Optimized magnetic hysteresis management in numerical electromagnetic field simulations

The treatment of hysteresis in numerical simulations represents major issues as large computational times and significant memory space allocations are required. The memory management of the Jiles-Atherton model is simple, but its integration requires relatively fine temporal discretization to achieve convergence. Oppositely, the Preisach model gives satisfactory results with a coarser temporal grid but requires vast memory space and complex management. The Derivative Static Hysteresis Model (DSHM) is an alternative solution for improved performances. The hysteresis law is considered in a generalized input vector space. An interpolation matrix is constructed with the columns and rows denoting the discrete values of H and
$B$
and whose terms stand for the dB/dH slope at the corresponding point. Up to now, the filling step of the DSHM matrix has always been through experimental first-order reversal curves, but getting such experimental data is always complex. In this study, we propose to fill the DSHM matrix alternatively. We use simulated first-order reversal curves obtained from the Jiles-Atherton or the Preisach model, which have been identified using limited experimental data (the first magnetization curve and the major hysteresis cycle).