Reduced Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training

TL;DR

This paper introduces a multi-fidelity reduced order modeling framework for predicting rocket combustor instabilities, utilizing truncated domain simulations to significantly reduce training costs while maintaining accuracy and robustness.

Contribution

The novel framework combines multi-fidelity modeling with truncated domain ROM training, enabling efficient and accurate predictions of combustor instabilities without full geometry simulations.

Findings

ROMs trained on truncated domains are accurate and robust.

The framework reduces training costs compared to traditional methods.

Numerical tests show improved predictive capabilities outside training conditions.

Abstract

A multi-fidelity framework is established and demonstrated for prediction of combustion instabilities in rocket engines. The major idea is to adapt appropriate fidelity modeling approaches for different components in a rocket engine to ensure accurate and efficient predictions. Specifically, the proposed framework integrates projection-based Reduced Order Models (ROMs) that are developed using bases generated on truncated domain simulations. The ROM training is performed on truncated domains, and thus does not require full order model solutions on the full rocket geometry, thus demonstrating the potential to greatly reduce training cost. Geometry-specific training is replaced by the response generated by perturbing the characteristics at the boundary of the truncated domain. This training method is shown to enhance predictive capabilities and robustness of the resulting ROMs, including at conditions outside the training range. Numerical tests are conducted on a quasi-1D model of a single-element rocket combustor and the present framework is compared to traditional ROM development approaches.