Automated Optimization of Supersonic Missile Performances Taking into Account Design Uncertainties

Gaiddon, Arnaud; Greard, Jean-Noel; Pagan, Didier

doi:10.2514/6.2003-3879

Cited by 4 publications

(1 citation statement)

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“…Although it is customary in multi-disciplinary design optimisation to represent a system as a set of connected components that exchange information through connecting links (see [32] for an example of multi-disciplinary optimisation under uncertainty with Evidence Theory) in an ENM the specific properties of the nodes and the form in which they exchange information is such that Belief functions can be computed in polynomial time. The properties of an ENM and the difference with respect to common MDO [33,34] formulations will be explained in Sec. 6.…”

Section: Introductionmentioning

confidence: 99%

Space systems resilience optimisation under epistemic uncertainty

et al. 2019

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This paper introduces the concept of Resilience Engineering in the context of space systems design and a model of Global System Reliability and Robustness that accounts for epistemic uncertainty and imprecision. In particular, Dempster-Shafer Theory of evidence is used to model uncertainty in both system and environmental parameters. A resilience model is developed to account for the transition from functional to degraded states, and back, during the operational life and the dependency of these transitions on system level design choices and uncertainties. The resilience model is embedded in a network representation of a complex space system. This network representation, called Evidence Network Model (ENM), allows for a fast quantification of the global robustness and reliability of the system. A computational optimisation algorithm is then proposed to derive design solutions that provide an optimal compromise between resilience and performance. The result is a set of design solutions that maximise the probability of a system to recover functionalities in the case of a complete or partial failure and at the same time maximises the

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Section: Introductionmentioning

confidence: 99%