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Semantic Image Segmentation of Hybrid Rocket Fuel Combustion Data using Convolutional Neural Networks

Assenmacher, Oliver and Rüttgers, Alexander and Petrarolo, Anna and Gelain, Riccardo (2024) Semantic Image Segmentation of Hybrid Rocket Fuel Combustion Data using Convolutional Neural Networks. In: SciTech Forum 2024. SciTech Forum 2024, 08.-12. Jan. 2024, Orlando, USA. doi: 10.2514/6.2024-0799.

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Abstract

Semantic image segmentation using a convolutional neural network was applied to image data of hybrid rocket combustion tests to accurately compute the fuel regression rate over time. Combustion tests with different paraffin-based fuels have been performed at the German Aerospace Center (DLR) and have been captured with a high-speed video camera leading to large image datasets. The main task to allow for the further experimental evaluation with an optical approach is to create binary masks of the solid fuel. For this purpose, a neural network model to segment 120,000 images is presented and is justified by a thorough analysis. This analysis includes the generalization capabilities of the neural network to new image data and an analysis of the model uncertainty. As a result, time-dependent regression rates are computed for the combustion tests over a sequence of different spatial positions. This allows for a detailed time-dependent and spatial comparison of the different experimental configurations and gives valuable insights into phenomena that appear during combustion.

Item URL in elib:https://elib.dlr.de/199279/
Document Type:Conference or Workshop Item (Speech)
Title:Semantic Image Segmentation of Hybrid Rocket Fuel Combustion Data using Convolutional Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Assenmacher, OliverUNSPECIFIEDhttps://orcid.org/0000-0003-4614-4715UNSPECIFIED
Rüttgers, AlexanderUNSPECIFIEDhttps://orcid.org/0000-0001-6347-9272UNSPECIFIED
Petrarolo, AnnaUNSPECIFIEDhttps://orcid.org/0000-0002-2291-2874UNSPECIFIED
Gelain, RiccardoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2024
Journal or Publication Title:SciTech Forum 2024
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI:10.2514/6.2024-0799
Status:Published
Keywords:Image Segmentation, Hybrid Rockets, Machine Learning, Computer Vision
Event Title:SciTech Forum 2024
Event Location:Orlando, USA
Event Type:international Conference
Event Dates:08.-12. Jan. 2024
Organizer:American Institute of Aeronautics and Astronautics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transportation
DLR - Research area:Raumfahrt
DLR - Program:R RP - Space Transportation
DLR - Research theme (Project):R - Project Big Data Platform [RP], R - HPDA basic software
Location: Köln-Porz
Institutes and Institutions:Institute for Software Technology
Institute for Software Technology > High-Performance Computing
Institute of Space Propulsion > Spacecraft and Orbital Propulsion
Deposited By: Assenmacher, Oliver
Deposited On:30 Nov 2023 15:11
Last Modified:05 Feb 2024 08:37

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