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Convolutional neural networks for image analysis of high-speed videos from two slab burners

Assenmacher, Oliver and Gelain, Riccardo and Rüttgers, Alexander and Petrarolo, Anna and Hendrick, Patrick (2024) Convolutional neural networks for image analysis of high-speed videos from two slab burners. Acta Astronautica (219), pp. 931-940. Elsevier. doi: 10.1016/j.actaastro.2024.04.005. ISSN 0094-5765.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0094576524002042

Abstract

High-speed video recordings of slab burner experiments were analyzed using a machine learning approach with convolutional neural networks in order to compute the regression rate of hybrid rocket fuels over time. Combustion tests of paraffin-based fuel grains performed in two different hybrid rocket slab burners were recorded with high-speed video cameras and the resulting image data are analyzed in order to determine the height of the fuel in each frame. To this end, a deep neural network with U-net architecture is trained in a supervised fashion to segment the shape of the fuel slab. It is demonstrated that this approach is more capable to segment combustion images in unsteady flow conditions than classical computer vision methods based on thresholding or edge detection. Furthermore, methods in the area of uncertainty quantification of neural networks are applied to estimate the errors in the neural network prediction to new previously unseen data. Finally, the regression rate of the fuel is computed as the rate of change of this height. This method enables automatic analysis of a large amount of video data, taking full advantage of the optical access capabilities of slab burners. Additionally, the method delivers not only the time and space average values of the fuel regression rate, but also quantifies its variation over time and over the length of the slab, providing deeper insights into the combustion mechanics of hybrid rockets.

Item URL in elib:https://elib.dlr.de/203731/
Document Type:Article
Title:Convolutional neural networks for image analysis of high-speed videos from two slab burners
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Assenmacher, OliverUNSPECIFIEDhttps://orcid.org/0000-0003-4614-4715UNSPECIFIED
Gelain, RiccardoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rüttgers, AlexanderUNSPECIFIEDhttps://orcid.org/0000-0001-6347-9272UNSPECIFIED
Petrarolo, AnnaUNSPECIFIEDhttps://orcid.org/0000-0002-2291-2874UNSPECIFIED
Hendrick, PatrickUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:June 2024
Journal or Publication Title:Acta Astronautica
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.actaastro.2024.04.005
Page Range:pp. 931-940
Publisher:Elsevier
ISSN:0094-5765
Status:Published
Keywords:Hybrid rockets, Combustion, Machine learning, Computer vision
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
Institute of Space Propulsion
Deposited By: Assenmacher, Oliver
Deposited On:22 May 2024 09:19
Last Modified:22 May 2024 09:19

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