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Exploring Architectures for Integrated Resilience Optimization
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AI
DOI：10.2514/1.I010942.png)
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Abstract
En 中文
To achieve system milience, one can leverage high-level design features (e.g., redundancies and fail-safes), adjust operational profiles (e.g., load or trajectory), and use appropriate contingency management (e.g., emergency procedures) to mitigate potential hazards. For example, in the design of a novel drone, one would optimize the rotor and battery pack architectures (design), flight-plan (operations). and flight reconfiguration plans (contingency management) to maximize operational value while minimizing failure risk. In this work, the integrated resilience optimization formulation of the resilient design problem is defined, in which the sy stem design, operational profile. and contingency management are optimized in a single framework. To understand how best to leverage this framework in early design exploration, sequential, all-in-one, and bilevel optimization architectures on the exhaustive search of a discrete-variable drone model are then compared in terms of their effectiveness and computational performance. This comparison shows that using a bilevel or all-in-one optimization architecture can lead to better solutions than sequential architectures in design problems where the levels are coupled. Additionally. for this problem, a bilevel structure has lower computational cost than the all-in-one architecture, especially when the lower-level resilience optimization problem is decomposed into independent subproblems for each set of fault modes.
Keywords:
MULTIDISCIPLINARY DESIGN OPTIMIZATION
FRAMEWORK
MODEL
SYSTEMS
Journal
IF:
1.5
Papers:
71
Citations:
987
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