Component-Based Reduced-Order Modeling Framework for Rocket Combustion Dynamics in Multi-Injector Configurations

TL;DR

This paper introduces a component-based reduced-order modeling framework for large-scale rocket engines, enabling efficient and accurate simulations by decomposing the system into components and coupling their reduced models.

Contribution

The work develops a novel CBROM framework using advanced adaptive ROM techniques, significantly reducing computational costs for simulating complex rocket combustion dynamics.

Findings

Accurately predicts dynamic behavior changes with flow and geometric variations.

Demonstrates effective coupling of component ROMs for full-system simulation.

Provides spectral and field analysis consistent with high-fidelity results.

Abstract

Even with the most advanced computational capabilities, high-fidelity (e.g., large-eddy) simulations of large-scale rocket engines remain far out of reach. In the current work, we develop and establish a component-based reduced-order modeling (CBROM) framework to enable accurate and efficient parametric modeling of large-scale rocket engines by geometrically decomposing a single domain into a combination of several representative components, including injectors, combustor and nozzle. Individual component-based reduced-order models (ROMs) are trained for each component with fabricated system-level responses enforced through carefully formulated boundary conditions during the training, which only require high-fidelity simulations of a much smaller computational domain, thereby significantly reducing the costs of ROM training. The trained component-based ROMs are then coupled together to enable full-system simulations. Specifically, we pursue an advanced adaptive ROM formulation leveraging a model-form preserving least-squares with variable transformation (MP-LSVT) projection to construct the component-based ROMs. The CBROM framework is evaluated using a seven-injector model rocket combustor configuration that exhibits self-excited combustion dynamics with distinct characteristics that vary with flow condition and geometric variations. The framework is demonstrated to provide accurate parametric predictions of the changes in dynamic behaviors, expressed in the spectra from dynamic mode decomposition (DMD) analysis and features in the time-averaged and RMS fields of target state variables.