Special Section on Digital Twins: A New Frontier in Critical Infrastructure Protection and Resilience

Abstract

A digital twin (DT) is a computational model (or set of coupled) that evolves over time to persistently represent the critical structure, its components, system, or process. Digital twin underpins intelligent automation by supporting data-driven decision-making and enabling asset-specific analysis and system behavior. Within the context of critical infrastructure systems, the digital twins represent the flow of information among connected platforms. In the future, as many agencies turn to digital twin capabilities, they have to migrate toward continuous real-time performance models and calibrate by pairing data from real-time sensors, meters, weather, and other data. The digital twin can be used to run "what-if" scenarios, predict and prevent failures, provide early alerts of anomalies, and conduct predictive analysis. The strength of a digital twin is the interconnectivity of data and models. The main characteristics of a digital twin are:It is worth noting that digital twin technology and simulation are not the same. The DT technology is more dynamic and performs real-time updates of the virtual models. Simulation, which is more "static," cannot perform any real-time updates of the virtual model. Once the input data are defined, there is no room for real-time updates. Therefore, DT technology provides more accurate behavior, including the system's performance over time. It is worth noting that a digital twin without a physical twin is a traditional model.With all the current research in critical infrastructure systems, a major missing element is the appropriate selection and use of graphical models, which govern the information and data exchange between the physical and the virtual model. The graphical model encodes the two connections between the physical and the virtual. Much research is needed in this critical area of DT implementation.This Special Section contains four papers. Badiru et al. discuss the modeling for critical infrastructure. The outcome of this model is connected to a climate variable. The work relies heavily on the work of COP26. The work has strong National Defense implications, and the authors did a fine work of proposing a new model that can serve as a blueprint in both systems engineering and address critical fracture behavior with climate as a driving factor.Yanik et al. applied DT technology as a verification and validation tool in rotating machinery. This approach has an extensive application in various industrial machines and equipment. Issues like fault diagnosis and prognosis, which are essential in industrial medicine, can be addressed using DT technology. The author's approach and idea have a wide range of applications.da Silva et al. address the performance of a classifier of a mechanical system using transfer learning. The authors used the Gaussian mixture model. The paper is application-oriented but demonstrates the effective application of the domain adaptation model.de-Carvalho Michalski et al. used the DT technology as a possible alternative to detect faults in a physical space. The key advantage of this approach is its capability to defects without prior knowledge of system operating conditions. The approach presented by the authors involves the use of system equations and sensor readings. The work is more on the theoretical side.General research on digital twin technology will only be enormous with time. Researchers must not mix the DT technology with simulation. Also, the graphical model encodes physical and virtual systems that need urgent application. In terms of the latter, if care is not taken, DT technology's decision-making will be flawed and meaningless.