Nanofluid Cooling Performance in Automotive Systems
Summary
Efficient thermal management is essential to maintain engine performance, fuel economy and longevity in modern vehicles. Nanofluids—suspensions of nanoparticles in conventional coolants—offer enhanced thermal conductivity, improved convective heat transfer and reduced component size. In automotive radiators and heat exchangers, optimising nanoparticle type, concentration and flow conditions can yield significant gains in heat dissipation. Advances in nanoparticle dispersion stability, hybrid formulations and surface treatments have mitigated agglomeration and fouling, leading to more reliable performance over prolonged operation. Numerical and experimental studies demonstrate that modest particle loadings (typically below 2 vol%) strike a balance between heat transfer enhancement and pumping power penalty. The integration of statistical methods and machine learning has enabled multi‐parameter optimisation, while emerging hybrid nanofluids combine complementary materials to push convective heat transfer improvements beyond 50 percent. These developments underscore the global potential of nanofluid technology to downsize cooling systems, reduce vehicle mass and lower emissions.
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Nanofluid Cooling Performance in Automotive Systems publication trend
The graph below shows the total number of articles in nanofluid cooling performance in automotive systems across all publications each year (not limited to Nature Index journals).
Technical terms
Nanofluid: Suspension of nanoparticles within a base fluid to enhance thermal properties.
Nusselt number (Nu): Dimensionless parameter expressing convective heat transfer relative to conduction.
Reynolds number (Re): Dimensionless ratio of inertial to viscous forces indicating flow regime.
Hybrid nanofluid: Nanofluid containing more than one type of nanoparticle to optimise heat transfer.
Grey relational analysis (GRA): Statistical method converting multiple performance measures into a single optimisation grade.
Response surface methodology (RSM): Mathematical and statistical technique for modelling and analysis of problems influenced by several variables.
Artificial neural network (ANN) modelling: Computational framework inspired by neural structures for predicting complex relationships among variables.
References
- The Effect of Nanofluid Concentration on the Cooling System of Vehicles Radiator. Advances in Mechanical Engineering (2014).
- Integrated Taguchi-GRA-RSM optimization and ANN modelling of thermal performance of zinc oxide nanofluids in an automobile radiator. Case Studies in Thermal Engineering (2021).
- Heat Transfer Enhancement by Hybrid Nano Additives—Graphene Nanoplatelets/Cellulose Nanocrystal for the Automobile Cooling System (Radiator). Nanomaterials (2023).
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