LAUSR

Nonlinear thermal simulations of distributed parameter systems with complex geometry can be performed using finite element analysis (FEA). In order to achieve accurate results, fine spatial and time discretization is required, which often leads to large computation times. However, many methods from system theory, such as parameter identification, real-time model-based control, and model-in-the-loop simulation, heavily rely on either multiple iterations or computation time limits. Hence, a direct model deviation from FEA is unfeasible for these approaches. Model order reduction (MOR) techniques have been proposed to improve computational performance. However, most of them are only applicable to linear systems, but linearization of nonlinear boundary conditions over a wide temperature range does not always fulfill accuracy requirements. Therefore, we propose a simplified nonlinear system description by decoupling nonlinear affected states, performing MOR of the remaining linear term and apply calculated projection to the nonlinear affected part. During simulation, the reduced linear system is frequently corrected by the nonlinear term with a specified execution trigger. As a result, computation performance is increased significantly, maintaining sufficient accuracy, which prospectively enables high-performance approximation of nonlinear system behavior.