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. Nomenclature A = channel area [mm 2 ]b = channel width [mm] d = wall thickness [mm] D h = hydraulic diameter [mm] f = friction factor [−] G = mass flow density [kg s −1 m −2 ] h = channel height [mm] * Research scientist, rocket engine department, Guenther.Waxenegger@dlr.de. † PhD student, rocket engine department, Kai.Dresia@dlr.de. ‡ Group leader, rocket engine department, Jan.Deeken@dlr.de. § Department head, rocket engine department, Michael.Oschwald@dlr.de.

We present a data-driven method for the early detection of thermoacoustic instabilities. Recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data. Features like recurrence rate are used to train support vector machines to detect the onset of instability a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H2 research thrust chamber. In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training. The results are compared with state-of-the-art early warning indicators.

To increase liquid rocket engines (LREs) lifetime capability and allow for reusability applications, the efficient evaluation of the most critical subcomponents' remaining useful life plays a vital role. Regeneratively cooled combustion chamber (CC) wall must withstand extremely high loads emerging from a massive temperature gradient between the hot gas and the low temperature of the coolant. The combined loading and unloading operations, together with high temperature and rate dependent inelastic strain, significantly lessen the combustion chamber inner liner life. Within the presented research, the post-processing model was developed for low cycle fatigue (LCF) evaluation of the reusable LRE's combustion chamber walls. The proposed damage accumulation model is based on the amalgamation of Bonora-Gentile-Pirondi ( 2004) and Dufailly-Lemaitre (1995) methods, and it incorporates ductile and brittle damage components which are embedded in the post-processing method. Moreover, the required numerical calculation time is further decreased on account of the proposed routine which allows for analysis of only two initial numerically acquired FE cycles. The obtained results based on the developed method combined with coupled thermal-structural quasi 2D Finite Element Analysis (FEA) of the nozzle throat cross-section, were confirmed to be in good agreement with the validation data acquired from the M51 thermo-mechanical laboratory site at DLR Lampoldshausen. The proposed model can be successfully applied for a quick evaluation of the remaining useful life of the CC wall for various rocket engine architectures.

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Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. Compared to carefully tuned open-loop sequences and PID controllers, the deep reinforcement learning controller achieves the highest performance. In addition, it requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control.