Evolutionary Methods Based Modeling and Analysis of Solar Thermal Systems

This book presents insights into the thermal performance of solar thermal collectors using both computational and experimental modeling. It consists of various computational and experimental case studies conducted by the authors on the solar thermal collector system. The authors begin by developing thermal modeling using a case study that shows the effect of different governing parameters. A few more experimental cases studies follow that highlight the energy, exergy, and environmental performance of the solar thermal collector system and to examine the performance of a modified solar collector system, illustrating performance improvement techniques. Finally, application of different evolutionary optimization techniques such as soft computing and evolutionary methods, like fuzzy techniques, MCDM methods like fuzzy logic based expert system (FLDS), Artificial Neural Network (ANN), Grey relational analysis (GRA), Entropy-Jaya algorithm, Entropy-VIKOR etc. are employed. - Covers improvement of solar thermal systems and advances in solar air collector systems, modeling, and optimization; - Includes modeling and parametric optimization issues for the practitioners of solar thermal industries; - Provides a new method for modeling and optimizing solar air collectors using actual case studies from the field.