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标题：Improvements in Numerical Simulations of Isotachophoresis by Using Adaptive Mesh Refinement
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作者：Pablo A. Kler ; Gustavo A. Rios Rodríguez ; Fabio A. Guarnieri 等
期刊名称：Mecánica Computacional
印刷版ISSN：2591-3522
出版年度：2010
卷号：XXIX
期号：35
出版社：CIMEC-INTEC-CONICET-UNL
摘要：Isotachophoresis (ITP) belongs to a wide group of analytical techniques named as Electrophoretic separations, which also includes capillary electrophoresis, isoelectric focusing and free ﬂow electrophoresis. Electrophoretic separations are based on the dissimilar mobility of ionic species under the action of an external electric ﬁeld. In particular in ITP assays, the sample is introduced between a fast leading electrolyte and a slow terminating electrolyte. After applying a difference of electric potential, a low electrical ﬁeld is created in the leading electrolyte and a high electrical ﬁeld in the terminating electrolyte. The constituents will completely separate from each other, and concentrate at an equilibrium point, surrounded by sharp electrical ﬁeld differences. These sharpnesses in the electric ﬁeld give place to spurious oscillations due to the high local values of the Pèclet number. In order to avoid this oscillations, a large amount of nodes in the mesh are required. The additions of points have to be cleverly done in order to avoid a substantial increase in the computational costs. This is achieved by using an h-adaptive mesh reﬁnement (AMR) method. In this work, we present two examples of ITP in microﬂuidic chips, in order to show the advantages of using AMR in these kind of problems. Examples were solved by using a generalized numerical model of electrophoresis on microﬂuidic devices previously presented. The model is based on the set of equations that governs electrical phenomena (Poisson equation), ﬂuid dynamics (Navier-Stokes equations), mass transport (Nerst-Planck equation) and chemical reactions. The numerical simulations were carried out by using PETSc-FEM (Portable Extensible Toolkit for Scientiﬁc Computation - Finite Elements Method), in a Python environment using high performance parallel computing and solving techniques based on domain decomposition methods.