A well-trained artificial neural network for predicting the rheological behavior of MWCNT-Al(2)O(3) (30-70%)/oil SAE40 hybrid nanofluid

Self-assembled pH-stable gellan/κ-carrageenan bigel: Rheological studies and viscosity prediction by neural network

The current study focused on analysing and predicting the effect of physicochemical parameters on the rheological properties of the novel polysaccharide-based bigel. This is the first study to report a bigel fabricated entirely from polysaccharides and develop a neural network to predict the modulation in its rheology. This bi-phasic gel had gellan and κ-carrageenan as the constitutive elements in the aqueous and the organic phase, respectively. Physicochemical studies revealed the influence of organogel in eliciting high mechanical strength and smooth surface morphology to the bigel. Furthermore, variation in physiochemical parameters indicated the bigel's inertness towards change in pH of the system. However, variation in temperature led to a noticeable change in the rheology of the bigel. It was observed that after gradual decline, the bigel regained its original viscosity as the temperature increased beyond 80 °C. Insights from this study can pave way for the development of highly-stable polysaccharide bigels.

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO₂), graphene oxide (GO), and silica aerogel (SA) were investigated experimentally. Nanofluid viscosity was measured over a volume fraction range of VF = 0.25-1.5%, a temperature range of T = 5-55 °C, and a shear rate range of SR = 40-1000 rpm. The preparation of ternary-hybrid nanofluids involved a two-step process, and the nanomaterials were dispersed in SAE 10W40 using a magnetic stirrer and ultrasonic device. In addition, CeO₂, GO, and SA nanoadditives underwent X-ray diffraction-based structural analysis. The non-Newtonian (pseudoplastic) behavior of ternary-hybrid nanofluid at all temperatures and volume fractions is revealed by analyzing shear stress, dynamic viscosity, and power-law model coefficients. However, the nanofluids tend to Newtonian behavior at low temperatures. For instance, dynamic viscosity declines with increasing shear rate between 4.51% (at 5 °C) and 41.59% (at 55 °C) for the 1.5 vol% nanofluid. The experimental results demonstrated that the viscosity of ternary-hybrid nanofluid declines with increasing temperature and decreasing volume fraction. For instance, assuming a constant SR of 100 rpm and a temperature increase from 5 to 55 °C, the dynamic viscosity increases by at least 95.05% (base fluid) and no more than 95.82% (1.5 vol% nanofluid). Furthermore, by increasing the volume fraction from 0 to 1.5%, the dynamic viscosity increases by a minimum of 14.74% (at 5 °C) and a maximum of 35.94% (at 55 °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, and RSM) were used to develop models for the nanofluid's dynamic viscosity, and their accuracy and complexity were compared. The COMBI algorithm with R² = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

Comprehensive investigations of mixed convection of Fe-ethylene-glycol nanofluid inside an enclosure with different obstacles using lattice Boltzmann method

In the present paper, nanofluid mixed convection is investigated in a square cavity with an adiabatic obstacle by using the Lattice Boltzmann method (LBM). This enclosure contains Fe–ethylene-glycol nanofluid and three constant temperature thermal sources at the left wall and bottom of the enclosure through a lateral wall. The fluid is incompressible, laminar, and Newtonian. The obtained results are presented in the constant Ra = 10⁴ and a Pr = 0.71 for different Ri = 0.1, 1, and 10. The effects of the slope of the enclosure, volume fraction of nanoparticles \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( \varphi \right)$$\end{document}φ, the location of adiabatic obstacles, and nanoparticle diameter in the fluid are investigated on the value of heat transfer. A change in the attack angle of the enclosure leads to changes in the movement distance for fluid between hot and cold sources and passing fluid through case E, which affects the flow pattern strongly. In each attack angle, on colliding with an obstacle, the fluid heat transfers between two sources, which leads to uniform heat transfer in the enclosure. By increasing the velocity of the lid, the Richardson number decreases leading to improvement of the convective heat transfer coefficient and Nusselt number enhancement. The results so obtained reveal that by augmenting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi$$\end{document}φ value the effect of Richardson number reduction can augment Nusselt number and the amount of absorbed heat from the hot surface. Consequently, in each state where a better flow mixture and lower depreciation of fluid velocity components, due to the penetration of lid movement and buoyancy force, occurs higher heat transfer rate is accomplished. Furthermore, it is shown that when Ri = 0.1, the effect of cavity angle is more important but when Ri = 10, the effect of the position of obstacle is more visible.