The solar flat plate collector operating under different convective modes has low efficiency for energy conversion. The energy absorbed by the working fluid in the collector system and its heat transfer characteristics vary with solar insolation and mass flow rate. The performance of the system is improved by reducing the losses from the collector. Various passive methods have been devised to aid energy absorption by the working fluid. Also, working fluids are modified using nanoparticles to improve the thermal properties of the fluid. In the present work, simulation and experimental studies are undertaken for pipe flow at constant heat flux boundary condition in the mixed convection mode. The working fluid at low Reynolds number in the mixed laminar flow range is undertaken with water in thermosyphon mode for different inclination angles of the tube. Local and average coefficients are determined experimentally and compared with theoretical values for water-based Al2O3 nanofluids. The results show an enhancement in heat transfer in the experimental range with Rayleigh number at higher inclinations of the collector tube for water and nanofluids.
Porous Carbon Nanoparticles (PCNs) with well-developed microporosity were obtained from bio-waste oil palm leaves (OPL) using single step pyrolysis in nitrogen atmosphere at 500-600 °C in tube-furnace without any catalysis support. The key approach was using silica (SiO2) bodies of OPL as a template in the synthesis of microporous carbon nanoparticles with very small particle sizes of 35-85 nm and pore sizes between 1.9-2 nm.
The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids. This study conducted experiments in the Reynolds number range of 1300 to 21,000 with particle volume concentrations of up to 1.0%. Nanofluids exhibited superior heat transfer coefficients and friction factor values than the base liquid values. The highest enhancement in heat transfer was 5.4% and 8.3% for glycerol and ethylene glycol -based silicon dioxide Nanofluid with a relative friction factor penalty of ∼30% and 75%, respectively. To model and predict the complicated, nonlinear experimental data, five machine learning approaches were used: linear regression, random forest, extreme gradient boosting, adaptive boosting, and decision tree. Among them, the decision tree-based model performed well with few errors, while the random forest and extreme gradient boosting models were also highly accurate. The findings indicate that these advanced machine learning models can accurately anticipate the thermal performance of nanofluids, providing a dependable tool for improving their use in a variety of thermal systems. This study's findings help to design more effective cooling solutions and improve the sustainability of energy systems.