Nanofluids are stable suspensions of nanoparticles in a conventional fluid. They have shown superior potential in heat transfer enhancement. In this research, ZnO/water nanofluids were prepared at various concentrations from 0. 2 to 1. 5vol%, and their thermal conductivity was measured. The results showed that the thermal conductivity of ZnO/water nanofluids depends on particle concentration and increases non-linearly with the volume fraction of nanoparticles. The effects of particle size and temperature on the thermal conductivity were also investigated at 1. 5vol%. The results indicated that thermal conductivity enhanced with decreasing particle size and increasing with temperature. For nanofluids containing 10-15 nm and 45-50 nm particle sizes, the enhancements were 26. 3 and 22. 8% at 40oC, respectively. In this research, the convective heat transfer coefficient of ZnO/water nanofluids with the above particle sizes was also measured under laminar flow in a horizontal tube heat exchanger. It was observed that both nanofluids showed higher heat transfer coefficients compared to the base fluid at a constant concentration (1. 5 vol%). For nanofluids with 10-15 nm and 45-50 nm particle sizes, the average heat transfer coefficient enhancement was 18. 1 and 14. 9% at Re=1115, respectively.

Thermal conductivity is defined as the ability of a material to heat transfer. In other words, thermal conductivity is the natural tendency of material to energy dispersion when temperature equilibrium disturbed by the imposition of a temperature gradient. Therefore, it plays a significant role in the issues of heat transfer. Due to the various applications of nanoscale materials in heat transfer and the importance of determining the thermal conductivity of nanofluids, this study, investigates eleven models for predicting the thermal conductivity of nanofluids (includes water and TiO2 nanoparticles) and comparing the results of calculations with the experimental results in the articles. Based on this study, it was found that the effective thermal conductivity ratio (thermal conductivity of the mixture of basic fluid and distributed nanoparticle) to the basic fluid conductivity (keff/kf) for variable Volumetric percentages ranging from 0. 01 to 0. 03 varies between 1. 01 and 1. 1. In the other words, addition of nanoparticles in the range of 1 to 3 Volume percentage is able to promote the keff/kf to 1. 1 (10%). Therefore, thermal conductivity of the mixture of basic fluid and distributed nanoparticle increases up to 10% compared with basic fluid. Also, increasing the diameter of the nanoparticles reduces the thermal conductivity improvement.

Thermal conductivity of MWCNT(40%)-CuO(30%)-TiO2(30%) /Water nanofluid in different volume fractions and temperatures is modeled and analyzed by artificial neural network method. The type of artificial neural network is MLP. 48 experimental data series are used, 70%, 15%, and 15% are used for training, validation, and testing, respectively. The optimal neural structure has two hidden layers, in which there are 4 neurons in the first layer and 5 neurons in the second layer, with the transfer functions of logsig and tansig, respectively. Neural network training is done with Levenberg-Marquardt (ML) algorithm. The values of regression coefficient, R, and mean squared error, MSE, for the optimal structure are obtained as 0.9995753 and 2.8734E-06, respectively. The correlation equation is also presented to predict the thermal conductivity of nanofluid. The comparison between the correlation equation and the artificial neural network shows the superiority of the artificial neural network. MOD values for artificial neural network are also in the range of -3% to +7%.

Application of feed pellets in animal and aquatic farming industries has grown because of both the physical and the nutritional benefits it provides. Development of feed pellets manufacturing industry is also considerable. Steam conditioning process, which plays an important role in pelleting production, includes heating feed particles, adding moisture, and mixing the mash. Pellets cooling and drying processes are also involved in heat transfer phenomena. In this study, thermal conductivity of feed pellets was determined at different temperatures ranging from 25 to 85oC and moisture contents of 11.8 to 18.2% wb. It was measured by the transient technique using the line heat source method assembled in a thermal conductivity probe. It turned out that decreasing moisture contents from 18.2 to 11.8% (wb) produced non-linear reduction in thermal conductivity.The average values of thermal conductivity changed from 0.1509 to 0.2143 W m-1 oC-1 at different moisture contents. Tests conducted on two pellet size categories (based on nominal diameter) revealed a significant difference in thermal conductivity between these categories. The thermal conductivities of the first category (minor than nominal dia.) appeared to be 8.5% higher than those of the second category (superior to nominal dia.).Average values of thermal conductivity changed from 0.1538 to 0.2333 W m-1 oC-1 for the first category and from 0.1235 to 0.2456 W m-1 oC-1 for the second category (in 25oC). In addition, some empirical models were developed to express thermal properties as a function of moisture content and temperature.

Liquid paraffin as a coolant fluid can be applied in electronic devices as a result to its suitable capabilities such as electrical insulating, high heat capacity, chemical and thermal stability, and high boiling point. However, the poor thermal conductivity of paraffin has been confined its thermal cooling application. Addition of high conductor nanoparticles to paraffin can fix this drawback properly. In this article, the influence of the nanoparticles on the thermal conductivity of base material was assessed. Temperature (20-50° C) and volume fractions (0-3%) effect on the thermal conductivity of paraffin/alumina nanofluids have been considered. Nanofluid samples were prepared applying the two-step method. The thermal conductivity was measured by a KD2 pro instrument. The results indicated the thermal conductivity augments smoothly with an increase in volume fraction of nanoparticles as well as temperature. Moreover, it observed that for nanofluids with more volume-fraction the temperature affection is more remarkable. Thermal conductivity enhancement (TCE) and effective thermal conductivity (ETC) of the nanofluid was calculated and new correlations were reported to predict the values of them based on the volume fraction of nanoparticles and temperature of nanofluid accurately.

Blended films of polyvinyl alcohol and polyethylene oxide reinforced with CuCl2.2H2O (10–50 wt%) were prepared using the solution casting technique to investigate the impact of salt concentration on structural, thermal, mechanical, and physical properties. Experimental results revealed that the thermal conductivity coefficient initially increased with rising salt content, then decreased at intermediate concentrations, and subsequently increased again at higher salt ratios. This non-linear trend suggests structural modifications due to interactions between PVA, PEO, and CuCl2.2H2O, which altered the films’ internal morphology. For mechanical properties, irregular behavior was observed in hardness, impact resistance, and tensile strength as the salt ratio increased. Fracture energy and impact toughness exhibited inconsistent trends, while elongation at break fluctuated unpredictably, reflecting changes in the films’ flexibility and structural cohesion. Physically, the true density of the films initially decreased and then increased with higher salt content. Apparent porosity first declined but later rose irregularly, whereas water absorption decreased initially before increasing steadily with salt addition. These trends indicate that CuCl2.2H2O influences polymer network formation, potentially enhancing or disrupting intermolecular bonds depending on its concentration. The study confirmed chemical interactions between PVA, PEO, and CuCl2.2H2O, which directly affected the films’ properties. The non-linear relationship between property and salt concentration highlights the need for further optimization studies to determine ideal ratios for specific practical applications.