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

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...

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Vydáno v:	Journal of the Royal Society of New Zealand Ročník 51; číslo 2; s. 194 - 211
Hlavní autoři:	McQuarrie, Shane A., Huang, Cheng, Willcox, Karen E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Wellington Taylor & Francis 01.06.2021 Taylor & Francis Ltd
Témata:	Accuracy Algorithms Combustion Computational efficiency Computer applications Computing costs data-driven model data-driven reduced model Dynamic stability Dynamics Inference Injectors Learning algorithms Machine learning Mathematical models MATHEMATICS AND COMPUTING Model accuracy Model reduction Operator Inference Optimization Physics Reduced order models Regularization Rocket engines scientific machine learning Size reduction of materials Time integration Training
ISSN:	0303-6758, 1175-8899, 1175-8899
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Popis
Shrnutí:	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.
Bibliografie:	Includes graphs, illustrations, notes, references, table Archived by the National Library of New Zealand ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 SC0019303; FA9550-17-1-0195 USDOE Office of Science (SC) US Air Force Office of Scientific Research (AFOSR)
ISSN:	0303-6758 1175-8899 1175-8899
DOI:	10.1080/03036758.2020.1863237