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.

Volume Subject Area:
Heat Transfer in Energy Systems
Topics:
Algorithms, Artificial neural networks, Fluid dynamics, Heat exchangers, Heat transfer, Modeling, Pipes, Polynomials, Design, Heat transfer coefficients, Prandtl number, Pressure drop, Testing, Trains
This content is only available via PDF.
You do not currently have access to this content.