The thermal conductivity, viscosity, and cloud points of bentonite nanofluids with n-pentadecane as the base fluid

Experimental Exploration of Hybrid Nanofluids as Energy-Efficient Fluids in Solar and Thermal Energy Storage Applications

In response to the issues of environment, climate, and human health coupled with the growing demand for energy due to increasing population and technological advancement, the concept of sustainable and renewable energy is presently receiving unprecedented attention. To achieve these feats, energy savings and efficiency are crucial in terms of the development of energy-efficient devices and thermal fluids. Limitations associated with the use of conventional thermal fluids led to the discovery of energy-efficient fluids called "nanofluids, which are established to be better than conventional thermal fluids. The current research progress on nanofluids has led to the development of the advanced nanofluids coined "hybrid nanofluids" (HNFs) found to possess superior thermal-optical properties than conventional thermal fluids and nanofluids. This paper experimentally explored the published works on the application of HNFs as thermal transport media in solar energy collectors and thermal energy storage. The performance of hybrid nano-coolants and nano-thermal energy storage materials has been critically reviewed based on the stability, types of hybrid nanoparticles (HNPs) and mixing ratios, types of base fluids, nano-size of HNPs, thermal and optical properties, flow, photothermal property, functionalization of HNPs, magnetic field intensity, and orientation, and φ, subject to solar and thermal energy storage applications. Various HNFs engaged in different applications were observed to save energy and increase efficiency. The HNF-based media performed better than the mono nanofluid counterparts with complementary performance when the mixing ratios were optimized. In line with these applications, further experimental studies coupled with the influence of magnetic and electric fields on their performances were research gaps to be filled in the future. Green HNPs and base fluids are future biomaterials for HNF formulation to provide sustainable, low-cost, and efficient thermal transport and energy storage media.

Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning

Because of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg–Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of ±3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within ±20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.

Do Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC/MEH-PPV/Al Diode

In this paper, we study a new model that represents the symmetric connection between capacitance–voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.

Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

For various obstacles in the path of a flowing liquid stream, an artificial neural networking (ANN) model is constructed to study the hydrodynamic force depending on the object. The multilayer perceptron (MLP), back propagation (BP), and feed-forward (FF) network models were employed to create the ANN model, which has a high prediction accuracy and a strong structure. To be more specific, circular-, octagon-, hexagon-, square-, and triangular-shaped cylinders are installed in a rectangular channel. The fluid is flowing from the left wall of the channel by following two velocity profiles explicitly linear velocity and parabolic velocity. The no-slip condition is maintained on the channel upper and bottom walls. The Neumann condition is applied to the outlet. The entire physical design is mathematically regulated using flow equations. The result is presented using the finite element approach, with the LBB-stable finite element pair and a hybrid meshing scheme. The drag coefficient values are calculated by doing line integration around installed obstructions for both linear and parabolic profiles. The values of the drag coefficient are predicted with high accuracy by developing an ANN model toward various obstacles.

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

The convective heat transfer in non-Newtonian fluid flow in the presence of temperature stratification, heat generation, and heat absorption effects is debated by using artificial neural networking. The heat transfer rate is examined for the four different thermal flow regimes namely (I) thermal flow field towards a flat surface along with thermal radiations, (II) thermal flow field towards a flat surface without thermal radiations, (III) thermal flow field over a cylindrical surface with thermal radiations, and (IV) thermal flow field over a cylindrical surface without thermal radiations. For each regime, a Nusselt number is carried out to construct an artificial neural networking model. The model prediction performance is reported by using varied neuron numbers and input parameters, and the results are assessed. The ANN model is designed by using the Bayesian regularization training procedure, and a high-performing MLP network model is used. The data used in the creation of the MLP network was 80 percent for model training and 20 percent for testing. The graph shows the degree of agreement between the ANN model projected values and the goal values. We discovered that an artificial neural network model can provide high-efficiency forecasts for heat transfer rates having engineering standpoints. For both flat and cylindrical surfaces, the heat transfer normal to the surface reflects inciting nature towards the Prandtl number and heat absorption parameter, while the opposite is the case for the temperature stratification parameter and heat generation parameter. It is important to note that the magnitude of heat transfer is significantly larger for Flow Regime-IV in comparison with Flow Regimes-I, -II, and -III.

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

Numerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method

In the current work, the pressure drop of the refrigerant flow in smooth and micro-fin pipes has been modeled with artificial neural networks as one of the powerful machine learning algorithms. Experimental analyses have been evaluated in two groups for the numerical model such as operation parameters/physical properties and dimensionless numbers used in two-phase flows. Feed forward back propagation multi-layer perceptron networks have been developed evaluating the practically obtained dataset having 673 data points covering the flow of R22, R134a, R410a, R502, R507a, R32 and R125 in four different pipes. The outputs acquired from the artificial neural network have been evaluated with the target ones, and the performance factors have been estimated and the prediction accuracy of the network models has been resourced comprehensively. The results revealed that the neural networks could predict the pressure drop of the refrigerant flow in smooth and micro-fin pipes between 10% deviation bands.

Experimental Study of Thermal Properties and Dynamic Viscosity of Graphene Oxide/Oil Nano-Lubricant

This experimental study was carried out based on the nanotechnology approach to enhance the efficacy of engine oil. Atomic and surface structures of graphene oxide (GO) nanoparticles were investigated by using a field emission scanning electron microscope and X-ray diffraction. The nano lubricant was produced by using a two-step method. The stability of nano lubricant was analyzed through dynamic light scattering. Various properties such as thermal conductivity, dynamic viscosity, flash point, cloud point and freezing point were investigated and the results were compared with the base oil (Oil- SAE-50). The results show that the thermal conductivity of nano lubricant was improved compared to the base fluid. This increase was correlated with progressing temperature. The dynamic viscosity was increased by variations in the volume fraction and reached its highest value of 36% compared to the base oil. The cloud point and freezing point are critical factors for oils, especially in cold seasons, so the efficacy of nano lubricant was improved maximally by 13.3% and 12.9%, respectively, compared to the base oil. The flash point was enhanced by 8%, which remarkably enhances the usability of the oil. It is ultimately assumed that this nano lubricant to be applied as an efficient alternative in industrial systems.