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Ai-based evaluation of hybrid rocket combustion tests

Assenmacher, Oliver und Rüttgers, Alexander und Petrarolo, Anna (2023) Ai-based evaluation of hybrid rocket combustion tests. Helmholtz AI Conference 2023, 2023-06-12 - 2023-06-14, Hamburg, Deutschland.

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Kurzfassung

High-speed camera recordings of rocket combustion tests were analyzed using deep learning. In hybrid rocket engines thrust is generated via the chemical reaction of a solid fuel and a fluid oxidizer, as such they can combine some of the advantages of solid and liquid rockets. One important metric in the design of hybrid rocket engines is the regression rate, which is the rate with which the surface of the solid fuel recedes during combustion. The regression rate depends on many factors, such as the makeup of the fuel, the flowrate of the oxidizer and the pressure in the combustion chamber. In order to investigate these dependencies, several combustion tests were performed at the German Aerospace Center and recorded with a high-speed camera. The shape of the remaining fuel is detected in each frame and the regression rate is computed as the rate of change of the height of the fuel. Detecting the shape of the fuel using classical computer vision algorithms is impractical because the optical access to the combustion chamber may be partially obstructed by soot, reflections or flames forming at the side of the fuel. Therefore, a neural network with U-net architecture is trained to detect the shape of the fuel. This training requires manual labelling, which can only be done for a small fraction of the frames of a given high-speed recording. Hence, several forms of data augmentation are employed to avoid overfitting and improve robustness with respect to the prominent sources of noise in the data.

elib-URL des Eintrags:https://elib.dlr.de/195571/
Dokumentart:Konferenzbeitrag (Poster)
Titel:Ai-based evaluation of hybrid rocket combustion tests
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Assenmacher, Oliveroliver.assenmacher (at) dlr.dehttps://orcid.org/0000-0003-4614-4715NICHT SPEZIFIZIERT
Rüttgers, AlexanderAlexander.Ruettgers (at) dlr.dehttps://orcid.org/0000-0001-6347-9272NICHT SPEZIFIZIERT
Petrarolo, AnnaAnna.Petrarolo (at) dlr.dehttps://orcid.org/0000-0002-2291-2874NICHT SPEZIFIZIERT
Datum:Juni 2023
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:machine learning, hybrid rocket fuels, convolutional neural networks, data augmentation, high-performance computing, semantic segmentation
Veranstaltungstitel:Helmholtz AI Conference 2023
Veranstaltungsort:Hamburg, Deutschland
Veranstaltungsart:nationale Konferenz
Veranstaltungsbeginn:12 Juni 2023
Veranstaltungsende:14 Juni 2023
Veranstalter :Helmholtz AI
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrtsysteme
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - Aufgaben SISTEC
Standort: Köln-Porz
Institute & Einrichtungen:Institut für Softwaretechnologie > High-Performance Computing
Institut für Raumfahrtantriebe > Satelliten- und Orbitalantriebe
Institut für Softwaretechnologie
Hinterlegt von: Assenmacher, Oliver
Hinterlegt am:27 Jun 2023 10:03
Letzte Änderung:01 Jul 2024 03:00

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