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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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THE FUTURE OF CFD AND THE CFD OF THE FUTURE

Том 4, Выпуск 6, 2012, pp. 517-524
DOI: 10.1615/ComputThermalScien.2012006511
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Краткое описание

CFD is undergoing a rapid evolution. The distinction between CFD and the so called Structural FE codes is disappearing. Solids and plastics are already being viewed as special subsets of fluids and eventually the structural and fluid codes will merge together and evolve as multi-physics design tools. The algorithmic advancements will have to include a much stronger emphasis on rheology, fluid structure interaction and physics that includes the complete spectrum of solids, plastics, liquids, gases and other phases in-between. A second evolution is occurring in computer architecture. The codes of today, with a few exceptions, rely heavily on iterative matrix solvers. The algorithmic core of most of the current CFD codes was developed when the paradigm was a single CPU with limited memory or a parallel system with multiple CPUs − at the most numbered in 100 s − in a MIMD or SIMD architecture. Hence the methodology used is that adapted to such configurations. The architecture of the future will be multi-core cloud or grid-computing CPUs, GPUs or ASICs. The use of matrix solvers for such architecture will present bottlenecks associated with communication and management software. This will necessitate a new look at how to solve the governing equations and how to do so effectively within the paradigm of cloud computing. A third evolution will occur in the implementation of CFD as a design tool. Increasingly, the emphasis will shift to embedded applications so that CFD as a stand-alone tool will practically disappear. CFD as an embedded application will then merge with virtual reality tools and be included in intelligent AI type interfaces where the emphasis is on the design function of interest rather than on the CFD per se. CFD will then be part of an interactive tool such as the one for x-ray tomography or the performance analysis of an aircraft engine and, with increasing task specific embedded applications, the day is perhaps not far off when specific ASIC (Application Specific Integrated Circuits) chips may implement CFD for such applications. This will give rise to EVR (Engineering Virtual Reality) Design Tools. In summary, CFD will surely become ubiquitous but buried to such an extent that it will be rarely obvious that a CFD tool is being used. Everyone knows there is an engine in a car yet hardly anyone cares to ask what that engine is.

ЦИТИРОВАНО В
  1. Runchal Akshai Kumar, Rao Madhukar M., CFD of the Future: Year 2025 and Beyond, in 50 Years of CFD in Engineering Sciences, 2020. Crossref

  2. Taylor Simon J. E., Anagnostou Anastasia, Kiss Tamas, Terstyanszky Gabor, Kacsuk Peter, Fantini Nicola, Lakehal Djamel, Costes Joris, Enabling Cloud-Based Computational Fluid Dynamics With a Platform-as-a-Service Solution, IEEE Transactions on Industrial Informatics, 15, 1, 2019. Crossref

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