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Computational Thermal Sciences: An International Journal

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ISSN Печать: 1940-2503

ISSN Онлайн: 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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INVERSE VOF MESHLESS METHOD FOR EFFICIENT NONDESTRUCTIVE THERMOGRAPHIC EVALUATION

Том 7, Выпуск 2, 2015, pp. 105-121
DOI: 10.1615/ComputThermalScien.2015012337
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Краткое описание

A novel computational tool based on the localized radial-basis function (RBF) collocation (LRC) meshless method coupled with a volume-of-fluid (VoF) scheme capable of accurately and efficiently solving transient multidimensional heat conduction problems in composite and heterogeneous media is formulated and implemented. While the LRC meshless method lends its inherent advantages of spectral convergence and ease of automation, the VoF scheme allows one to effectively and efficiently simulate the location, size, and shape of cavities, voids, inclusions, defects, or deattachments in the conducting media without the need to regenerate point distributions, boundaries, or interpolation matrices. To this end, the inverse geometric problem of cavity detection can be formulated as an optimization problem that minimizes an objective function that computes the deviation of measured temperatures at accessible locations to those generated by the LRC-VoF meshless method. The LRC-VoF meshless algorithms will be driven by an optimization code based on the genetic algorithms technique, which can efficiently search for the optimal set of design parameters (location, size, shape, etc.) within a predefined design space. Initial guesses to the search algorithm will be provided by the classical 1D semi-infinite composite analytical solution, which can predict the approximate location of the cavity. The LRC-VoF formulation is tested and validated through a series of controlled numerical experiments. The proposed approach will allow solving the onerous computational inverse geometric problem in a very efficient and robust manner while affording its implementation in modest computational platforms, thereby realizing the disruptive potential of the proposed multidimensional high-fidelity nondestructive evaluation (NDE) method.

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