How to Leverage Digital Twin for System Design?

Digital Twins (DT) are deeply rooted in digital simulation environments. Today, they are still considered datadriven constructs aimed at supporting simulation, optimization, prediction on a physical system. However, data alone may not completely describe a system. This necessitates additional knowledge, encapsulated within models, which forms the foundation of the Model-Driven Digital Twins (MDDT) paradigm. At the start of a DT life-cycle, or when dealing with a system under construction, models becomes the primary artifact enabling the DT due to the lack of available data. This paper explores the advantages of simultaneously engaging in model-driven system design while preparing its corresponding DT. Using a real-world case study focused on developing a hydrogen valley, we demonstrate the substantial benefits of integrating models at the earliest
stages of the DT’s design and implementation process. This covers preparing data collection and sensors, and incorporating human knowledge throughout the system lifecycle, enhancing replicability