Интенсификация теплопередачи в двухфазных системах с капиллярными насосами

Heat transfer in capillary pumped loops (CPL) is carried out by transferring the mass of the circulating coolant in the form of liquid and vapor. Therefore, the hydrodynamics of the phases in the CPL determines their heat transfer capacity (heat flow or the product of the heat flow by the heat transfer length). The influence of structural, hydraulic and thermo-physical properties of capillary structures used as capillary pumps in two-phase thermal control systems (Loop Heat Pipes - LHP) on their heat transfer capacity has been analyzed. Methods of increasing the heat transfer capacity of LHP, due to the use of anisotropic capillary structures with a decrease in pore sizes in the direction of the vaporization zone, have been determined. The conditions of LHP operability and the method of analytical calculation of the temperature field in anisotropic capillary structures for a model with pseudo-convection have been considered. The calculated and experimental data have been compared.

THERMO PHYSICAL PROPERTIES OF TERNARY MIXTURES OF 1-HEXANOL/1- OCTANOL CONTAINING N-N-DIMETHYLFORMAMIDE AND TOLUENE AT 303.15 K

In the present work, ultrasonic velocity (u), viscosity (η) and density (ρ) of ternary mixtures of 1-hexonal and 1-octanol with dimethylformamide in toluene at 303.15 K have been measured over the entire composition range. From the experimental data, acoustical parameters such as adiabatic compressibility (β), intermolecular free length (L ), free volume (V ), acoustic impedance (Z), excess adiabatic compressibility , excess free length , excess free f f E E (b ) (L )f volume and excess acoustic impedance have been computed. The variation of these properties with composition are E E (V ) (Z ) f discussed in terms of molecular interactions between unlike molecules of the mixtures.

A comprehensive review on the impact of nanofluid in solar photovoltaic/thermal system

Solar energy is a major renewable energy resource used in power production, heating processes, and other applications such as domestic and industrial utilization. It is an abundant form of green energy. Different techniques have been made for energy conversion and one among them is solar photovoltaic/thermal (PV/T) system. Unfortunately, the greatest cause of concern is the rise in temperature of solar PV cells, which will have a negative effect on electrical performance. Thereby, eliminating excess heat on PV cells with heat transfer fluids to lower the temperature of the cells can improve electrical efficiency. A nanofluid is a promising heat transfer fluid to effectively enhance the system efficacy compared with conventional fluids. As the nanoparticle size is very small, the surface area of the nanoparticle is large so it enhances the heat transfer rate. Thereby, recently it has taken on a new dimension for research studies to enhance its thermal behavior for engineering application. This review paper discusses about the importance of nanofluid in solar PV/T system and advantages of employing nanofluid in PV/T system which has high thermo-physical properties. Nanoparticle and nanofluid preparation methods were presented. The thermo-physical properties like thermal conductivity, viscosity, density, and specific heat capacity were also discussed.

Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.