Complex engineered systems can carry risk of high failure consequences, and as a result, resilience—the ability to avoid or quickly recover from faults—is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.
Co-simulation of complex engineered systems enabled by a cognitive twin architecture
