Data-driven reduced-order models via regularized operator inference for a single-injector combustion process

TL;DR

This paper develops a robust, scalable operator inference method with regularization for creating accurate, fast reduced-order models of rocket combustion dynamics, demonstrating significant computational speedups and improved accuracy over existing methods.

Contribution

It advances the formulation and scalability of operator inference with regularization, enabling effective modeling of complex combustion dynamics.

Findings

Achieves near a million times speedup in simulations.

Provides models with accuracy comparable or superior to existing methods.

Demonstrates effectiveness on a complex rocket combustion example.

Abstract

This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends data-driven learning with physics-based modeling. The non-intrusive nature of the approach enables variable transformations that expose system structure. The specific contribution of this paper is to advance the formulation robustness and algorithmic scalability of the Operator Inference approach. Regularization is introduced to the formulation to avoid over-fitting. The task of determining an optimal regularization is posed as an optimization problem that balances training error and stability of long-time integration dynamics. A scalable algorithm and open-source implementation are presented, then demonstrated for a single-injector rocket combustion example. This example exhibits rich dynamics that are difficult to capture with state-of-the-art reduced models. With appropriate regularization and an informed selection of learning variables, the reduced-order models exhibit high accuracy in re-predicting the training regime and acceptable accuracy in predicting future dynamics, while achieving close to a million times speedup in computational cost. When compared to a state-of-the-art model reduction method, the Operator Inference models provide the same or better accuracy at approximately one thousandth of the computational cost.