###### Abstract  
Device optimization plays a paramount role in current research on magnetic refrigeration. Solid state refrigerants have been characterized and numerical simulations assume a critical relevance in the development of magnetocaloric technology to have alternatives to vapour-compression systems whose operating elements have high global warming potential. Experimental studies have shown that the thermal properties of several magnetocaloric materials considerably change around their Curie temperatures (\(T_{C}\)) and that this temperature dependency should not be dismissed. Current numerical research does not fully predict the complete thermal response of such materials, due to inaccuracies from neglecting the impact of combining both thermal conductivity (\(k\)) and specific heat (\(C_{p}\)) dependence on temperature. In this study, a simple unidimensional model includes \(k(T)\) and \(C_{p}(T)\) functions as input parameters, highlighting the relevance of considering temperature dependent thermophysical properties' inputs when simulating the magnetic refrigerant's heat transfer processes. The obtained results evidence that neglecting the temperature dependence of the magnetocaloric material thermophysical properties, namely its thermal conductivity and its specific heat, affects its temperature response, what may strongly affect the results after a succession of (hundreds or thousands) cycles. heat transfer; numerical modelling; thermophysical properties; thermal conductivity; magnetocaloric materials; temperature dependence +
Footnote †: journal: Applied  
Curie temperature (\(T_{C}\)), such that if the MCM is demagnetized adiabatically its temperature decreases [7]. MC devices take advantage of these sharp changes on the refrigerant's thermodynamic properties and exploit them in the most efficient thermodynamic cycle. The MC system usually is made of field generating magnets, a porous regenerator made of a chosen MCM, a fluid pumping system, and heat exchangers (HEX) [8]. Combining the aforementioned MC cycle, along with the Heat Transfer Fluid (HTF) oscillation, a temperature difference is created between the heat exchangers [9]. For example, Gadolinium (Gd), the most common MCM used in MC prototypes (\(T_{C}\) around 294 K), can display a maximum adiabatic temperature span (\(\Delta T_{ad}\)) of approximately 5.5 K, when subject to an applied magnetic field from 0 to 2 T [10]. Active magnetic regenerators (AMR) were developed and adopted, in every type of MC device, to bypass the material's low \(\Delta T_{ad}\), promoting larger heat storage capacities on both opposite heat sinks. As the energy transfer increases due to a larger temperature span, in each magnetic cycle, the HTF transfers larger heat loads between the magnetocaloric material and the heat exchangers. In the AMR cycle, the MCM's heat capacity acts as a dynamic regenerator, absorbing or releasing heat into the HTF, actively creating a heat regenerative ensemble throughout the process [11]. One of the main goals of the regenerator is to extract as much thermal energy as possible from the magnetized active volume while still having excellent heat transfer towards the HTF. To optimize such operation, AMR numerical models are created to help reduce the uncertainty of the next generation prototypes' performance. Some examples can be found in recent years in which numerical models are used to predict and improve the efficiency of MC regenerators when small variations are introduced in one specific aspect of the device [12; 13; 14]. Modelling becomes an essential tool for designing improved thermomagnetic devices, reducing the number of fully developed prototypes needed to reach high performance magnetocaloric systems. In the literature, most numerical models use a 1D approach because of its simplicity and low computational requirements [15]. These types of model are easy to make, modify and adapt, but they are less accurate.