Title: Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process

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 modelling. 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. Regularisation is introduced to the formulation to avoid over-fitting. The task of determining an optimal regularisation is posed as an optimisation 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 regularisation 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.

Authors:

McQuarrie, Shane A.  ^[1];

• Search DOE PAGES for author "McQuarrie, Shane A."
• Search DOE PAGES for ORCID "0000-0003-0231-5359"

Huang, Cheng  ^[2];

• Search DOE PAGES for author "Huang, Cheng"
• Search DOE PAGES for ORCID "0000-0002-1213-7229"

Willcox, Karen E.  ^[1]

• Search DOE PAGES for author "Willcox, Karen E."
• Search DOE PAGES for ORCID "0000-0003-2156-9338"

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+ Show Author Affiliations

1. Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
2. Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA

Publication Date:

Sun Jan 31 00:00:00 EST 2021

Research Org.:

Univ. of Texas, Austin, TX (United States). Oden Institute for Computational Engineering and Sciences

Sponsoring Org.:

USDOE Office of Science (SC); US Air Force Office of Scientific Research (AFOSR)

OSTI Identifier:

2325428

Alternate Identifier(s):

OSTI ID: 1782859

Grant/Contract Number:  

SC0019303; FA9550-17-1-0195

Resource Type:

Published Article

Journal Name:

Journal of the Royal Society of New Zealand

Additional Journal Information:

Journal Name: Journal of the Royal Society of New Zealand Journal Volume: 51 Journal Issue: 2; Journal ID: ISSN 0303-6758

Publisher:

Informa UK Limited

Country of Publication:

New Zealand

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; model reduction; operator inference; scientific machine learning; combustion; data-driven model