Electromagnetic induction heating of polymer nanocomposites: a computational study on design parameters

Abstract

Abstract Electromagnetic induction technology enables rapid, noncontact heating of conductive polymer nanocomposites, yet uncontrolled localized heating during this process can induce significant thermomechanical damage. Key influencing factors include nanoparticle dispersion, agglomeration, magnetic field frequency, and coil geometry. This study presents a multiphysics computational model to simulate the induction heating of acrylonitrile butadiene styrene reinforced with iron oxide (Fe3O4) nanoparticles, assessing the impact of these variables on heating efficiency. Numerical predictions were validated against experimental data at four Fe3O4 weight concentrations, demonstrating strong agreement and confirming a positive correlation between nanoparticle content and heating rate. Additionally, higher frequencies substantially enhanced heating, while nanoparticle agglomeration was found to promote localized overheating, posing a risk of material degradation. Although parameters such as particle size, coil design, and polymer positioning influenced heating rates, their effects were comparatively minor. The developed computational framework, experimentally validated, proves reliable and adaptable for modeling induction heating in diverse polymer nanocomposite systems.

Keywords

• Electromagnetic induction heating
• Nanocomposite
• Iron oxide particles
• Computational modeling
• Magnetite