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Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks

Waxenegger-Wilfing, Günther and Dresia, Kai and Deeken, Jan C. and Oschwald, Michael (2020) Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks. Journal of Thermophysics and Heat Transfer. American Institute of Aeronautics and Astronautics (AIAA). doi: 10.2514/1.T5865. ISSN 0887-8722.

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Official URL: https://arc.aiaa.org/doi/10.2514/1.T5865


Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design reliable and efficient cooling systems, heat transfer modeling is a fundamental issue to address. Advanced computational fluid dynamics (CFD) calculations achieve sufficient accuracy, but the associated computational cost prevents an efficient integration in optimization loops. Surrogate models based on artificial neural networks (ANNs) offer a great speed advantage. It is shown that an ANN, trained on data extracted from samples of CFD simulations, is able to predict the maximum wall temperature along straight rocket engine cooling channels using methane with convincing precision. The combination of the ANN model with simple relations for pressure drop and enthalpy rise results in a complete reduced order model, which can be used for numerically efficient design space exploration and optimization.

Item URL in elib:https://elib.dlr.de/128908/
Document Type:Article
Title:Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Waxenegger-Wilfing, GüntherUNSPECIFIEDhttps://orcid.org/0000-0001-5381-6431UNSPECIFIED
Dresia, KaiUNSPECIFIEDhttps://orcid.org/0000-0003-3229-5184UNSPECIFIED
Deeken, Jan C.UNSPECIFIEDhttps://orcid.org/0000-0002-5714-8845UNSPECIFIED
Oschwald, MichaelUNSPECIFIEDhttps://orcid.org/0000-0002-9579-9825UNSPECIFIED
Date:10 January 2020
Journal or Publication Title:Journal of Thermophysics and Heat Transfer
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:American Institute of Aeronautics and Astronautics (AIAA)
Keywords:Heat Transfer Prediction, Neural Networks, Machine Learning, Supercritical Fluids, Rocket Engine Cooling Channels
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 LUMEN (Liquid Upper Stage Demonstrator Engine)
Location: Lampoldshausen
Institutes and Institutions:Institute of Space Propulsion > Rocket Propulsion
Deposited By: Waxenegger-Wilfing, Günther
Deposited On:27 Aug 2019 08:03
Last Modified:24 Oct 2023 15:02

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