Forecasting thermoacoustic instabilities across domains using domain adversarial neural networks

Acta Astronautica, vol. 248, pp. 314–323

Abstract

The occurrence of combustion instabilities poses a high risk to the safe operation of liquid propellant rocket engines. Forecasting such instabilities in real-time is an active field of research, as it would allow the engine operator to initiate emergency procedures during testing and operation before the combustion chamber sustains potentially irreversible damage. In recent years, machine learning has emerged as a promising approach to time-series forecasting. However, like most data-driven methods, they require large amounts of relevant data to be collected beforehand, which is often not available or cost intensive to produce. This study investigates the feasibility of using Domain Adversarial Neural Networks to combine datasets of three combustion chambers, representing a wide range of operating conditions and experimental setups. Using sliding windows of multimodal sensor data, the models forecast the future evolution of pressure fluctuation statistics 200 ms ahead, from which impending instabilities can be inferred. We show that Domain Adversarial Neural Networks are capable of extracting domain-invariant features from the multimodal sensor inputs, leading to improved cross-domain prediction performance, particularly under limited target data availability.

Authors 7

1. David Baldsiefen

   University of Würzburg · Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR), Institute of Space Propulsion, Im Langen Grund, Hardthausen am Kocher, 74239, Germany

   University of Würzburg, Institute of Computer Science, Am Hubland, Würzburg, 97074, Germany

2. Jan Martin

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR), Institute of Space Propulsion, Im Langen Grund, Hardthausen am Kocher, 74239, Germany

3. Michael Börner

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR), Institute of Space Propulsion, Im Langen Grund, Hardthausen am Kocher, 74239, Germany

4. Justin Hardi

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR), Institute of Space Propulsion, Im Langen Grund, Hardthausen am Kocher, 74239, Germany

5. Jan C. Deeken Aachen Institute of Jet Propulsion and Turbomachinery

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) · RWTH Aachen University

   Affiliation as printed

   German Aerospace Center (DLR), Institute of Space Propulsion, Im Langen Grund, Hardthausen am Kocher, 74239, Germany

   RWTH Aachen University, Institute of Jet Propulsion and Turbomachinery, Templergraben 55, Aachen, 52062, Germany

6. Andreas Gernoth

   European Space Agency · European Space Research and Technology Centre

   Affiliation as printed

   European Space Agency (ESA), European Space Research and Technology Centre (ESTEC), Keplerlaan 1, Noordwijk, 2201 AZ, The Netherlands

7. Günther Waxenegger-Wilfing corresponding

   University of Würzburg · Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR), Institute of Space Propulsion, Im Langen Grund, Hardthausen am Kocher, 74239, Germany

   University of Würzburg, Institute of Computer Science, Am Hubland, Würzburg, 97074, Germany