Complex systems—such as gas turbines, industrial plants, and infrastructure networks—are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin.

In this work the impact of combined module variances on the overall performance of a high-bypass aircraft engine is investigated. Therefore, a comprehensive sensitivity analysis on the example of a turbofan engine performance model is provided by means of Kucherenko indices. Direct influences of selected model inputs on key model outputs as well as influences due to interaction effects between these input variables are identified. The selected input variables of the performance model are partly subject to considerable dependencies that are taken into account by the Kucherenko indices. The results confirm known direct influences of deterioration effects on the key performance parameters of the aircraft engine on the one hand, and provide profound insights into complex interaction effects between the components and their impact on the V2500-A1 aircraft engine performance on the other.

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The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aerothermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan highpressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas- Path-Analysis for further modules, such as the compressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency hHPT and capacity m˙ HPT ) are particularly driven by the variation of the stagger angle. On the other hand, hHPT is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow m˙ HPT are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches.

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