Using artificial neural networks to predict the rheological behavior of non-Newtonian graphene–ethylene glycol nanofluid

Journal of Thermal Analysis and Calorimetry - The ability of the artificial neural network (ANN) to predict the viscosity of graphene nanosheet/ethylene glycol ( $$\mu_{{\text{Gr/EG}}}$$ ) was...     

Predict the thermal conductivity of SiO2/water–ethylene glycol (50:50) hybrid nanofluid using artificial neural network

Journal of Thermal Analysis and Calorimetry - In the current work, after generating experimental data points for different volume fraction of nanoparticles ( $$\phi$$ ) and different temperatures,...     

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R ²) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.     

ANN modeling,cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer

Journal of Thermal Analysis and Calorimetry - In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71%...     

Application of SiO2–water nanofluid to enhance oil recovery

The present study aims to investigate effects of nanofluid flooding on EOR and also compares its performance with water flooding in field scale using the published experimental data provided from core-scale studies. The nanofluid is based on water including silica nanoparticles. The relative permeability curves of water, nanofluid and oil for a light crude oil core sample obtained in an experimental study are used in this numerical investigation. A 2D heterogeneous reservoir model is constructed using the permeability and porosity of the last layer of SPE-10 model. It has been shown that nanofluid flooding can substantially improve the oil recovery in comparison with the water flooding case. Afterward, the operational parameters of the 13 injection and production wells have been optimized in order to meet the maximum cumulative oil production. First, pattern search (PS) algorithm was implemented which has a good convergence speed, but with a high probability of trapping in local optimum points. Particle swarm optimization (PSO) approach has also been employed, which requires a large number of population (to approach the global optimum) with so many simulations. Accordingly, a hybrid PSO–PS algorithm with confined domain is proposed. The hybrid algorithm starts with PSO and depending on the distribution density of the values of each parameter, confines the searching domain and provides a proper initial guess to be used by PS. It is concluded that the hybrid PSO–PS method could obtain the optimal solution with a high convergence speed and reduced possibility of trapping in local optimums.

     

Entropy generation of graphene–platinum hybrid nanofluid flow through a wavy cylindrical microchannel solar receiver by using neural networks

Journal of Thermal Analysis and Calorimetry - Analyzing microchannel heat sinks (MCHS) in terms of the second thermodynamic law is useful, and it is necessary to examine MCHSs in terms of...     

Numerical and artificial neural network modeling study on the first-law and second-law performance of a novel helical heat sink filled with water–silver nanofluid

Journal of Thermal Analysis and Calorimetry - This paper is devoted to studying the first-law and second-law performances of a novel helical heat sink (HS). The biologically synthesized...     

Thermal–hydraulic efficiency management of spiral heat exchanger filled with Cu–ZnO/water hybrid nanofluid

Journal of Thermal Analysis and Calorimetry - In this paper, the effect of different channel spacing in spiral heat exchanger filled with Cu–ZnO–water hybrid nanofluid was investigated...     

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.     

Effects of novel hybrid nanofluid (TiO2–Cu/EG) and geometrical parameters of triangular rib mounted in a duct on heat transfer and flow characteristics

Journal of Thermal Analysis and Calorimetry - Heat transfer and flow characteristics have been numerically analyzed by using four different fluids [pure ethylene glycol (EG), TiO2/EG and Cu/EG...     

Forced convection magnetohydrodynamic Al2O3–Cu/water hybrid nanofluid flow over a backward-facing step

Forced convection hybrid nanofluid flow over a backward-facing step under a non-uniform magnetic field is numerically studied using a finite volume method. The external magnetic source is placed in the step edge. The study is performed for a range of nanoparticles volume fraction, φ, from 0 to 2%, Hartmann number, Ha, from 0 to 50, and Reynolds number, Re, from 100 to 300. Results show that the reattachment length reduces by increasing volume fraction of nanoparticles and by decreasing Reynolds number. The recirculation bubble weakens and the conductive heat transfer mode growth by increasing Hartmann number at weak magnetic field intensity. It totally disappears at high Hartmann number when the convective mode dominates. The average Nusselt number increases by increasing volume fraction of nanoparticles and varies with the Hartmann number. The effects of Lorentz force and hybrid nanoparticles on the heat transfer enhancement rates are strongly linked with volume fraction of nanoparticles and Hartmann and Reynolds numbers.

     

A CFD investigation of the effect of non-Newtonian behavior of Cu–water nanofluids on their heat transfer and flow friction characteristics

Journal of Thermal Analysis and Calorimetry - In the present study, a finite volume method is used to investigate heat transfer and flow friction behavior of non-Newtonian nanofluids. To study a...     

ANSYS simulation study of a low volume fraction CuO–ZnO/water hybrid nanofluid in a shell and tube heat exchanger

For the first time, the heat transfer performance of a CuO–ZnO (80:20)/water hybrid has been studied experimentally and numerically in a shell and tube heat exchanger under turbulent flow conditions nanofluid (STHE). All experiments are carried out with 0.01 ​vol% CuO–ZnO (80:20)/water hybrid nanofluid at Reynolds numbers (N_Re) ranging from 1900 to 17,500. The stabilized hybrid nanofluids (30 ​°C-Tube side) are then used as a coolant to reduce the hot fluid (60 ​°C-shell side) temperature using a STHE, with the results for the convective heat transfer coefficient, Nusselt number, friction factor, and pressure drop reported. The primary goal of this paper is to investigate the impact of hybrid nanoparticle mixing ratio optimization on STHE heat transfer efficiency under various operating conditions. According to the findings, the CuO–ZnO (80:20)/water hybrid nanofluid improved the heat transfer performance of the STHE at all Reynolds numbers. When using nanofluid over water, the Nusselt number and pressure drop were improved by approximately 33% and 13%, respectively. The hybrid nanofluid's maximum thermal performance factor and thermal efficiency enhancement were 1.45 and 7%, respectively, at N_Re ​= ​17,500. According to the study, the thermal conductivity of nanofluid varies by only 5% after ten trials. Furthermore, the ANSYS Fluent program was used to predict the behavior of the hybrid nanofluid in STHE, and the simulation results fit the experimental values very well.     

Preparation and characterization of crack-free sol–gel based SiO2–TiO2 hybrid nanoparticle film

Owing to the diverse potential applications of hybrid silica–titania thin films, the synthesis and characterization of these films have been carried out with a special focus on application as a medium index layer for multilayered functional coatings. For synthesis, tetraethylorthosilicate and titanium tetraisopropoxide were chosen as precursors for the formation of silica-titania hybrid thin films/nano-composites through an in situ sol–gel process. These films were sequentially obtained on Cu substrate utilizing spin coating. The hybrids were characterized by field emission scanning electron microscope, energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction, atomic force microscopy and Fourier transform infrared spectroscopy (FTIR). Field emission scanning electron microscope morphology displayed a smooth, densified and crack- free layer of silica-titania hybrid nanoparticles in the range of 20–71 nm after calcinations at low temperature of 300ºC for 1 h. X-ray diffraction pattern confirms the phases of titania with higher crystallinity and phase transformation at low temperature. The prepared films were uniform with low 8.852 nm RMS value. The stoichiometry of films was confirmed by EDX results. The FTIR spectroscopy indicated the establishment of heterogeneous chemical bonding between the Ti and Si surfaces through oxygen.     

Effect of MWCNT–Fe3O4/water hybrid nanofluid on the thermal performance of ribbed channel with apart sections of heating and cooling

Journal of Thermal Analysis and Calorimetry - A two-dimensional (2D) numerical simulation is performed to simulate the laminar forced convection of a nanofluid in a ribbed channel with apart...     

Thermal behavior and effect of SiO2 and PVA-SiO2 matrix on formation of Ni–Zn ferrite nanoparticles

Journal of Thermal Analysis and Calorimetry - The paper presents the synthesis of ZnFe2O4/SiO2, NiFe2O4/SiO2, Ni0.4Zn0.6Fe2O4/SiO2 and Ni0.4Zn0.6Fe2O4/PVA-SiO2 nanocomposites by a modified...     

Mullitization behavior and microstructural development of B2O3–Al2O3–SiO2 mixtures activated by high-energy ball milling

This paper reports on phase formation and microstructural development of mullite, derived from B₂O₃–Al₂O₃–SiO₂ mixtures activated with different milling methods. It is found that the addition of B₂O₃ has a positive effect on mullite phase formation from the mixture of Al₂O₃–SiO₂, which can be qualitatively attributed to the fact that B₂O₃ brought out higher nucleation density and enhanced reaction kinetics. Anisotropic grain growth of mullite was not observed in the unmilled mixture since mullite phase formation occurred after densification. Dense and interlocking microstructures of mullite ceramics were produced from the mixture activated with stainless steel milling media, whereas mullite whiskers were formed in the samples milled with tungsten carbide media. The high-energy ball milling refined the precursors and thus reduced the mullitization temperature. As a result, anisotropic microstructures and mullite whiskers could be derived from the milled mixtures. This way to produce anisotropic microstructures of mullite ceramics is very simple and requires temperatures much lower than those reported in the literature. This method is believed to be useful to fabricate reinforced mullite or mullite-related materials via in-situ anisotropic grain growth.