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Numerically Efficient Fatigue Life Prediction of Rocket Combustion Chambers using Artificial Neural Networks

Dresia, Kai and Waxenegger-Wilfing, Günther and Riccius, Jörg and Deeken, Jan C. and Oschwald, Michael (2019) Numerically Efficient Fatigue Life Prediction of Rocket Combustion Chambers using Artificial Neural Networks. In: Proceedings of the 8th European Conference for Aeronautics and Space Sciences. 8th European Conference for Aeronautics and Space Sciences EUCASS, 1. - 4. Jul. 2019, Madrid, Spain. DOI: 10.13009/EUCASS2019-264

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Abstract

Fatigue life prediction is an essential part of multidisciplinary design studies and optimization loops, but state of the art finite element based methods are numerically inefficient. We overcome this challenge by training an artificial neural network to predict the number of cycles to failure, based on combustion chamber geometry and operational point. To accomplish this, a 2-d finite element analysis generates 250 000 training data samples. The trained network then predicts previously unseen data with a mean absolute percentage error of 6:8 % in less than 0:1 ms per sample compared to up to 5 min with finite element based methods. To the best of our knowledge, this publication is the first to successfully apply machine learning to fatigue life prediction.

Item URL in elib:https://elib.dlr.de/130206/
Document Type:Conference or Workshop Item (Speech)
Title:Numerically Efficient Fatigue Life Prediction of Rocket Combustion Chambers using Artificial Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dresia, KaiKai.Dresia (at) dlr.dehttps://orcid.org/0000-0003-3229-5184
Waxenegger-Wilfing, GüntherGuenther.Waxenegger (at) dlr.dehttps://orcid.org/0000-0001-5381-6431
Riccius, JörgJoerg.Riccius (at) dlr.dehttps://orcid.org/0000-0002-5935-874X
Deeken, Jan C.Jan.Deeken (at) dlr.dehttps://orcid.org/0000-0002-5714-8845
Oschwald, MichaelMichael.Oschwald (at) dlr.dehttps://orcid.org/0000-0002-9579-9825
Date:2019
Journal or Publication Title:Proceedings of the 8th European Conference for Aeronautics and Space Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.13009/EUCASS2019-264
Status:Published
Keywords:machine learning, artificial neural network, liquid rocket engines, fatigue life prediction, surrogate models
Event Title:8th European Conference for Aeronautics and Space Sciences EUCASS
Event Location:Madrid, Spain
Event Type:international Conference
Event Dates:1. - 4. Jul. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transport
DLR - Research area:Raumfahrt
DLR - Program:R RP - Raumtransport
DLR - Research theme (Project):Proj LUMEN (Liquid Upper Stage Demonstrator Engine)
Location: Lampoldshausen
Institutes and Institutions:Institute of Space Propulsion > Rocket Propulsion
Deposited By: Hanke, Michaela
Deposited On:18 Nov 2019 09:14
Last Modified:18 Nov 2019 09:14

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