Bilişim Sistemleri Mühendisliği Koleksiyonu

Evaluation of using different metals and working fluids on the thermal performance of nano heat pipes

Thu, 01 Jan 2026 00:00:00 GMT

Evaluation of using different metals and working fluids on the thermal performance of nano heat pipes Huang, He; Dheyaa, J. Jasim; Sawaran Singh, Narinderjit Singh; Ahmad, Nafis; Saydaxmetova, Shaxnoza; Smerat, Aseel; Salahshour, Soheil; Sajadi, S. Mohammad; Emami, N. The precise and effective management of heat produced by micro-scale devices, such as electronic processors, is of utmost importance. Heat pipes (HPs) are among the instruments utilized for this objective. The incorporation of nanofluids can significantly improve the thermal performance of HPs at smaller scales. This study examines the impact of spherical nanoparticles on the working fluid of a micro flat-plate HP. A variety of metals and working fluids were utilized, and molecular dynamics (MD) simulations were performed. The findings indicate that, for any specified nanoparticle volume fraction (φ), the highest and lowest evaporation rates correspond to EtOH and H2O, respectively. Platinum (Pt) and aluminum (Al) exhibit the lowest and highest evaporation rates, respectively. In general, an increase in φ leads to enhancements in both mass transfer and heat flux. The maximum condensation rate (79%) is achieved with Cu-EtOH at φ = 1.05, while the minimum (65%) is observed with Pt-H2O at φ = 0.35. The highest mass transfer rate (40%) is recorded for AlAr at φ = 1.05, whereas the lowest (26%) is noted for Pt-H2O at φ = 0.35. The minimum heat flux (1613 W/cm2) is associated with Pt-EtOH, while the maximum (2092 W/cm2) is linked to Cu-H2O. The body material and the working fluid play a crucial role in determining the heat flux within the HP.

Precise forecasting of shear stress, viscosity, and density for an aqueous CuO/CaCO3/SiO2 ternary hybrid nanofluid utilizing the artificial neural network

Wed, 01 Jan 2025 00:00:00 GMT

Precise forecasting of shear stress, viscosity, and density for an aqueous CuO/CaCO3/SiO2 ternary hybrid nanofluid utilizing the artificial neural network Jin, Yi; Basem, Ali; Jumaan Al-Nussairi, Ahmed Kateb; K Kareem, Muthanna; Hasanabad, Alimohammadi; Li, Zhenghui; Salahshour, Soheil The accurate prediction of thermophysical properties in hybrid nanofluids is crucial for enhancing the efficiency of advanced heat transfer and energy conversion systems. Most published research has largely concentrated on single- or binary-nanoparticle systems, and ternary hybrid systems are still poorly understood in terms of interactions. The present study, however, developed two-layer feedforward artificial neural networks to predict shear stress, viscosity, and density for a water-based nanofluid containing copper oxide, calcium carbonate, and silicon dioxide in volume ratios of 60, 30, and 10%, respectively. Training and validation of the networks were based on experimental data collected at temperatures ranging from 25 to 70 °C and nanoparticle volume fractions ranging from 0.5 to 3%. That model achieved outstanding predictive performance, with average root-mean-square errors (evaluated via K-fold cross-validation) of 0.0008 Pa for shear stress, 0.0097 mPa s for viscosity, and 0.0003 g/cm³ for density. Minimum mean squared errors were 1.63 × 10⁻⁶, 3.11 × 10⁻⁵, and 4.03 × 10⁻⁵, respectively, with correlation coefficients over 0.999 across all data sets. The calculated maximum relative errors were 0.71% for shear stress, 1.34% for viscosity, and 0.06% for density, which endorse the reliability and precision of the produced model. Further sensitivity analysis demonstrated that temperature dominance over shear stress and viscosity, although nanoparticle concentration exerted a significantly stronger impact on density. The proposed framework served as an accurate, data-driven tool for modeling ternary hybrid nanofluids, providing practical insights into their optimized formulations for high-performance thermal management applications.

An examination of the entropy generation of nanofluids and the natural convection dynamics within a circular cavity influenced by a cold fluid jet

An examination of the entropy generation of nanofluids and the natural convection dynamics within a circular cavity influenced by a cold fluid jet Sheikhi, Omid; Basem, Ali; Marzban, Ali; Sawaran Singh, Narinderjit Singh; Akbari, Omid Ali; Ahmed, Saadaldeen Rashid; Ahmadi, Gholamreza; Salahshour, Soheil; Naseri, Hesam Currently, cavities serve as one of the most prevalent methods for managing and dissipating heat produced by centralized indoor sources. The examination of fluid dynamics within cavities influenced by external cold flows has not garnered significant attention from scholars. This study focuses on a cavity with a unique geometry designed to regulate an internal heat source within a spherical buffer via flow injection. The temperature variations within the enclosure affect the flow through the outer shell via the convection heat transfer process. This distinctive heat transfer configuration distinguishes the findings of this research from those of previous studies. The heat transfer and flow dynamics will be simulated for Rayleigh numbers ranging from 100 to 1000. The nanotubes under investigation will have volume fractions between 0 and 0.06 and will exist as a stable solution. The research employs the finite volume method in a two-dimensional steady-state framework. The findings of this study show that the temperature changes between the surface of the circular shell and the hot source will result in natural convection heat transfer. According to the geometrical conditions of the hot circular surface, which side of the cavity experiences stronger fluid movement will result in a better heat transfer distribution. In Reynolds numbers ( Re ) = 300 and 700, strengthening the buoyancy force as an effective factor in improving the Nusselt number ( Nu ) can increase it by more than 1.5 times. At Re = 100, increasing the volume fraction of solid nanoparticles ( φ ) will also increase Nu by 4 %. For Re = 300 and 700, compared to the base fluid, using a nanofluid with φ = 6 % improves Nu by 8 %. With the increase of Re , due to the strengthening of the fluid inertia, the amount of changes in layer velocity will decrease, and finally, the friction factor (C f ) will decrease. In geometries similar to the investigated problem, reducing C f is possible only by the external enhancement of the velocity, such as increasing Re in the injected flow outside the cavity. The presence of solid nanoparticles increases density and viscosity, while also improving temperature distribution. Increasing Ra can strengthen a more uniform velocity distribution with fewer velocity gradients in the cavity, especially in the areas near the wall. The changes of Cf loc depend on velocity gradients near the hot wall. In all the investigated cases, in the places where hot surface experiences flow separation, the lowest Cf loc will be created.

Experimental investigation of rheological behavior of water/graphite nanofluid and presenting a new empirical relation and predicting data using artificial neural network

Wed, 01 Jan 2025 00:00:00 GMT

Experimental investigation of rheological behavior of water/graphite nanofluid and presenting a new empirical relation and predicting data using artificial neural network Nouraei, Ali; Keyvani, Bahram; Aghayari, Reza; Ehsani, Mahtab; Moradbakhsh, Maryam; Toghraie, Davood; Salahshour, Soheil The present study examines the changes in the viscosity of graphite nanoparticles (GNPs) with distinct insights. The temperature range of the nanofluid (NF) from 20 to 50 °C and the volume fraction (VF) of GNP, as compared with the fluid, varying from 0.1 to 0.5%, are the two key variables to achieve optimal NF characteristics. The experimental results confirm that the viscosity of the NF is inversely proportional to the temperature changes, whereas GNP VF follows a direct trend upon changes in viscosity. The intermolecular attraction forces decreased with an increase in temperature, helping to dilute the fluid. At high concentrations, the inhibitory role is lost, and the nanoparticles themselves contribute to the increase in yield stress by coagulation. The optimal condition corresponds to 0.5% VF of GNP at 20 °C. Under such conditions, a 22.6% increase in viscosity was achieved compared to the water-based fluid. Utilizing varying NF temperature and GNP VF (as input), a set of experimental viscosity data (as the target function) was primarily collected. A curve-fitting technique was then employed to develop a theoretical correlation based on the input and target functions. The obtained correlation coefficient (R), i.e., 0.99753, indicates a strong correlation between the experimental data and the proposed relation. In the present study, the perceptron neural network, the Purelin, and the tangent sigmoid functions were used. The algorithm considered was the Levenberg–Marquardt (LM) algorithm, and 32 neurons were used for both optimization and prediction. The root mean squared error (RMSE), mean squared error (MSE), correlation coefficient (R), and mean absolute error (MAE) for the proposed relation, as well as artificial neural network (ANN) data, are shown. These values for the proposed relation data are reported as follows: 0.00782, 0.00000342, 0.997, and 6.25 × 10−7. Accordingly, the ANN is: 8.74 × 10−3), 7.6 × 10−5, 0.998, and 6.939 × 10−18, respectively. The margin of deviation (MOD) was calculated as -2.1831 < MOD < 3.1420.