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Computational Thermal Sciences: An International Journal

Publicou 6 edições por ano

ISSN Imprimir: 1940-2503

ISSN On-line: 1940-2554

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: 1.5 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 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: 0.3 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: 0.00017 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: 0.28 SJR: 0.279 SNIP: 0.544 CiteScore™:: 2.5 H-Index: 22

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Comprehensive investigation of an inverse geometry problem in heat conduction via adjoint-based optimization method

Volume 3, Edição 3, 2011, pp. 187-201
DOI: 10.1615/ComputThermalScien.2011001776
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RESUMO

An inverse geometry problem in heat conduction is solved using different versions of an iterative regularization method. The algorithm consists of direct and inverse problems, which aims to modification of geometry. The direct problem is solved using a finite element method(FEM). The employed iterative regularization method is constructed using the adjoint and sensitivity equations that are used to calculate the gradient of the objective function and the optimal step size, respectively. Results shown that the Powel-Beale version has the best convergence rate compared to the Fletcher-Reeves and Polak-Ribiere versions of the conjugate gradient method (CGM). Effects of geometric parameters, location and number of sensors, heat flux value, error of sensors, and size of meshes are studied. Results show that as the sensors get closer to the unknown boundary, both accuracy and convergence rate of the algorithm improve. Increasing the number of sensors has a positive effect on accuracy and convergence rate, only when it is smaller than a certain number. Presence of a measurement error leads to inaccurate estimation of the geometry shape. A proper size of mesh has the best convergence and accuracy in shape identification problem.

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