In this paper a genetic algorithm-polynomial neural network approach is used in order to model the effect of important parameters on heat transfer as well as fluid flow characteristics for a double-pipe helical heat exchanger by using numerical-certified results. In this way, overall heat transfer coefficient (Uo), inner and annular pressure drop (ΔPin, ΔPan) are modeled with respect to the variation of inner and annular dean number, inner and annular Prandtl number, and pitch of coil which are defined as input (design) variables. The numerical-certified data was randomly divided into test and train sections which the former is used for benchmark. The GA-PNN structure was instructed by 75 percent of the numerical-validated data. 25 percent of the primary data which had been considered for testing procedure were entered into GA-PNN proposed models and results were compared by statistical criteria.
- Heat Transfer Division
Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger
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Mehrabi, M, Pashaee, T, Sharifpur, M, & Meyer, JP. "Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger." Proceedings of the ASME 2013 Heat Transfer Summer Conference collocated with the ASME 2013 7th International Conference on Energy Sustainability and the ASME 2013 11th International Conference on Fuel Cell Science, Engineering and Technology. Volume 1: Heat Transfer in Energy Systems; Thermophysical Properties; Theory and Fundamental Research in Heat Transfer. Minneapolis, Minnesota, USA. July 14–19, 2013. V001T01A015. ASME. https://doi.org/10.1115/HT2013-17194
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