Using a hybrid numerical–machine learning framework, the researchers show that controlled aggregation of nanodiamond particles can increase heat transfer efficiency by up to 30%, whereas non-aggregated particles offer smoother flow with lower energy loss.
In recent years, nanofluids—liquids infused with nanoscale particles—have emerged as promising heat transfer media due to their enhanced thermal conductivity compared with conventional fluids. Among them, carbon-based nanomaterials have attracted growing attention because of their exceptional stability and heat conduction. At the same time, surface engineering, such as using wavy or sinusoidal geometries, has been widely explored as a way to intensify heat transfer by disturbing thermal boundary layers and promoting fluid mixing. However, existing studies often examine these factors in isolation. How nanoparticle aggregation, magnetic fields, and surface waviness interact simultaneously remains poorly understood, leaving engineers without clear guidance on how to optimize real-world systems that combine all three effects.
A study (DOI: 10.48130/scm-0025-0013) published in Sustainable Carbon Materialson 29 January 2026 by Caiyan Qin’s team, Harbin Institute of Technology, reports that nanoparticle aggregation, magnetic field strength, and surface geometry jointly determine whether heat transfer gains outweigh the associated hydrodynamic penalties.
The researchers developed a computational framework that couples high-fidelity numerical modeling with artificial neural networks. First, they constructed a physical model describing laminar free convection of diamond–water nanofluids flowing along a vertical nonlinear wavy surface under a transverse magnetic field. The governing equations for momentum and heat transfer were transformed into a dimensionless form and solved using the Keller-box method, a well-established numerical scheme known for its stability and accuracy in nonlinear boundary-layer problems. This approach allowed the team to systematically vary key parameters, including magnetic field strength, surface waviness, and nanoparticle volume fraction, while comparing two distinct configurations: aggregated and non-aggregated nanodiamond particles. The numerical results revealed clear, quantitative trends. Aggregated nanodiamonds form conductive networks within the fluid, significantly enhancing effective thermal conductivity. As a result, the Nusselt number—a measure of heat transfer efficiency—increased by as much as 30% compared with the base fluid. This gain, however, came with a cost: skin friction and viscous dissipation rose by roughly 25%, implying higher pumping power requirements. In contrast, non-aggregated nanoparticles produced smoother velocity profiles and lower drag, delivering more modest heat transfer enhancement of up to 22% but with superior hydrodynamic performance. Surface waviness introduced oscillatory thermal behavior that generally reduced heat transfer by 15–20% due to boundary-layer disruption, yet aggregation helped offset this loss by maintaining continuous thermal pathways. To accelerate analysis and enable rapid prediction, the team trained artificial neural networks on the numerical data. The machine-learning model reproduced the detailed simulation results with extremely high accuracy, achieving mean squared errors on the order of 10⁻⁷, while reducing computational time from hours to seconds.
Overall, the study shows that there is no single “best” nanofluid configuration; instead, optimal design depends on application-specific priorities. Aggregated nanodiamond fluids are well suited for high heat-flux environments where maximum thermal performance is essential and additional pumping power is acceptable, such as power electronics or advanced heat exchangers. Non-aggregated nanofluids, by contrast, are better for flow-sensitive or miniaturized systems where low resistance and energy efficiency are paramount. By integrating physics-based modeling with machine learning, this work delivers actionable design guidelines and a powerful predictive tool for engineering efficient thermal systems in complex geometries.
