Experimental studies and ANN predictions on the thermal properties of TiO2-Ag hybrid nanofluids: Consideration of temperature, particle loading, ultrasonication and storage time

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R²) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young-Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R² were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter.

Hybrid Nanofluid Thermal Conductivity and Optimization: Original Approach and Background

The focus of this paper was to develop a comprehensive nanofluid thermal conductivity model that can be applied to nanofluids with any number of distinct nanoparticles for a given base fluid, concentration, temperature, particle material, and particle diameter. For the first time, this model permits a direct analytical comparison between nanofluids with a different number of distinct nanoparticles. It was observed that the model's average error was ~5.289% when compared with independent experimental data for hybrid nanofluids, which is lower than the average error of the best preexisting hybrid nanofluid model. Additionally, the effects of the operating temperature and nanoparticle concentration on the thermal conductivity and viscosity of nanofluids were investigated theoretically and experimentally. It was found that optimization of the operating conditions and characteristics of nanofluids is crucial to maximize the heat transfer coefficient in nanofluidics and microfluidics. Furthermore, the existing theoretical models to predict nanofluid thermal conductivity were discussed based on the main mechanisms of energy transfer, including Effective Medium Theory, Brownian motion, the nanolayer, aggregation, Molecular Dynamics simulations, and enhancement in hybrid nanofluids. The advantage and disadvantage of each model, as well as the level of accuracy of each model, were examined using independent experimental data.

A Perspective Review on Thermal Conductivity of Hybrid Nanofluids and Their Application in Automobile Radiator Cooling

Hybrid nanofluids developed with the fusion or suspension of two or more different nanoparticles in a mixture as a novel heat transfer fluid are currently of interest to researchers due to their proven better measured thermal conductivities. Several reviewed articles exist on the thermal conductivity of hybrid nanofluids, a vital property for which the heat transfer rate is directly dependent. This review aims to understand the current developments in hybrid nanofluids and their applications. An extensive literature survey was carried out of heuristic-based articles published in the last 15 years. The review reiterates topical research on the preparation methods and ways to improve the stability of readied fluid, thermophysical properties of mixture nanofluids, and some empirical correlations developed for estimating thermal conductivity. Hybrid nanofluid studies on heat transfer performance in automobile radiator cooling systems were also obtained and discussed. The review’s significant findings include the following: (1) hybrid nanofluids produce a noticeable thermal conductivity enhancement and a relatively higher heat transfer coefficient than mono nanofluids and regular liquids. Furthermore, through the uniform dispersion and stable suspension of nanoparticles in the host liquids, the maximum possible thermal augmentation can be obtained at the lowest possible concentrations (by <0.1% by volume). (2) An automobile radiator’s overall heat transfer accomplishment can thus be boosted by using a mixture of nanofluids as conventional coolants. Up-to-date literature results on the thermal conductivity enhancement of mixture fluids are also presented in this study. Nonetheless, some of the barriers and challenges acknowledged in this work must be addressed for its complete deployment in modern applications.

The effects of ultrasonication power and time on the dispersion stability of few-layer graphene nanofluids under the constant ultrasonic energy consumption condition

Few-layer graphene (FLG) nanofluids have received widespread interest in recent years due to their excellent thermal and optical properties. However, the low dispersion stability is one of the main bottlenecks for their commercialization. Ultrasonication is an effective method and almost an essential step to improve the stability of nanofluids. This work aimed to determine the optimal ultrasonication process for preparing stable FLG nanofluids, particularly under the constant ultrasonic energy consumption condition. For this purpose, FLG nanofluids were prepared under various amplitudes (20%-80%) and times (33.75-135 min) and evaluated by both sedimentation and optical spectrum analysis techniques. It was found that ultrasonication treatment at 30% amplitude for 90 min was sufficient for proper dispersion of FLG, and a further increase in the ultrasonication power would not benefit the stability enhancement much. However, for FLG nanofluids prepared at amplitudes higher than 30% under the constant ultrasonic energy consumption condition, their stability deteriorated seriously due to the reduced ultrasonication time, while for FLG nanofluids prepared at 20% amplitude for 135 min, they showed the higher stability, which indicates that the stability of FLG nanofluids is more sensitive to ultrasonication time than power. Therefore, a relatively longer ultrasonication time rather than a higher amplitude is recommended to prepare stable FLG nanofluids for practical applications at given ultrasonic energy consumption.