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High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches

Debus, Charlotte und Rüttgers, Alexander und Petrarolo, Anna und Kobald, Mario und Siggel, Martin (2019) High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches. 2020 AIAA SciTech Forum, 2020-01-06 - 2020-01-10, Orlando, FL, USA. doi: 10.2514/6.2020-1161.

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Offizielle URL: https://arc.aiaa.org/doi/10.2514/6.2020-1161

Kurzfassung

Hybrid rocket fuels pose several advantages over conventional solid or liquid bi-propellant solutions in terms of safety, cost and thrust controllability. For paraffin-based fuels, the droplet entrainment process plays a major role in the combustion kinetics. This phenomenon is well reported in the literature, but dedicated quantitative analyses of optical measurements are still lacking. In this study, k-means++ clustering with different numbers of clusters and spectral clustering were employed to high-speed video data of four different combustion experiments with varying fuel and oxidizer configurations. The goal was to identify short-term turbulences and irregularities in the burning kinetics, which could further resolve the process of droplet entrainment. Our results show that k-means++ is able to identify main flow phases, but cannot resolve short-term structures, even with a large number of clusters k = 20. Spectral clustering, which is based on graph theory, requires the computation of an adjacency matrix, which becomes computationally expensive for large data sets. We implemented a highly parallel version of the algorithm, which allowed computation of the pairwise similarities on 30 000 video images in approximately 1 h (150 processes). The similarity matrix gives a qualitative assessment of the combustion kinetics, and several short-term irregularities could be identified. Full spectral clustering of the data yielded quantitative partitioning of the individual frames into long-term main components and short-term fluctuations. Results indicate that a fuel combination of paraffin with the addition of 5% polymer yields the most homogeneous combustion kinetics, and that the oxidizer flow has a substantial influence. The distributed implementation of the algorithm allows future investigations to be conducted on many experiments, giving more insight into the mechanisms of the combustion process.

elib-URL des Eintrags:https://elib.dlr.de/132472/
Dokumentart:Konferenzbeitrag (Vortrag)
Zusätzliche Informationen:2020 AIAA Propellants and Combustion Best Paper Award
Titel:High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Debus, CharlotteCharlotte.Debus (at) dlr.dehttps://orcid.org/0000-0002-7156-2022NICHT 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
Kobald, MarioMario.Kobald (at) dlr.dehttps://orcid.org/0000-0002-1708-3944NICHT SPEZIFIZIERT
Siggel, Martinmartin.siggel (at) dlr.dehttps://orcid.org/0000-0002-3952-4659NICHT SPEZIFIZIERT
Datum:Dezember 2019
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
DOI:10.2514/6.2020-1161
Name der Reihe:AIAA Scitech 2020 Forum
Status:veröffentlicht
Stichwörter:Hybride Raketentreibstoffe, Machinelles Lernen, Clustering
Veranstaltungstitel:2020 AIAA SciTech Forum
Veranstaltungsort:Orlando, FL, USA
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:6 Januar 2020
Veranstaltungsende:10 Januar 2020
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 - Vorhaben SISTEC (alt)
Standort: Köln-Porz
Institute & Einrichtungen:Institut für Simulations- und Softwaretechnik > High Performance Computing
Institut für Raumfahrtantriebe
Hinterlegt von: Rüttgers, Dr. Alexander
Hinterlegt am:13 Dez 2019 12:53
Letzte Änderung:24 Apr 2024 20:35

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