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Soft Computing Applications in Thermal Energy Systems

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Book cover Soft Computing in Green and Renewable Energy Systems

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 269))

Abstract

Soft computing methodologies, of which artificial neural networks (ANNs), genetic algorithms (GAs), fuzzy logic (FL), and cluster analysis (CA) are elements, have gained much attention in recent years as practical tools to analyze complex problems in real-world applications. This chapter presents a review of SC applications in energy systems that belong to the field of thermal engineering. Special attention is devoted to the analysis, design and control of heat exchangers. For each methodology considered, the principles of operation are briefly described and discussed. Various applications to other energy systems are also mentioned.

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  1. California State University, Los Angeles, CA, 90032, USA

    Arturo Pacheco-Vega

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  1. Arturo Pacheco-Vega
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Editors and Affiliations

  1. Civil Engineering Associate, Ames Lab, US Department of Energy Research Affiliate, Iowa Bioeconomy Institute, Iowa State University, 354 Town Engineering Building, 50011, Ames, IA, USA

    Kasthurirangan Gopalakrishnan

  2. Department of Electrical and Computer Engineering, Iowa State University, 1113 Coover Hall, 50011, Ames, IA, USA

    Siddhartha Kumar Khaitan

  3. Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, P.O. Box 50329, 3603, Limassol, Cyprus

    Soteris Kalogirou

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Pacheco-Vega, A. (2011). Soft Computing Applications in Thermal Energy Systems. In: Gopalakrishnan, K., Khaitan, S.K., Kalogirou, S. (eds) Soft Computing in Green and Renewable Energy Systems. Studies in Fuzziness and Soft Computing, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22176-7_1

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  • DOI: https://doi.org/10.1007/978-3-642-22176-7_1

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