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Journal of Enhanced Heat Transfer

Published 8 issues per year

ISSN Print: 1065-5131

ISSN Online: 1563-5074

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: 2.3 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: 1.8 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: 0.2 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: 0.00037 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: 0.6 SJR: 0.433 SNIP: 0.593 CiteScore™:: 4.3 H-Index: 35

Indexed in

A Genetic Algorithm Optimization Technique for Compact High Intensity Cooler Design

Volume 3, Issue 4, 1996, pp. 281-290
DOI: 10.1615/JEnhHeatTransf.v3.i4.40
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ABSTRACT

This paper initially reviews the operation and design criteria for a compact high intensity cooler (CHIC) unit as used in avionic equipment. Here high heat loads are dissipated via multiple impinging jets fed sequentially through a series of fins connected with a bus bar to the heat source. The analytical basis for the heat transfer design, most of which has been published previously, is shown to predict the performance of CHIC units to a high degree of accuracy. This then permits optimizing the design. Most optimization techniques depend on continuous variables, while in the design of CHIC unit many of the critical geometrical variables must assume discrete values. A genetic algorithm, generally not well known in engineering circles, that looks for an optimum by simulating an evolutionary process was found to be satisfactory for this problem with its mixture of discrete and continuous variables. It is also shown that in an actual optimization problem, where the fluid pressure drop across the unit has to be balanced against a low overall thermal resistance, an optimum geometrical design can be determined. This design is an improvement over the empirical "best" design previously reported in the literature.

CITED BY
  1. Milano Michele, Koumoutsakos Petros, A Clustering Genetic Algorithm for Cylinder Drag Optimization, Journal of Computational Physics, 175, 1, 2002. Crossref

  2. Cheung Brian C., Carriveau Rupp, Ting David S.K., Multi-objective optimization of an underwater compressed air energy storage system using genetic algorithm, Energy, 74, 2014. Crossref

  3. Najafi Hamidreza, Najafi Behzad, Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm, Heat and Mass Transfer, 46, 6, 2010. Crossref

  4. Bryden Kenneth M., Ashlock Daniel A., McCorkle Douglas S., Urban Gregory L., Optimization of heat transfer utilizing graph based evolutionary algorithms, International Journal of Heat and Fluid Flow, 24, 2, 2003. Crossref

  5. Eckert E.R.G., Goldstein R.J., Ibele W.E., Simon T.W., Kuehn T.H., Strykowski P.J., Tamma K.K., Bar-Cohen A., Heberlein J.V.R., Davidson J.H., Bischof J., Kulacki F., Kortshagen U., Heat transfer — a review of 1996 literature, International Journal of Heat and Mass Transfer, 43, 8, 2000. Crossref

  6. Bryden K.M., McCorkle D.S., Evolutionary optimization of energy systems using population graphing and neural networks, Advances in Engineering Software, 35, 5, 2004. Crossref

  7. McCorkle D.S., Bryden K.M., Carmichael C.G., A new methodology for evolutionary optimization of energy systems, Computer Methods in Applied Mechanics and Engineering, 192, 44-46, 2003. Crossref

  8. Gosselin Louis, Tye-Gingras Maxime, Mathieu-Potvin François, Review of utilization of genetic algorithms in heat transfer problems, International Journal of Heat and Mass Transfer, 52, 9-10, 2009. Crossref

  9. Najafi Hamidreza, Najafi Behzad, Hoseinpoori Pooya, Energy and cost optimization of a plate and fin heat exchanger using genetic algorithm, Applied Thermal Engineering, 31, 10, 2011. Crossref

  10. Mishra Manish, Das P.K., Sarangi Sunil, Second law based optimisation of crossflow plate-fin heat exchanger design using genetic algorithm, Applied Thermal Engineering, 29, 14-15, 2009. Crossref

  11. Najafi Hamidreza, Najafi Behzad, Multi-objective optimization of a fire-tube heat recovery steam generator system, 2009 IEEE Electrical Power & Energy Conference (EPEC), 2009. Crossref

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