Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis (original) (raw)

Abstract

power. Maré et al. [7] have studied experimentally the thermal-hydraulic performance of Al 2 O 3 and carbon nanotubes (CNT) aqueous based nanofluids in a plate heat exchanger at low temperatures. The results show that the impact of viscosity and pressure drop is important and has to be taking into account before to use nanofluids. Because of high viscosity of SiO 2 /water based nanofluids at high particles concentration, Ferrouillat et al. [8]. reported that the pumping power is very high and that the practical benefits of using nanofluids is not important compared to base fluid. The rheological behavior of nanofluids could be strongly affected by the preparation method of nanofluids [9], viscosity of the base fluid, particles shape and size [10], particles concentration, temperature, surfactant and dispersion state of the nanoparticles. A number of project related to the viscosity of nanofluids have been descripted. The rheological behavior of TiO 2 /Ethylene Glycol nanofluids reported by Chen et al. [11] and the experimental results show that, the shear viscosity is a strong function of temperature, particle concentration and aggregation and the relative viscosity is independent of the temperature. Later on, the rheological behavior of TiO 2 /Ethylene Glycol nanofluids and the effects of particles shape, particles concentration and temperature on viscosity are examined by Chen et al. [12]. Kulkarni et al. [13] studied the effect of temperature for some nanofluids including CuO, Al 2 O 3 and SiO 2 in Ethylene Glycol and water. They confirmed that viscosity decreases exponentially with the increase of temperature. Also, Kole and Dey [14] found that the Brownian motion of the nanoparticles in the fluid plays an important role in understanding the viscosity of nanofluids such as Al 2 O 3 in car engine coolant nanofluid. On the other hand, the experimental data and the ranges of investigated variables such as the particle volume concentration, particle size and temperature for the viscosity Abstract This paper applies the model including backpropagation network (BPN) and principal component analysis (PCA) to estimate the effective viscosity of carbon nanotubes suspension. The effective viscosities of multiwall carbon nanotubes suspension are examined as a function of the temperature, nanoparticle volume fraction, effective length of nanoparticle and the viscosity of base fluids using artificial neural network. The obtained results by BPN-PCA model have good agreement with the experimental data.

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