Current goals for emission-free aviation require novel system concepts, such as fuel cell-driven propulsion. Yet, such concepts do not exist in current aircraft. With such low operational experience, the requirements for system design may not be fully understood. Consequently, there is a rising potential for “blind spots” in the design that emerge in integration testing or even later, leading to costly adaptations. These unexpected scenarios may be discovered early by intelligent exploration during virtual, simulation-based testing. However, there is still a gap between the physical-dynamic test data and the functional-logic requirements definition by engineers. Test results may contain many data measurements. For a human engineer, it is time-consuming and error-prone to process the amount of time series data, making it impractical in an industrial setting. To enable the engineering evaluation and derivation of missing requirements, a computer-aided abstraction step is needed. This work presents a framework to derive principal functional-logic scenarios from critical test data and to present them visually. Concretely, analysis agents extend the data with discrete system states, followed by feature-based clustering. Finally, single sequences are derived for each cluster. The results are visualized as functional-logic parallel lifeline charts. The approach is evaluated using a fuel cell-driven propulsion example.
D. Hillig, Frank Thielecke· Journal of Aerospace Informa...· 0 citations
Future aircraft with increasingly flexible high aspect ratio wings are more vulnerable to gust and turbulence encounters. Active control technologies are therefore required to mitigate the effects of atmospheric disturbances and reduce structural sizing loads. However, the achievable load alleviation performance is constrained by system limitations such as time delays, parasitic dynamics, actuator limits, and sensor noise. In this context, model predictive control systems offer strong potential, as they can address these limitations. This paper presents the design and evaluation of such a model predictive gust load alleviation controller for a flexible test wing. The aeroelastic simulation model is based on a modal description of the structural dynamics and aerodynamic strip theory, with its parameters identified from ground vibration and wind tunnel tests. A Kalman filter is designed to estimate structural loads and non-measurable quantities including generalized structural coordinates and wind disturbances from highly noisy wind tunnel measurements. Preview information of upcoming gusts is provided to the controller, enabling feedforward control to compensate for time delays. The formulation can account for actuator limits and maximum allowable loads, ensuring effective operation within the system boundaries. To reduce the computational effort of the controller, Laguerre functions and an efficient soft output constraint formulation are employed. The resulting control system is evaluated in virtual wind tunnel tests based on the identified model with gust encounters of varying frequency. Further, the effects of degraded actuator limits and failure cases are investigated. Particular emphasis is placed on encounters with short and load-critical gusts, where the controller achieves good load alleviation performance despite restrictive system limitations.
Leif Rieck, Benjamín Herrmann, O. Luderer et al.· CEAS Aeronautical Journal· 0 citations