Abstract: In this study, the effect of nanofluids on the pool film
boiling was experimentally investigated at saturated condition under
atmospheric pressure. For this purpose, four different water-based
nanofluids (Al2O3, SiO2, TiO2 and CuO) with 0.1% particle volume
fraction were prepared. To investigate the boiling heat transfer, a
cylindrical rod with high temperature was used. The rod heated up to
high temperatures was immersed into nanofluids. The center
temperature of rod during the cooling process was recorded by using
a K-type thermocouple. The quenching curves showed that the pool
boiling heat transfer was strongly dependent on the nanoparticle
materials. During the repetitive quenching tests, the cooling time
decreased and thus, the film boiling vanished. Consequently, the
primary reason of this was the change of the surface characteristics
due to the nanoparticles deposition on the rod-s surface.
Abstract: The effect of Alumina nanoparticle size on thermophysical
properties, heat transfer performance and pressure loss characteristics of
Aviation Turbine Fuel (ATF)-Al2O3 nanofluids is studied experimentally for
the proposed application of regenerative cooling of semi-cryogenic rocket
engine thrust chambers. Al2O3 particles with mean diameters of 50 nm or 150
nm are dispersed in ATF. At 500C and 0.3% particle volume concentration,
the bigger particles show increases of 17% in thermal conductivity and 55% in
viscosity, whereas the smaller particles show corresponding increases of 21%
and 22% for thermal conductivity and viscosity respectively. Contrary to these
results, experiments to study the heat transfer performance and pressure loss
characteristics show that at the same pumping power, the maximum
enhancement in heat transfer coefficient at 500C and 0.3% concentration is
approximately 47% using bigger particles, whereas it is only 36% using
smaller particles.
Abstract: An accurate and proficient artificial neural network
(ANN) based genetic algorithm (GA) is developed for predicting of
nanofluids viscosity. A genetic algorithm (GA) is used to optimize
the neural network parameters for minimizing the error between the
predictive viscosity and the experimental one. The experimental
viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15
to 343.15 K and volume fraction up to 15% were used from
literature. The result of this study reveals that GA-NN model is
outperform to the conventional neural nets in predicting the viscosity
of nanofluids with mean absolute relative error of 1.22% and 1.77%
for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results
of this work have also been compared with others models. The
findings of this work demonstrate that the GA-NN model is an
effective method for prediction viscosity of nanofluids and have
better accuracy and simplicity compared with the others models.