Parametric study and application of a data-mining model in 2D and 3D micro-fin tubes

Micro-fins (<0.5 mm tall) are an engineered roughness with the advantage of reducing thermal resistance and the disadvantage of increased pressure drop when applied inside a tube in heat-exchange applications. The competing effects highlight the need for careful optimization that identifies micro-fin surfaces with the potential to match heat exchanger design needs. Hence, the objectives of this study are chosen to enable efficient optimization in future studies. The main goals are: (1) study the effects of micro-fin design variables on heat transfer and friction factors; and (2) evaluate the potential of a data-mining model as a surrogate of computational fluid dynamic (CFD) models in 2 dimensional (D) and 3D micro-fin tubes. This study applied conductive and convective heat transfer and turbulent fluid flow simulation to evaluate the performance of different 2D and 3D micro-fin tubes. Different configurations were generated by varying micro-fin height (e), helix angle (alpha), number of starts (N-f), and discontinuity features. Coupled solid and periodic fluid domains were applied in ANSYS Fluent 19.1. Performance was mapped for 210 different simulations (including a smooth tube) using a realizable k-epsilon turbulence model at Reynolds number (Re) of 48,928. Two different least squares-support vector regression (LS- SVR) models were employed to estimate the Colburn j factor as a function of geometric variables and the Fanning friction factor (f) as a function of geometric variable and j factor. Results of the parametric study showed that the best 2D micro-fin tube can enhance efficiency index (eta) up to 1.18. Results of the LS-SVR model showed that the percentage of average absolute error (AAE) between simulated and estimated j andf factors are 2.05% and 2.93% for 3D micro-fin tubes, respectively.

Automated summary

This study investigates the effects of micro-fin design on heat transfer and friction factors, and evaluates the potential of a data-mining model as a surrogate for computational fluid dynamic (CFD) models in 2D and 3D micro-fin tubes.