Parametric Model Order Reduction for Localized Nonlinear Feature Inclusion

Abstract

Injecting parametric dependency and treating nonlinear phenomena pose the main challenges when seeking to construct an accurate Reduced Order Model (ROM) of an actual complex system. Also, real-life structures may often comprise multiple components demanding separate treatment. This paper derives a physics-based ROM, reflecting dependencies on system properties and characteristics of the induced excitation. This is achieved utilizing a projection strategy relying on Proper Orthogonal Decomposition. The framework is then coupled with the substructuring approach in [13]. This representation allows integrating localized domains experiencing nonlinearity or damage on the ROM and enables response learning both at a global and a local level. The pROM derived through this fusion can address each component's dynamics separately and apply individual reduction, leading to a modular formulation.