Automatic Model Reduction of Linear Population Balance Models by Proper Orthogonal Decomposition
نویسندگان
چکیده
منابع مشابه
Automatic Model Reduction of Linear Population Balance Models by Proper Orthogonal Decomposition
This paper discusses the use of Proper Orthogonal Decomposition (POD) for the model reduction of particle processes in fluid flow described by Population Balance Equations (PBEs). This class of processes is very important for chemical engineering. As detailed models of such processes turn out to be very complicated, POD is an attractive way to obtain reduced models of low order. This paper repo...
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Zusammenfassung Der Beitrag beschreibt den Einsatz der Proper Orthogonal Decomposition (POD) für die Modellreduktion von Partikelprozessen in fluider Strömung. Diese Prozessklasse ist von großer Bedeutung für die chemische und pharmazeutische Industrie. Physikalische Modelle solcher Prozesse sind häufig sehr komplex und für Regelungsaufgaben wenig geeignet. POD bietet hier eine attraktive Mögli...
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Mathematical models for human tissue and blood flow both represent time dependent nonlinear partial differential equations in three space dimensions. Their numerical solution based on appropriate space/time discretizations requires computational times that even when using state-of-the-art algorithmic solvers are far from being acceptable for real time OR scenarios. A way to overcome this diffic...
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Proper Orthogonal Decomposition (POD), alternatively known as Principal Component Analysis or the Karhunen-Loève decomposition, is a model-reduction technique which generates the optimal linear subspace of dimension D for a given set of higher-dimensional data. That is, if the data are contained within an attractor, the POD process can produce the affine linear space that best approximates the ...
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2015
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2015.05.019