Abstract
A flexible one-dimensional model was created in Python to determine the periodic steady state cooling power of an active magnetocaloric regenerator. Several features of the model provide advantages when performing large parametric studies. Geometry, material properties, and operation parameters are read in as input. Fluid flow and magnetization profiles are parameterized, and thermodynamic consistency is forced in magnetocaloric material properties. A dynamic time step is used, and three associated model constants are set to work over a wide range of input parameter combinations. Derivative gain is adjusted to minimize convergence time resulting in an average convergence time reduction of approximately 50%.
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