Advancements in hybrid nanofluids for diesel engine thermal management: a comparative review

Hybrid nanofluids show considerable promise for improving thermal management in diesel engines – outperforming both single-nanoparticle fluids and standard coolants. When it comes to performance, we rigorously evaluated multiple hybrid formulations against four key metrics: thermal conductivity, viscosity, long-term stability, and corrosion resistance. Blending specific nanoparticle types, namely alumina (Al₂O₃), silica (SiO₂), and titania (TiO₂), with carefully chosen surfactant agents. This combination directly boosted how effectively these fluids transfer thermal energy. The research demonstrates that hybrid nanofluids substantially boost thermal conductivity, increasing it by 30% to 50% compared to conventional coolants. In particular, the Al₂O₃-SiO₂-TiO₂ combination showed exceptional effectiveness, surpassing other nanofluid mixtures by roughly 20–30%. Surfactants significantly enhanced the dispersion of nanoparticles, reduced their aggregation, and decreased viscosity by around 10–15%, which subsequently reduced the energy required for pumping. These advancements increased the durability and reliability of hybrid nanofluids, thereby broadening their potential applications. The study emphasized the importance of surfactants in maintaining effective nanoparticle suspension and preventing sedimentation, ensuring sustained stability. Among all the compounds analyzed, the surfactant-modified Al₂O₃-SiO₂-TiO₂ nanocomposite blend showed superior outcomes, striking a balance between enhanced thermal conductivity, stability, and controllable viscosity. Hybrid nanofluids present a promising method for improving diesel engine cooling; however, significant obstacles such as cost, scalability, and durability remain. This study tackles these barriers and contributes valuable perspectives for advancing thermal management technologies. The paper amalgamates experimental and theoretical findings from 82 peer-reviewed studies, providing a comparative analysis without introducing new experimental data.