Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids.

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.

Biosynthesis of Iron Oxide Nanoparticles Using Leaf Extract of Ruellia tuberosa: Mechanical and Dynamic Mechanical Behaviour Kevlar-Based Hybrid Epoxy Composites.

One of the more enticing, ecologically responsible, as well as safe and sustainable methodologies is eco-friendly nanomaterial synthesis. Vegetation materials will be used as reductants instead of toxic substances for synthesising nanoparticles. The current study used Ruellia tuberosa (RT) leaf extract digest to synthesise FeO nanomaterials, which were then characterised using XRD. Following that, microbially produced FeO molecules were mixed with a Kevlar-based polymeric matrix to study the blended consequences. To examine the interbreeding, the current experimental analyses were performed, including both static and dynamic mechanical characteristics. The addition of FeO nanofillers improved the elastic modulus, tensile strength, and storage modulus of the nanocomposite. Impact force uptake has been raised to a certain extent by the addition of nanoparticles. The findings of this research show that incorporating FeO nanofillers into Kevlar fabrics is a promising technique for increasing the mechanical characteristics of hybrid laminated composites. As per DMA evaluation, the sample without nanomaterials had a more volcanic lava response, which is a useful thing for body systems for missile use. Another critical aspect of a nanoparticles-filled nanocomposite that must be addressed is the relatively uniform scattering of padding as well as the development of interfacial adhesion in such a combination. The presence of FeO fillers in polymeric composites is confirmed by XRD analysis.