This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.

In thermal engineering, predicting nanofluid thermophysical properties is essential for efficient cooling systems and improved heat transfer. Traditional methods often fall short in handling complex datasets. This study leverages machine learning (ML) and metaheuristic algorithms to predict key nanofluid properties, such as specific heat capacity (SHC), thermal conductivity (TC), and viscosity. By utilizing Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gradient Boosting (GB), and Linear Regression (LR), alongside metaheuristic models like Differential Evolution (DE) and Particle Swarm Optimization (PSO), we achieve superior prediction accuracy compared to traditional models. The integration of these computational techniques with empirical data demonstrates their effectiveness in capturing the complex dynamics of thermofluids. Our results validate the precision of ML and metaheuristic models in predicting nanofluid properties and underscore their potential as robust tools for researchers and practitioners in thermal engineering. This work paves the way for future exploration of ML algorithms in thermal management, marking a significant advancement in optimizing nanofluid applications in industry and research.

This study investigates the combined electroosmotic and pressure-driven flow of viscoelastic fluids in a parallel-plate microchannel, emphasizing the interplay of flow and thermal transport under realistic electrokinetic conditions. The simplified Phan–Thien–Tanner model is used to characterize nonlinear rheology, while the electric double-layer potential is evaluated using the full Poisson–Boltzmann formulation, beyond the Debye–Hückel approximation. Closed-form solutions are obtained for the velocity field, flow rate, and stress distribution under hydrodynamically fully developed conditions. The thermally developing regime is analyzed through the energy equation, considering Joule heating and viscous dissipation. To improve predictive accuracy, a physics-informed neural network (PINN) framework is integrated with the governing equations to reconstruct velocity and temperature profiles from sparse data without mesh generation. Multiple PINN architectures were investigated, with skip connections shown to significantly enhance convergence in deeper networks. A hybrid training strategy, combining physics-constrained learning with data-driven fine-tuning, outperforms conventional neural networks. A Hybrid Skip-PINN (five hidden layers) achieves the best performance with mean squared error of 1.3783 × 10−6 and demonstrates robustness against noisy data. Parametric studies highlight the effects of viscoelastic, Brinkman, Peclet, and surface potential parameters. The proposed hybrid framework offers a robust methodology for design and optimization of microfluidic systems.