EVALUATION OF A NOVEL TWO-PHASE CLOSED THERMOSYPHON SYSTEM UTILIZING NiFe2O4/DW, Fe3O4/DW, Fe2O3/DW MAGNETIC NANOFLUIDS

Solar energy is a safe and clean source of energy, available on the Earth throughout the year. A photovoltaic/thermal (PV/T) system is a device designed to take solar energy and convert it into electrical/thermal energy. Photovoltaic/ thermal systems can also be useful to produce hot fluid (usually water) along with the generation of electrical energy. In addition, the electric generating performance of PVs increases with heat discharging ability of thermal system, which also prevents overheating in PV systems. Nanofluid is a new generation heat transfer fluid that delivers higher thermal conductivity and heat transfer rate compared to conventional fluids. The thermal conductivity of the nanofluid depends on the size of the nanoparticles, concentration of the nanofluid, and the method of its preparation. In this study, it is aimed to increase the thermal heat transfer of the PV/T system by using hybrid nanofluids, manufactured by adding 0.5% Fe2O3 and Fe3O4 nanoparticles to the water as a working fluid. By using hybrid nanofluids, increase in bidirectional performance along with enhanced cooling is achieved. In the experimental study, more heat was withdrawn from the heated PV panels by utilizing the high thermal conductivity of the hybrid nanofluid, and the best improvement in total efficiency was obtained as 86% for the hybrid nanofluid. With the use of hybrid nanofluids in the cooling circuit, the electrical and thermal efficiency of the PV panel has reached to overall 81% on average basis.

Improving Thermal and Electricity Generation Performance of Photovoltaic/Thermal (Pv/T) Systems Using Hybrid Nanofluid

Swese,

Sözen,

Rezaeızadeh

et al. 2024

Impact of Filling Ratio and Cellulose Nanofiber Nanofluid on the Total Thermal Resistance and the Startup of a Miniature Thermosyphon

Mekcem,

Berkani,

Bilgili

2024

Thermosyphons are highly effective heat transfer devices used for thermal management in different fields, such as electronic systems, solar collectors and nuclear reactors. The working fluid within the thermosyphon provides the heat transport from the evaporator to the condenser and limits its thermal performance. In this study, the influence of filling ratio (FR) and the eco-friendly cellulose nanofiber (CNF) nanofluid concentration on the total thermal resistance and the startup of a two-phase closed thermosyphon (TPCT) at various heat loads are investigated experimentally. The working fluids are deionized water (DI) and CNF suspensions with 0.5, 1, and 2 vol.% and filling ratios were set to 25, 50, and 75%. Total thermal resistance of the TPCT was obtained using the recorded data of wall temperature distribution at the steady state of each experiment. Addition of CNF with 1 vol.% to DI at filling ratio of 75% reduced the evaporator wall temperature by 40% and 23%, also it reduced the total thermal resistance by 58.78% and 33.65% at 20 W and 80 W, respectively. Moreover, it shortened the startup duration by 33% and reduced its temperature by 42%. This paper contains important findings that prove that CNF enhanced the thermal performance of the TPCT when applying an appropriate concentration and filling ratio.

Machine Learning-Based Predictions of Nanofluid Thermal Properties

Oh,

Guo

2024

In this study, machine learning-based predictions of thermal conductivity, dynamic viscosity, and specific heat of nanofluids are explored. Various types of nanofluids and parametric conditions are considered to broaden and evaluate the effectiveness of popular machine learning models, including multilayer perceptron, random forest, light gradient boosting machine, extreme gradient boosting, and stacking algorithms. The performance of these prediction models is assessed using the mean squared error and the coefficient of determination. The influence of each input variable on model development was examined to identify key features. Information gain is introduced and calculated for determining the importance of parameters in prediction. External validation is performed with an additional unseen dataset to further assess the applicability of the selected models across different experimental data points. It was found that the stacking technique is the most accurate machine learning algorithm among those investigated. The LightGBM is the top choice when considering both computational accuracy and efficiency. The results demonstrate that machine learning methods can provide excellent predictions of the thermophysical properties of complex nanofluids.