No abstract

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 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.

The German Aerospace Center (DLR) Institute of Space Propulsion has unique expertise in operating test facilities for rocket engine testing and development in Europe since 1959. However, essential elements of the test site were designed up to half a century ago. In order to ensure a futureproof and intelligent digital test infrastructure, the potential of test automation, advanced control, and monitoring systems is investigated based on machine learning. Such intelligent control systems are expected to reduce engine development and test preparation times, thereby lowering the associated costs. Additionally, advanced monitoring systems are anticipated to increase the safety and reliability of the test infrastructure. This paper presents the results of two pilot projects: the first project uses reinforcement learning to automatically generate test sequences based on test requirements, while the second project develops a feed-forward forecasting model to predict deviations from expected behavior in the feed-line of a rocket engine test facility.