| Summary: | 碩士 === 國立成功大學 === 機械工程學系碩博士班 === 101 === In this study, the multi-parameter constrained optimization procedure integrating the design of experiments (DOE), full factorial experimental design (FFED), genetic algorithm (GA) and computational fluid dynamics (CFD) is proposed to design two-dimensional wavy channel with nanofluids (Cu/water, /water and CuO/water). The elliptical, coupled, steady-state, two-dimensional governing partial differential equations for laminar forced convection of nanofluids are solved numerically using the finite volume approach.
Some important parameters for the influences of heat transfer enhancement such as Reynolds number, the particle volume concentration, the wavy channel amplitude and the wavy numbers on the enhancement of nanofluid(100 nm) heat transfer have been investigated. The numerical results with single-phase model are first validated with the available data in the literature, the maximum discrepancy within 8%, and then further extend to two phase model.
The numerical results indicates that the thermal enhancement can achieve 15%、24% in the wavy channel flow compared with pure fluid, with the particle volume concentrationφ= 3% and φ= 5% of Cu/water nanofluids. The averaged Nusselt number increases with the increase of the particle concentration and Reynolds number. Among the mixtures studied, the Cu/water nanofluid appears to offer a better heat transfer enhancement than Al2O3/water and CuO/water. On the other hand, the friction factor of the nanofluids is also discussed, and it seems that the friction factor mainly depends on the amplitude of the wavy wall rather than the nanoparticle volume concentration. Furthermore, two-phase model predict almost identical hydrodynamic fields but different thermal ones.
In addition, after the validation of the numerical results, the numerical optimization of this problem is also presented by using full factorial experimental design and genetic algorithm (GA) method. The objective function E which is defined as Thermal Performance Factor has developed a correlation function with three design parameters, wave amplitude, wavy numbers and the particle volume concentration.
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