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International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

A Tailored Strategy for PDE-Based Design of Hierarchically Structured Porous Catalysts

Volumen 6, Edición 2, 2008, pp. 179-190
DOI: 10.1615/IntJMultCompEng.v6.i2.60
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SINOPSIS

Optimization problems involving the solution of partial differential equations (PDE) are often encountered in the context of optimal design, optimal control, and parameter estimation. Based on the reduced-gradient method, a general strategy is proposed to solve these problems by reusing existing software. As an illustration, this strategy was employed to solve a PDE-based optimization problem that arises from the optimal design of the network of pore channels in hierarchically structured porous catalysts. A Fortran implementation was developed by combining a gradient-based optimization package, NLPQL, a multigrid solver, MGD9V, and a limited amount of in-house coding. The value and gradient of the objective function are computed by solving the discretized PDE and another system of linear equations using MGD9V. These are subsequently fed into NLPQL to solve the optimization problem. The PDE was discretized in terms of a finite volume method on a matrix of computational cells. The number of the cells ranged from 129 × 129 to 513 × 513, and the number of the optimization variables ranged from 41 to 201. Numerical tests were carried out on a Dell laptop with a 2.16-GHz Intel Core2 Duo processor. The results show that the optimization typically converges in a limited number (i.e., 9−48) of iterations. The CPU time is from 2.52 to 211.52 s. The PDE was solved 36−201 times in each of the numerical tests. This study calls for the use of our strategy to solve PDE-based optimization problems.

CITADO POR
  1. Keil Frerich J., Complexities in modeling of heterogeneous catalytic reactions, Computers & Mathematics with Applications, 65, 10, 2013. Crossref

  2. Wang Gang, Kleijn Chris R., Coppens Marc-Olivier, Multi-Scale PDE-Based Design of Hierarchically Structured Porous Catalysts, in Advanced Computational Methods in Science and Engineering, 71, 2009. Crossref

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