Interfacial Thermal Resistance in Magnetocaloric Epoxy‐Bonded La‐Fe‐Co‐Si Composites. Issue 8 (22nd June 2018)

• Record Type: Journal Article
• Authors: Sellschopp, Kai; Weise, Bruno; Krautz, Maria; Cugini, Francesco; Solzi, Massimo; Helmich, Lars; Hütten, Andreas; Waske, Anja
• Is Part Of: Energy technology. Volume 6:Issue 8(2018:Aug.)
• Page Start: 1448
• Page End: 1452
• Publication Date: 2018-06-22
• Subjects: composite materials -- interfacial thermal resistance -- magnetocaloric cooling -- numerical simulations -- X-ray tomography; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.201800111
• Languages: English
• ISSNs: 2194-4288

Abstract

Abstract: Magnetocaloric composites made from La−Fe−Co−Si particles and an epoxy binder matrix exhibit mechanical stability and good magnetocaloric properties, but also a large characteristic time for thermal transport. Here, the origin of this large time constant is examined by comparing two measurement techniques, direct and contactless, to finite‐element simulations based on a tomographic dataset of the sample. The combination of the low thermal conductivity of the epoxy matrix and a thermal resistance at the interface between epoxy and La−Fe−Co−Si is shown to be in good agreement in simulations and experiments. The findings help to disentangle the role of the thermal conductivity and the interfacial thermal resistance for the heat flow in magnetocaloric composites. It is shown that the low thermal conductivity of the epoxy alone cannot explain the large time constant and possibilities for using the interfacial thermal resistance to tailor anisotropic thermal conductivity for directional heat transfer in magnetocaloric composites are presented. Abstract : The role of the interface between particle and matrix for the thermal transport in magnetocaloric composites is discussed. Direct and indirect experimental approaches are used to measure temperature development in these composites and to compare them with numerical simulations based on a real 3D dataset of the measured sample.