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In this paper, a two-stage method, along with an ultrasonic device, a magnetic mixture, and acidity control technique were used to produce ZnO-Ag/water nanofluid and the test results, along with results from data analysis are presented by diagrams and tables. The effect of volume fraction (0.125–2%) and temperature (25–50 °C) on thermal conductivity of nanofluid was examined on thermal conductivity of a hybrid nanofluid, i.e. zinc oxide‑silver ZnO–Ag (50%–50%), is presented in a diagram. At the end, a model was proposed for calculating thermal conductivity of the nanofluid, based on existing data. The results show that since Brownian motion increases with raising temperature, an increase in volume fraction at higher temperatures has a greater role in increasing thermal conductivity. In low volume fractions, temperature has a weaker effect on thermal conductivity. We found that the highest relative thermal conductivity was observed in volume fraction of 2% at 50 °C. Also, according to the obtained results, the margin of deviation is equal to 1.3%. [ABSTRACT FROM AUTHOR]

• Simulation of blood flow is carried out inside the artery. • Different constant heat fluxes are applied on the walls of the artery. • Results are reported in axial and radial directions. • Blood temperature is increased with increasing axial distance. • Maximum temperatures are seen at maximum axial and radial distances from entry. In the present research, simulation of blood flow is carried out inside the artery with different radiuses of 0.002 m, 0.0025 m, 0.003 m, and 0.0035 m. To simulate the blood as non-Newtonian fluid using of Sisko model, different constant heat fluxes are applied on the boundary walls of the artery. Then, the results of velocity, temperature, and Nusselt number are reported versus axial and radial directions. Results show that blood temperature is enhanced with increasing axial distance. Also, maximum temperatures are seen at maximum axial and radial distances from references of entry and central regions of artery. Furthermore, increasing the radius of the artery can increase blood temperature due to a reduction in blood velocity inside the vessel. Consequently, blood particles can spend more time to receive thermal energy, which leads to emerging higher blood temperature. This phenomenon can be important in the oxygenation process inside the human body. It is observed that effect of increasing the radius of the artery can enhance blood temperature as much as 0.001 K. Also, applying constant heat fluxes in order 4 W/m2 to 5 W/m2 and 6 W/m2 on the artery wall brings axial Nusselt values of 0.365–0.4575 and 0.55, respectively. As a result of axial and radial Nusselt numbers, it is reported that because radial Nusslet is unchanged in the central region of the artery, temperature shall be constant in a radius less of 0.0019 m. Therefore, the influences of heat fluxes are ignorable in the central region of the vein. Also, maximum temperatures are reported as much as 310.5 K, 311.1 K, and 311.5 K in order of applying thermal boundary flux of q'' = 400 W/m2, q'' = 800 W/m2 and q'' = 1000 W/m2 respectively. Therefore, applying heat fluxes in the range of investigated can raise the blood temperature as much as 1.5 °C, which is equal to 38.5 °C. Thus, there is no doubt that such a high temperature is dangerous for human health. As conclusion, the results of this research are important hints for medical diagnostics of oxygenation, hematocrit, polycythemia, and blood disorders. [ABSTRACT FROM AUTHOR]

The present paper examines turbulent flow and heat transfer characteristics of a three-dimensional laterally perforated finned heat sink (LPFHS) using response surface methodology (RSM) and computational fluid dynamics (CFD) approaches. The effects of the Reynolds number and perforations geometry, such as porosity (size) and cross-sectional shape (square, circular, hexagonal, and triangular), were investigated on the average friction factor and average Nusselt number. RSM is an applicable approach to improve process conditions by analyzing the impact of different factors and their interdependence on the measured outcomes during a specific technological operation. Three accurate models for the percentage of heat transfer enhancement (PHTE), the percentage of friction factor reduction (PFCR), and the percentage of weight reduction (PWR) as the most important design objectives for heat sinks were proposed using the RSM technique. RSM models served as the foundation for two- and three-objective optimizations. Results demonstrate that fins with square perforations exhibited the highest PHTE value. Additionally, fins with square, circular, and hexagonal perforations experienced increased PFCR with rising porosity at different Reynolds numbers. Fins with square perforations were particularly effective in PWR because the perforations matched well with the lateral surfaces of the fin. The optimal conditions for the LPFHS were achieved using square perforations with Reynolds number and porosity values of 2101 and 0.494, respectively. In the optimal case, PHTE, PFCR, and PWR increased by 7.965%, 24.198%, and 49.367%, respectively, compared to the base case (solid fin). In addition, the optimization strategy implemented in the present study allows weighting of the objectives, which leads to flexible designs. By assigning importance coefficients to each objective, the designer can obtain a set of optimal designs that meet different requirements or trade-offs. [ABSTRACT FROM AUTHOR]

In this study, after generating experimental data points of Zinc Oxide (ZnO)–Silver (Ag) (50%–50%)/Water nanofluid, an algorithm is proposed to calculate the best neuron number in the Artificial Neural Network (ANN), and the performance and correlation coefficient for ANN has been calculated. Then, using the fitting method, a surface is fitted on the experimental data, and the correlation coefficient and performance of this method have been calculated. Finally, the absolute values of errors in both methods have been compared. It can be seen that the best neuron number in the hidden layer is 7 neurons. We concluded that both methods could predict the behavior of nanofluid, but the fitting method had smaller errors. Also, the ANN method had better ability in predicting the thermal conductivity of nanofluid based on the volume fraction of nanoparticles and temperature. Finally, we found that, in ANN, all outputs, the maximum absolute value of error is 0.0095, and the train performance is 1.6684e-05. [ABSTRACT FROM AUTHOR]