Component-Based Reduced Order Modeling of Large-Scale Complex Systems - Huang, Cheng; Duraisamy, Karthik; Merkle, Charles (Frontiers in Physics, 2022)
Large-scale systems occurring in engineering, physics, biological and other fields feature complex, multi-scale interactions between multiple physical phenomena. Characterizing the operation and performance of such systems requires detailed computational models. Even with advances in modern computational capabilities, however, high-fidelity (e.g., Large Eddy) simulations of such a system remain out of reach. In this work, we develop a reduced complexity modeling framework to enable accurate predictions of large-scale systems. We target systems which are difficult to simulate at a high-enough level of fidelity, but are decomposable into different components. These component can be modeled using a combination of strategies, such as reduced-order models (ROM) or reduced-fidelity full-order model (RF-FOM). Component-based training strategies are developed to construct ROMs for each individual component. These ROMs are then integrated to represent the full system. Notably, this approach only requires high-fidelity simulations of a much smaller computational domain. System-level responses are mimicked via external boundary forcing during training. Model reduction is accomplished using model-form preserving least-squares projections with variable transformation (MP-LSVT) HuangMPLSVT2022. Predictive capabilities are greatly enhanced by developing adaptive bases which are locally linear in time. The trained ROMs are then coupled and integrated into the framework to model the full large-scale system. We apply the methodology to extremely complex flow physics involving combustion dynamics. With the use of the adaptive basis, the framework is demonstrated to accurately predict local pressure oscillations, time-averaged and RMS fields of target state variables, even with geometric changes.