# Is Digital Twin Technology Supporting Safety Management? A Bibliometric and Systematic Review
## 2 Background  
The DT concept first appeared in the aerospace field around the 1970s (Figure 1); the first complete characterization was proposed by [PERSON] during a course on Product Lifecycle Management at the University of Michigan [20]. Figure 1: The timeline of the evolution of the definition of a Digital Twin (DT). Basically, a DT refers to a system consisting of three main subsystems: (a) physical products in real space, (b) virtual products in virtual space, and (c) data and information that tie the virtual and real products together. [PERSON] [20] depicted DT flow as a cycle between the physical and virtual states (called twinning), with data flowing from the physical to the virtual and information and processes flowing from the virtual to the physical states (Figure 2). Then, NASA provided the first definition of a DT for the aeronautic sector [21]: A \"digital twin is an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin\". Differently from the first definition, this one referred explicitly to the concept of simulation modeling, which is often adopted in several tools for supporting the design, validation, and testing of a system. Within this definition, the simulation is considered as a multi-physics model, as more than one physical field is simultaneously involved and different physical properties are integrated; simulation modeling is also usually multi-scale because different levels of time and/or space are used; finally, it is probabilistic because it could easily be developed with probability calculations. These features characterize traditional simulation models, and they are enhanced in DT applications due to their stricter connection with the physical world. Traditional simulation models are developed based on real data, but their updating is usually a static process [22]. Differently, in DTs, a data update process is required, and this represents the most relevant feature of DTs. Thus, due to the diffusion of the Industry 4.0 paradigm, a huge amount of data about physical systems are now available--even in real time--to be used for operation redesign and control. This availability is becoming the main key enabler of DT system development in manufacturing systems. From 2010 onwards, the definition of DTs given by NASA was modified, and new definitions that were more focused on the industrial sector were proposed in the scientific literature. [PERSON] et al. [23] proposed DTs as an advancement in predictive manufacturing systems. In their definition, the DT is defined as a simulation model that acquires real data and transfers them to a simulator in the cloud. In line with this study, [PERSON] et al. [24] stated that DTs, by combining real data with simulation models, allowed for the drawing up of a forecast based on realistic data, thus providing a sort of guidance system that supports operators and planners during normal operation, as well as during maintenance and service. [PERSON] [25] described the DT as a computerized model of a physical device or system that represents all functional features and links with the working elements. In 2018, some authors identified the Digital Twin as a digital representation of a physical production system that uses integrated simulations and service data, holding information from multiple sources across a product's life cycle. This information is continuously updated based on operational changes and is visualized in different ways to forecast  
Figure 2: Digital Twin concept. current and future conditions of the physical counterpart in order to enhance decision making [26; 27]. Recently, one of the latest definitions identified the Digital Twin as a virtual instance of a physical system (twin) that is continually updated with the latter's performance, maintenance, and health status data throughout the physical system's life cycle [28].