  Numerical simulations are crucial for comprehending how engineering
structures behave under extreme conditions, particularly when dealing with
thermo-mechanically coupled issues compounded by damage-induced material
softening. However, such simulations often entail substantial computational
expenses. To mitigate this, the focus has shifted towards employing model order
reduction (MOR) techniques, which hold promise for accelerating computations.
Yet, applying MOR to highly nonlinear, multi-physical problems influenced by
material softening remains a relatively new area of research, with numerous
unanswered questions. Addressing this gap, this study proposes and investigates
a novel multi-field decomposed MOR technique, rooted in a snapshot-based Proper
Orthogonal Decomposition-Galerkin (POD-G) projection approach. Utilizing a
recently developed thermo-mechanically coupled gradient-extended
damage-plasticity model as a case study, this work demonstrates that splitting
snapshot vectors into distinct physical fields (displacements, damage,
temperature) and projecting them onto separate lower-dimensional subspaces can
yield more precise and stable outcomes compared to conventional methods.
Through a series of numerical benchmark tests, our multi-field decomposed MOR
technique demonstrates its capacity to significantly reduce computational
expenses in simulations involving severe damage, while maintaining a high level
of accuracy.
