首页|Parametric study and application of a data-mining model in 2D and 3D micro-fin tubes
Parametric study and application of a data-mining model in 2D and 3D micro-fin tubes
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
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 (α), number of starts (Nf), 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-ε 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 (η) up to 1.18. Results of the LS-SVR model showed that the percentage of average absolute error (AAE) between simulated and estimated j and f factors are 2.05% and 2.93% for 3D micro-fin tubes, respectively.
2D and 3D Micro-fin tubesEfficiency indexLeast squares support vector regression (LS-SVR)Turbulent numerical simulation
Soleimani S.、Eckels S.、Campbel M.
展开 >
Institute for Environmental Research (IER) Mechanical and Nuclear Engineering Department Kansas State University
NSTL
Elsevier
