# Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
## 2 Literature Review  
### Digital Twin  
A digital twin is defined as one virtual representation of a system (or an asset) that can be used to calculate the states of the system and to make the information available. The integrated models and data of the digital twin can provide decision support over the life cycle of the system or asset. The idea to use a twin model can be dated back to the 1970s when two identical space vehicles were built in NASA's Apollo program to allow for mirroring of the conditions of the space vehicle [6]. Although the concept of digital twins was first put forward in 1991 [7], the model of the digital twin was first introduced in 2002 as a concept for product lifecycle management (PLM). After its initial names of mirrored spaces model (MSM) and information mirroring model (IMM), the model was finally referred to as the Digital Twin in 2011 [8]. The digital twin has been applied to a wide range of industries including aerospace [9, 10], automotive [11, 12], healthcare [13, 14, 15, 16], manufacturing [17, 18, 19], and smart city [20, 21]. Some early applications of the digital twin can be found in NASA's spacecraft [22, 23, 24] and in the US Air Force's jet fighters [25]. More recently, world-class vendors such as Dassault Systemes, PTC, and Siemens use the digital twin concept in their PLM. The digital twin model was also proposed for the robust deployment of IoT [26]. Aiming at developing digital twins for all built cars, TESLA enabled synchronous data transmission between their cars and the factory [27]. From 2017 to 2019, the Gartner listed the digital twin as being among the top 10 strategic technology trends and indicated that billions of things would have their digital twins within 3-5 years [28, 29, 30]. Although the concept of the digital twin is not new, it was more a descriptive one and lacked auxiliary technologies in the first few years [31, 32, 33]. Figure 1 shows the number of results obtained by searching \"digital twin\" as a \"topic\" in the Web of Science database, indicating a tremendous increase of research interest in the digital twin both in industry and academia, especially since 2017/2018. In terms of the country of study, China is the leading country in publications on the digital twin, followed by Germany and the USA. Even though the digital twin technologies are of interest to companies in the industry, a major part of the studies (about 79%) are conducted by researchers in academia, according to a survey [34]. Most academic research focuses on improving modelling techniques rather than optimizing data and implementing digital twins. Only 6% of the articles are initiated in the industry; the remaining are collaborative studies between academia and industry. There are few connections between industry and academia, particularly due to commercial secrecy. To meet the new requirement from applications, [PERSON] et al. (2019) presented an extended five-dimension digital twin model, adding data and services to the initial three-dimensional digital twin concept. Figure 2 shows the key technologies for modelling each dimension of the digital twin. [PERSON] et al. (2019) classified the tools for the service applications of digital twins into platform service tools, simulation service tools, optimization service tools, and diagnostic and prognosis service tools. A list of the tools for each category is shown in Figure 3.  
Figure 1: The number of published results about digital twins in the Web of Science. Figure 2: Five-dimensional digital twin model and the key technologies. PE—Physical Entity, VE—Virtual Entity, Ss—Services, DD—Digital twin data, CN—Connection. Reproduced from ([PERSON] et al., 2022), with permission from Elsevier, 2022. ANSYS Twin Builder is one of the multi-physics simulation packages used to add physical realism to the digital twins [27].