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In the current research, surface-modified SiO2 nanoparticles were used upon immersion in an applied base fluid (ethylene glycol:water = 1:1). The atomic layer deposition method (ALD) was introduced to obtain a thin layer of TiO2 to cover the surface of SiO2 particles. After the ALD modification, the TiO2 content was monitored by energy dispersive X-ray spectroscopy (EDS). Transmission electron microscopy (TEM) and FT-IR spectroscopy were applied for the particle characterization. The nanofluids contained 0.5, 1.0, and 1.5 volume% solid particles and zeta potential measurements were examined in terms of colloid stability. A rotation viscosimeter and thermal conductivity analyzer were used to study the nanofluids' rheological properties and thermal conductivity. These two parameters were investigated in the temperature range of 20 °C and 60 °C. Based on the results, the thin TiO2 coating significant impacted these parameters. [ABSTRACT FROM AUTHOR]

In this paper, we present a study on thermal conductivity and viscosity of nanofluids containing novel atomic layer deposition surface-modified carbon nanosphere (ALD-CNS) and carbon nanopowder (ALD-CNP) core-shell nanocomposites. The nanocomposites were produced by atomic layer deposition of amorphous TiO2. The nanostructures were characterised by scanning (SEM) and transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, thermogravimetry/differential thermal analysis (TG/DTA) and X-ray powder diffraction (XRD). High-concentration, stable nanofluids were prepared with 1.5, 1.0 and 0.5 vol% nanoparticle content. The thermal conductivity and viscosity of the nanofluids were measured, and their stability was evaluated with Zeta potential measurements. The ALD-CNS enhanced the thermal conductivity of the 1:5 ethanol:water mixture by 4.6% with a 1.5 vol% concentration, and the viscosity increased by 37.5%. The ALD-CNS increased the thermal conductivity of ethylene–glycol by 10.8, whereas the viscosity increased by 15.9%. The use of a surfactant was unnecessary due to the ALD-deposited TiO2 layer. [ABSTRACT FROM AUTHOR]

Nanofluids are the most attractive mean to enhance the performance of heat transfer devices. Several types of nanoparticles were utilized as they have high thermal conductivity. Renewable energy makes use of these nanofluids special in solar collectors. Evacuated tube solar collector is one of the most utilized solar collectors in thermal applications. CeO 2 nanoparticles were used in the presented study. The mean diameter of CeO 2 was 25 nm. A stable CeO 2 /water was made. The stability was checked using Zeta potential machine. Experiments were carried out using three different volume concentration of CeO 2 nanoparticles of 0.015%, 0.025%, and 0.035%. The thermal performance of the evacuated tube solar collector was examined at different mass flux rates. Results showed that the temperature difference between inlet and outlet flow and absorbed energy increase when nanofluids are used. The volume fraction flow rate of 0.035% at the mass flux rate of 0.017 kg/s.m 2 had the maximum heat removable factor, the thermo-optical characteristic of the collector, and the thermal loss coefficient. The thermo-optical characteristic of the collector of tube solar corrector is raised up to 34%. [ABSTRACT FROM AUTHOR]

We report heat pulse experiments at room temperature that cannot be described by Fourier's law. The experimental data are modeled properly by the Guyer-Krumhansl equation, in its over-diffusion regime. The phenomenon may be due to conduction channels with differing conductivities and parallel to the direction of the heat flux. [ABSTRACT FROM AUTHOR]

An artificial neural network (NN)-based solution of the inverse heat conduction problem of identifying the temperature-dependent volumetric heat capacity function of a solid material is presented in this paper. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The volumetric heat capacity versus temperature function is to be determined using the measured transient temperature history of a single sensor. In this study, noiseless and noisy artificial measurements were generated by the numerical solution of the corresponding direct heat conduction problem. The inverse problem was solved by back-propagation and radial basis function type neural networks applying the whole history mapping approach. The numerical tests included the comparison of two different data representations of the network inputs (i.e., temperature vs. time and time vs. temperature) and accuracy analysis of the two network types with noiseless and noisy inputs. Based on the results presented, it can be stated that feed-forward NNs are powerful tools in a non-iterative solution of function estimation inverse heat conduction problems and they are likely to be very effective in evaluation of real measured temperature histories to determine the volumetric heat capacity as an arbitrary function of temperature. [ABSTRACT FROM AUTHOR]

Abstract: A new application of the cost function-based rearrangement of genes (proposed by Liu (2008) ) is presented in this paper through the genetic algorithm-based solution of the inverse heat conduction problem of identifying the temperature dependent thermal conductivity of a solid material using transient temperature histories. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. Through the solution of the inverse problem (using simulated measurements), different approaches of the application of the rearrangement of genes were studied and compared. Application of the rearrangement significantly improved the convergence performance and accuracy of the inverse solution compared to a real-valued genetic algorithm, which was adapted to the problem by the authors. In the algorithm that performed best, the rearrangement was applied in an approach different from Liu’s. The effect of random noise added to the temperature history and the effect of regularization was also studied. With significant improvement in computational efficiency, the proposed algorithm is likely to be very effective in evaluation of real measured temperature histories to determine thermophysical properties. [Copyright &y& Elsevier]

A comparative research on stability, viscosity (µ), and thermal conductivity (k) of carbon nanosphere (CNS) and carbon nanopowder (CNP) nanofluids was performed. CNS was synthesized by the hydrothermal method, while CNP was provided by the manufacturer. Stable nanofluids at high concentrations 0.5, 1.0, and 1.5 vol% were prepared successfully. The properties of CNS and CNP nanoparticles were analyzed with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), specific surface area (SBET), X-ray powder diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and energy dispersive X-ray analysis (EDX). The CNP nanofluids have the highest k enhancement of 10.61% for 1.5 vol% concentration compared to the base fluid, while the CNS does not make the thermal conductivity of nanofluids (knf) significantly higher. The studied nanofluids were Newtonian. The relative µ of CNS and CNP nanofluids was 1.04 and 1.07 at 0.5 vol% concentration and 30 °C. These results can be explained by the different sizes and crystallinity of the used nanoparticles. [ABSTRACT FROM AUTHOR]

Nanofluids obtained from halloysite and de-ionized water (DI) were prepared by using surfactants and changing pH for heat-transfer applications. The halloysite nanotubes (HNTs) nanofluids were studied for several volume fractions (0.5, 1.0, and 1.5 vol%) and temperatures (20, 30, 40, 50, and 60 °C). The properties of HNTs were studied with a scanning electron microscope (SEM), energy-dispersive X-ray analysis (EDX), Fourier-transform infrared (FT-IR) spectroscopy, X-ray powder diffraction (XRD), Raman spectroscopy and thermogravimetry/differential thermal analysis (TG/DTA). The stability of the nanofluids was proven by zeta potentials measurements and visual observation. With surfactants, the HNT nanofluids had the highest thermal conductivity increment of 18.30% for 1.5 vol% concentration in comparison with the base fluid. The thermal conductivity enhancement of nanofluids containing surfactant was slightly higher than nanofluids with pH = 12. The prepared nanofluids were Newtonian. The viscosity enhancements of the nanofluid were 11% and 12.8% at 30 °C for 0.5% volume concentration with surfactants and at pH = 12, respectively. Empirical correlations of viscosity and thermal conductivity for these nanofluids were proposed for practical applications. [ABSTRACT FROM AUTHOR]

Abstract: An artificial neural network based solution of the inverse heat conduction problem of simultaneous identification of the temperature-dependent volumetric heat capacity and thermal conductivity function of a solid material is presented in this paper. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The volumetric heat capacity and thermal conductivity vs. temperature functions are to be determined using the measured transient temperature histories of two sensors. In this study noiseless and noisy artificial measurements were generated by the numerical solution of the corresponding direct heat conduction problem. The inverse problem was solved by multi-layer feed-forward neural network trained by back-propagation algorithm (BP) and radial basis function (RBF) type neural network applying the whole history mapping approach. As suggested by Czél et al. [1], a novel (time vs. temperature) representation of the input data was applied. Numerical tests were performed to analyze the accuracy of the two network types with noiseless and noisy inputs. Moreover, it is not necessary to retrain the network when the temperature range of the measurement is changed. Based on the presented results it can be stated that feed-forward neural networks are powerful tools in non-iterative solution of function estimation inverse heat conduction problems and they are likely to be very effective in evaluation of real measured temperature histories to simultaneously determine the volumetric heat capacity and thermal conductivity as an arbitrary function of temperature. [Copyright &y& Elsevier]