Modern societies depend on the unrestricted availability of their critical infrastructures and their services. Critical infrastructures include, for example, water, food and energy supplies, transportation systems, telecommunications systems, and healthcare.
Due to the increasing level of automation and digitalization, infrastructure systems, processes and resources are gaining in complexity and form highly interconnected networks. The behavior and demand of society have a strong influence on the technical systems and must be considered as part of these systems. In addition, humans are involved in service delivery. These complex socio-technical systems interact with each other while continuously providing essential services to their respective communities.
Infrastructures are not only exposed to manageable disruptions and failures, but also to extreme events such as extreme weather events, mechanical failure, human failure, accidents, attacks, and previously unknown and thus non-specified events. Such events can exceed feasible safety and security measures in place. Unexpected and unpreventable disruptions can cause cascading failures that propagate through the infrastructure systems especially in the light of counter-intuitive system behavior. Therefore, it is vitally important to strengthen the resilience of critical infrastructures towards any kind of disruption or catastrophe, to preserve these lifelines during and after disruptive events.
In the context of critical infrastructures, resilience describes the ability to continue providing essential services reliably and with little interruptions in the face of disruptive events of any kind. In general, the resilience of a system depends on a set of dynamic skills and coping strategies. A highly resilient system is prepared for disruptions of unknown nature, monitors anomalies, responds and recovers in the immediate situation, learns from the past, and adapts to new threats. Resilience complements the generally accepted design goals of efficiency, usability and sustainability.
Since 2017, the new concept of Digital Twins has been increasingly adopted by research and industry for application in many fields such as industrial manufacturing, process industry, building management, health and smart city. Digital Twins are applied as a comprehensive tool that can be used to monitor, analyze, control, and optimize a system throughout its entire lifecycle. Due to the ongoing efforts in the area of digitalization, implementation of Digital Twins of large-scale systems become more and more feasible.
First introduced by Grieves in 2002, the Digital Twin describes a concept of a connected virtual representation of a real system, with both instances coupled to each other in real-time. The virtual instance models and augments the real system in as much detail and accuracy as necessary for the intended use case, i.?e., with regard to its form, function and behavior. In contrast to classical models, Digital Twins are bidirectionally coupled by means of a twinning mechanism. This ensures the virtual replica to be in an up-to-date state. Tools utilizing the replica can implement smart functionalities, simulation and control action on the real object.