Improved wall temperature prediction for the LUMEN rocket combustion chamber with neural networks

Please always quote using this URN: urn:nbn:de:bvb:20-opus-319169
  • Accurate calculations of the heat transfer and the resulting maximum wall temperature are essential for the optimal design of reliable and efficient regenerative cooling systems. However, predicting the heat transfer of supercritical methane flowing in cooling channels of a regeneratively cooled rocket combustor presents a significant challenge. High-fidelity CFD calculations provide sufficient accuracy but are computationally too expensive to be used within elaborate design optimization routines. In a previous work it has been shown that aAccurate calculations of the heat transfer and the resulting maximum wall temperature are essential for the optimal design of reliable and efficient regenerative cooling systems. However, predicting the heat transfer of supercritical methane flowing in cooling channels of a regeneratively cooled rocket combustor presents a significant challenge. High-fidelity CFD calculations provide sufficient accuracy but are computationally too expensive to be used within elaborate design optimization routines. In a previous work it has been shown that a surrogate model based on neural networks is able to predict the maximum wall temperature along straight cooling channels with convincing precision when trained with data from CFD simulations for simple cooling channel segments. In this paper, the methodology is extended to cooling channels with curvature. The predictions of the extended model are tested against CFD simulations with different boundary conditions for the representative LUMEN combustor contour with varying geometries and heat flux densities. The high accuracy of the extended model’s predictions, suggests that it will be a valuable tool for designing and analyzing regenerative cooling systems with greater efficiency and effectiveness.show moreshow less

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Metadaten
Author: Kai Dresia, Eldin Kurudzija, Jan Deeken, Günther Waxenegger-Wilfing
URN:urn:nbn:de:bvb:20-opus-319169
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Language:English
Parent Title (English):Aerospace
ISSN:2226-4310
Year of Completion:2023
Volume:10
Issue:5
Article Number:450
Source:Aerospace (2023) 10:5, 450. https://doi.org/10.3390/aerospace10050450
DOI:https://doi.org/10.3390/aerospace10050450
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Tag:LUMEN; heat transfer; machine learning; neural network; regenerative cooling; rocket engine; surrogate model
Release Date:2024/03/27
Date of first Publication:2023/05/12
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International