Modeling the heat transfer in magneto-sensitive shape-memory polymer nanocomposites with dynamically changing surface area to volume ratios

Abstract

Magneto-sensitive shape-memory polymer nanocomposites (SMPNCs) enable non-contact actuation of a shape-memory effect (SME) by inductive heating in an alternating magnetic field (AMF). Hereby, the achievable temperature (Tmax) at fixed magnetic field strength (H) and frequency is depending on the amount and type of incorporated magnetic fillers as well as on surface area to volume (S/V) ratio of the test specimen. Here we present a heat transfer model for predicting Tmax of SMPNCs samples with different S/V ratios when exposed to an AMF. The obtained temperature difference between sample and surrounding in an AMF of constant magnetic field strength decreases at uni-axial deformation with the square root of the stretching ratio. The model was validated with magnetically heating experiments of two different SMPNC systems (comprising crystallizable or amorphous switching segments) containing the same magnetic nanoparticles, while H was varied from 7 to 27 kA m−1 at a fixed frequency of 258 kHz. The experimentally achieved temperatures at deformations up to 50% could be predicted with a divergence below 6%. Finally the model was applied in a principle design study of a device consisting of a rolled SMPNC stripe, which was stepwise opened by increasing H. The modeling approach might be helpful to predict the temperature profiles of SMPNCs which were heated by other mechanisms, e.g., radiofrequency or near IR.