Soft Computing Applications in Thermal Energy Systems

  • Arturo Pacheco-Vega
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 269)


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.


Artificial Neural Network Heat Exchanger Fuzzy Logic Fuzzy Controller Fuzzy Inference System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Arturo Pacheco-Vega
    • 1
  1. 1.California State UniversityLos AngelesUSA

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