There are many uncertainties that complex engineered systems will face throughout their lifecycles due to changes in internal and external conditions. It is desirable for complex engineered systems to be resilient against various uncertainties. The ability to overcome such uncertainties should be embedded into the system from the beginning. However, there is a lack of applicable methodology that shows how to design a resilient system from the early design phase. This PhD dissertation introduces a new framework to apply resiliency techniques into the system from the beginning in early design stage to enable the system recovers from failures caused by internal or external uncertainties.
The first step of this research is to develop an initial functional model in early design stage and simulate the failure behavior of the system. The unique scenarios provide the information on the final behavior of the system assigned to each injected failure. A cost-risk model is developed to compare resiliency of different functional models in design space, and produce a preference ranking. The optimal design is selected based upon the cost-risk objective. The proposed framework is implemented for the design of a monopropellant propulsion system. The second part of research addressed the validation of the result generated by a selected model in the early design stage. The challenge in the early design stage is that the real system or prototype is not manufactured and there is no or inadequate information available from complete system’s behavior. Several methods are introduced. In the first method, expert knowledge is quantified based on Failure Modes and Effect Analysis to validate the result generated by a selected model in the early design stage. Another method is proposed based on non-parametric comparison of observed data and simulated data for one or more subsystems that currently exist. In addition, a local sensitivity analysis is developed to guide the designers to focus on sensitive parts of the system in further design stages, particularly when mapping the functional model to a component model. The third part of this PhD dissertation addresses the weakness of traditional design methods to cope with external uncertainty throughout the lifecycle of the system. In a traditional design approach, a pre-defined set of requirements based on market studies or user needs is established, and then the optimal design is selected to satisfy the requirements. The traditional approach is inadequate to respond to unexpected changes in initial requirements or operating conditions. To resolve this issue, a new dynamic design framework is developed to make the system resilient against the external uncertainties through the life cycle of the system. In the proposed approach, the flexible design features are utilized as control variables. By adjusting the design features to the optimal forecast obtained from the Kalman filter, the system overcomes external uncertainties. The proposed method is applied to design a dynamic satellite system.