Component-based Reduced Order Modeling for Simulations of Large-scale Rocket Engines

Large-scale engineering systems, such as rocket engines, feature complex, multi-scale interactions between multiple physical phenomena, the characterization of which requires detailed computational models. Unfortunately, even with advances in modern computational capabilities, high-fidelity (e.g., large-eddy) simulations of such a system remain out of reach. In this work, a component-based reduced-order modeling framework is established to enable accurate predictions of large-scale rocket engines, which are difficult to simulate at a high-enough level of fidelity but are decomposable into different components. These components can be modeled using a combination of strategies, such as reduced-order models (ROM) or reduced-fidelity full-order models (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) with enhancement through adaptation, which updates the low-dimensional subspaces based on the evaluated dynamics during online calculations to greatly enhance predictive capabilities. The trained ROMs are then coupled and integrated into the framework to model the full large-scale system. The framework is demonstrated on a multi-element rocket combustor configuration and is shown to accurately predict local pressure oscillations, time-averaged, and RMS fields of target state variables, even with geometric changes.