Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions

Nanofluid, coupled with machine learning, is at the forefront of cutting-edge research in sustainable and renewable energy sector. This review paper examines the latest developments in the intersection of nanofluid and machine learning for heat transfer enhancement. This hybrid nanofluid-machine learning review investigates nanofluid heat transfer enhancement leveraged by machine learning both in porous media as well as heat exchangers. Several studies in porous media nanofluid transport utilize advanced methodologies that integrate machine learning and computational techniques. Machine learning and computational methods are employed to tackle complex thermodynamics, transport processes, and heat transfer challenges in complex multiphysics systems. An interesting hybrid nanofluid-machine learning application involves applying a machine learning method such as Support Vector Machine (SVM) to forecast movement of hybrid nanofluid flows across porous surfaces. Such hybrid nanofluid-machine learning technique involves utilising training data obtained from computational fluid dynamics (CFD) to decrease computational time and expenses. Machine learning offers a more efficient and cost-effective modelling for nanofluid heat transfer enhancement. Techniques such as scanning electron microscopy (SEM) along with X-ray diffraction (XRD) are also often used for assessing the forms as well as nanocomposites configurations in heat exchangers while studying nanofluids. The importance of machine learning models, especially artificial neural networks (ANNs) and genetic algorithms, is evident in their ability to predict and optimize thermal performance of nanofluid application for nanofluid heat transfer enhancement. Furthermore, integrating nanofluids into various heat exchanger designs has demonstrated significant enhancements in efficiency, decreased energy usage, and total cost reduction. These achievements align with the research goal in sustainable and renewable energy, highlighting the critical role of nanofluid-enhanced heat exchange systems in tackling current difficulties related to energy efficiency and sustainability. Overall, combining nanofluids with machine learning shows promising advancements, providing a route toward creating more efficient and eco-friendly heat exchange systems. • Integration of nanofluid technology with machine learning advances thermal engineering research. • Machine learning techniques excel in predicting hybrid nanofluid flows, significantly cutting down computational time and costs. • Imaging techniques like SEM and XRD are employed to analyze the nanocomposite structures within heat exchangers. • Genetic algorithms and artificial neural networks (ANN) are proven to accurately predict and optimize nanofluid-based systems. • Nanofluid-enhanced heat transfer contribute to substantial improvements, aligning with sustainability goals in renewable energy.