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

Debus, Charlotte and Rüttgers, Alexander and Petrarolo, Anna and Kobald, Mario and Siggel, Martin (2019) High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches. 2020 AIAA SciTech Forum, 06.-10. Jan 2020, Orlando, FL, USA. (In Press)

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Abstract

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 : = 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.

Item URL in elib:https://elib.dlr.de/132472/
Document Type:Conference or Workshop Item (Speech)
Title:High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Debus, CharlotteCharlotte.Debus (at) dlr.dehttps://orcid.org/0000-0002-7156-2022
Rüttgers, AlexanderAlexander.Ruettgers (at) dlr.dehttps://orcid.org/0000-0001-6347-9272
Petrarolo, AnnaAnna.Petrarolo (at) dlr.dehttps://orcid.org/0000-0002-2291-2874
Kobald, MarioMario.Kobald (at) dlr.dehttps://orcid.org/0000-0002-1708-3944
Siggel, Martinmartin.siggel (at) dlr.dehttps://orcid.org/0000-0002-3952-4659
Date:December 2019
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:In Press
Keywords:Hybride Raketen-treibstoffe, Machinelles Lernen
Event Title:2020 AIAA SciTech Forum
Event Location:Orlando, FL, USA
Event Type:international Conference
Event Dates:06.-10. Jan 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben SISTEC
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology > High Performance Computing
Deposited By: Debus, Charlotte
Deposited On:13 Dec 2019 12:53
Last Modified:13 Dec 2019 12:53

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