Augmenting MBSE with Digital Twin Technology: Implementation, Analysis, Preliminary Results, and Findings

Abstract

Model-Based Systems Engineering (MBSE) requires more significant investment than traditional systems engineering in the early system life cycle phases. Program management justifies this additional investment by arguing that such investments can be expected to produce continuous gains across later phases of the systems life cycle resulting from early detection of defects, risk reduction, improved communication, superior supply chain integration, product line definition, and enhanced traceability. Since systems continue to evolve over their life cycle, system models need to be updated continually to reflect the system's current state and thereby sustain value. However, in today's systems engineering organizations, the current practice is to reallocate modeling resources to other projects once initial modeling on a particular project is completed. This practice results in a resource shortfall that impedes the ability to continuously update system models through the later phases of the system life cycle. This paper presents how digital twin technology can be exploited within MBSE to ensure continuous model updates throughout the system life cycle. This paper also presents preliminary results from prototyping and experiments conducted with a digital twin that is continuously updated with data from the physical system operating in the real world. Finally, this paper shows how operational analysis and system modeling can be significantly enhanced by leveraging digital twin technology within MBSE.