balance Model - based particulate process simulations via parallel computing
NamePrakash, Anuj Varghese (author); Ramachandran, Rohit (chair); Ierapetritou, Marianthi (internal member); Dutt, Meenakshi (internal member); Pandey, Preetanshu (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2013
Other Date2013-01 (degree)
SubjectChemical and Biochemical Engineering, Parallel programs (Computer programs), Parallel algorithms
Extentix, 74 p. : ill.
DescriptionThe use of Population Balance Models (PBM) for simulating dynamics of particulate systems are inevitably limited at some point by the demands they place on computational resources. PBMs are widely used to describe the time evolutions and distributions of many industrial particulate processes, and its efficient and quick simulation would certainly be beneficial for process design, control and optimization. This thesis is an elucidation of how MATLAB's Parallel Computing Toolbox (PCT), a third-party toolbox called JACKET, and the MATLAB Distributed Computing Server (MDCS) may be combined with algorithmic modification of the PBM to speed up these computations on a CPU (Central Processing Unit), GPU (Graphics Processing Unit) and a computer cluster respectively. Parallel algorithms were developed for three dimensional and four dimensional population balance models incorporating hardware class-specific parallel constructs such as SPMD and gfor. Results indicate significant reduction in computational time without compromising numerical accuracy for all cases except for the GPU. The GPU seemed promising for larger problems despite its limitations of lower clock speeds and on-board memory compared to the CPU. Evaluations of the speedup and scalability further affirm the algorithms' performance.
NoteM.S.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Anuj Varghese Prakash
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3C53JKM
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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