Prediction of heat transfer and fluid flow in a cross-corrugated tube using numerical methods, artificial neural networks and genetic algorithms

In this paper, multi-objective optimization of geometric parameters of spirally-cross-corrugated (SCC) tubes is carried out using numerical methods, genetic algorithms (GAs), and artificial neural networks (ANNs). First, the turbulent flow is numerically characterized in various SCC tube geometries using a finite volume method with the realizable k-epsilon turbulence model. In this approach, the heat transfer coefficient and friction factor f in tubes are calculated. First, two parameters (corrugation pitch-to-diameter ratio (PR = p/D) and corrugation depth-to-diameter ratio (DR = e/D)) are examined in a turbulent flow regime that affects the strength of quadruple longitudinal vortex flows and thermal characteristics. At the final step, using the obtained polynomials for neural networks, multi-objective genetic algorithms (NSGA II) are employed for Pareto based multi-objective optimization of flow parameters in such tubes. This analysis considers two conflicting parameters, f Re and Nusselt number Nu with respect to three design variables, Reynolds number Re, values of PR and DR. Some interesting and important relationships between the parameters and variables mentioned above emerge as useful optimal design principles involved in the heat transfer of such tubes through Pareto based multi-objective optimization. Such important optimal principles would not have been obtained without the use of a combination of numerical techniques, ANN modeling, and the Pareto optimization.