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Item type:Publication, Access status: without files , Engine cycle adaptation method for aircraft on-board systems evaluation considering system-engine interaction(American Institute of Aeronautics and Astronautics (AIAA), 2024-01); ; In the assessment of different aircraft on-board systems (OBS) using mission-level metrics like fuel burn, typically the same engine performance model is employed. One example is the comparison of conventional versus bleedless architectures with respect to their impact on mission block fuel. However, especially with the application of ultra-high bypass ratio (UHBR) turbofans, the cycle performance of such engines increasingly varies depending on the power offtakes (POT) requirements of the considered OBS architecture. This paper proposes a method for adapting the thermodynamic engine cycle to accommodate OBS POT requirements based on a given reference cycle. The method iteratively adjusts relevant engine design parameters by employing physical and semi-empirical correlations. The gas turbine simulation software NPSS is embedded into this process, ultimately posing the adapted cycle design point based on the input design parameters. The presented method is applied in the scope of an OBS architecture trade study for a modern mid-range concept aircraft. The results show a -0.7% reduction in block fuel mass for a bleedless OBS architecture with an adapted bleedless engine compared to the same bleedless architecture in combination with a conventional reference engine designed for bleed offtakes. This highlights the importance of integrated OBS architecture studies, for which the engine’s cycle design point and the employed engine performance model is adapted to the POT requirements for more accurate evaluation results.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Evaluation metrics for systems architecting demonstrated on cooling system of hydrogen-powered concept aircraft(American Institute of Aeronautics and Astronautics (AIAA), 2024-01); ; ; Climate change presents a global challenge, with aviation significantly contributing to greenhouse gas emissions. To reduce the environmental impact of aviation, novel aircraft concepts incorporating renewable energy sources, such as green hydrogen, are being explored during the aircraft conceptual design phase. Low temperature proton exchange membrane fuel cells are currently being investigated as one possible technology for supplying power to the aircraft power train and on-board systems. However, the design of a cooling system for these fuel cells is challenging due to the small available temperature difference between fuel cell operation and ambient temperatures in hot day on-ground scenarios. Therefore, the development of novel cooling system architecture concepts is necessary. For this design problem, many conceivable architecture variants need to be investigated, resulting in a complex and large design space during systems architecting. To assist the engineer during systems architecting and to reduce the design space to promising architecture variants, the Systems Architecting Assistant (SArA) methodology is used. In this work, SArA is extended by a method for evaluating architecture performance based on the metrics complexity and risk, including a knowledge-based and a model-based approach. This helps engineers quantitatively compare system architecture alternatives, thereby simplifying the down-selection process. The developed metrics are applied to eight different cooling system architecture variants of a hydrogen-powered concept aircraft, enabling a rapid and partially automated approach for architecture evaluation. While the use of a combined knowledge-based and model-based approach proved beneficial, it became apparent that complexity and risk as high-level metrics provide limited insights and value in the decision-making process. To enhance the value of the evaluation method and to gain a deeper understanding of the architectures, a comprehensive investigation of the selected technologies becomes imperative.Publicationtype: Conference Paper44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Agent-based discovery of unexpected falsification scenarios in aircraft systems virtual testing(2024-01); Innovative concepts for aircraft systems with the aim to satisfy current aviation emission goals include novel technologies, such as fuel cell systems. However, due to the lack of experience for such technologies in aviation the implicated requirements for the system design may not be fully understood or unknown. This creates a risk for unexpected “blind spots” in the system design that will at best be found in late testing stages. Virtual testing based on digital simulation models is one possible approach to conduct verification and validation much earlier during design phases, but additional methods are needed to find the unexpected scenarios. This work introduces a methodical framework that aims to discover such unexpected test scenarios by means of heterogeneous machine learning test agents. The agents are executed in parallel and exchange falsification scenario information to improve the search for unexpected scenarios that falsify a given system property. The concept is applied and evaluated on a virtual system example for a fuel-cell-driven electric propulsion engine.Publicationtype: Conference Paper32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Linear Parameter-Varying (LPV) system based hybrid loads observer using an uncertain aircraft modelThe hybrid loads observer has consistently demonstrated its effectiveness in precisely estimating structural loads and wind disturbances in prior applications. This level of precision is attained through the combination of a high-fidelity physical, nonlinear flight dynamics model with a data-driven correction model. To mitigate the typically high computational effort, the nonlinear model of the loads observer is substituted in this work with a linear parameter-varying (LPV) system while preserving a comparable level of accuracy of the hybrid observer. For this purpose, the model is approximated by scheduled Linear Time-Invariant (LTI) models derived from Jacobian linearization of the nonlinear model. The robustness of this linear parameter-varying hybrid approach against parameter uncertainties is evaluated through virtual flight test studies using the subscale test aircraft Wingfinity-BL as an application example. It is demonstrated that increased model uncertainties lead to a reduction in wind estimation accuracy of the hybrid loads observer. Additionally, increased parameter uncertainties adversely affect the quality of structural loads estimation within the model-based (physical) approach of the observer. Nonetheless, this loss of accuracy can be effectively compensated by the data-driven correction model, leading to a high degree of loads estimation accuracy. Finally, experimental data from a wind tunnel test campaign, utilizing a 1-DOF representative test wing of the aircraft, confirms the high estimation accuracy of the LPV-based hybrid loads observer. Despite employing low-fidelity models, achieving high accuracy is feasible while maintaining the characteristic low complexity of the correction model.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimization of large-scale aeroengine parts produced by additive manufacturing(American Institute of Aeronautics and Astronautics (AIAA), 2024-01); ; ; ; Additive Manufacturing (AM) presents a ground-breaking opportunity to produce lightweight parts with enhanced functionality and design flexibility. It revolutionizes assembly by enabling integrated designs that significantly reduce component counts, minimizing assembly efforts and potential faults. Consequently, AM stands out as an appealing choice for manufacturing aerospace engine parts. Among AM techniques for metal parts, Laser Powder Bed Fusion (LPBF), also known as Direct Metal Laser Melting (DMLM), currently dominates the industry due to its capability to achieve high part quality and density using advanced machinery. However, limitations in part size stem from the build envelopes of these machines. Hence, this study explores the feasibility of printing large-scale engine parts, covering the design process, additive manufacturing, and aerothermal testing. A turbine center frame, nearly one meter in diameter, serves as a demonstrator case. Employing a multi-objective Design for Additive Manufacturing (DfAM) approach, the frame's structure underwent optimization through generative design, aiming to minimize mass, maximize stiffness, and meet strength requirements. Furthermore, the manifold section of the frame was optimized to reduce system pressure loss within the designated design space. Inconel 718 using LPBF was selected, with initial segments confirming manufacturability. The manufacturing process was fine-tuned for productivity and part properties, establishing design guidelines accordingly. Subsequently, the optimized manifold design underwent successful aerothermal testing on a specific test rig under various flow conditions. The redesigned frame showcased a 34% weight reduction and a 91% decrease in pressure loss while consolidating over 100 parts into one assembly. For the production, two alternatives are discussed. On the one hand, the final design was printed using the GE Additive ATLAS, the largest available LPBF system, validating the AM feasibility for large-scale parts under controlled laboratory conditions. On the other hand, a modified design is proposed that allows for the printing of segments on a regular-sized AM machine and a subsequent welding.Publicationtype: Conference Paper35