# Generic Digital Twin Architecture for Industrial Energy Systems
## 5 Discussion  
The aim of our proposed GDTA is a simple architectural model that captures the essential components of a DT in a technology-independent way. We presented various concepts and architectures for DTs in Section 1.2. It becomes clear that DTs have to be able to handle various types of models for different applications. Also, a structuring of different functionality by using services as well as information and knowledge management are essential to gain a certain level of cognition. The user interaction with a DT is a very important aspect, as in Industry 4.0 the workers or operators will be the most flexible part of the CPS. They have the role of a strategical decisions-maker and problem solver [39] so they have to interact closely with a DT. This is not reflected by most frameworks or concepts. Almost all presented frameworks or architectures use a layered structure to specify some functionality. Various layers are introduced with different names, which often perform similar tasks. This leads to confusion. The alignment of our architecture with RAMI 4.0 facilitates a standardized wording and common understanding of the layers in a DT. This helps to classify, combine, and re-use already existing frameworks and technologies in the area of Industry 4.0 and the IIoT for designing and implementing a DT. In the following paragraph, the concepts, architectures, and frameworks for DTs, which have been introduced in Section 1.2 are re-visited and set into relation with our proposed GDTA. The foundation of our GDTA is the 5D-DT concept [13]. The data and service dimension of this concept is refined in our architecture by identifying service clusters and introducing a Shared Knowledge base, which semantically enriches available data and stores information about resources and services. This Shared Knowledge base is the main component inside the Smart Data Service in our proposed architecture and enhances the data dimension of the 5D-DT. The suggested architecture for an \"Intelligent Digital Twin\" in [14] emphasizes the usage of AI inside a DT to evaluate \"what-if\" scenarios. Therefore, certain components in the architecture are defined, like an \"Intelligent Algorithm\" module, or a \"Co-simulation Interface\". In our opinion, these are quite specific modules, which can be implemented by distinct services of the proposed GDTA (e.g., a simulation service can support co-simulation), but do not always have to be present in a DT. Also, the separation of \"intelligent\" algorithms from the services into dedicated components seems not always beneficial. Figure 11: Results of the Simulation Service for the output temperature \(T_{out}\) of the Packed-Bed Thermal Energy Storage (PBTES) for a specific load cycle. Nevertheless, important aspects and problems are addressed in this work, like user feedback, which is also considered in our architecture. A reference framework for DTs within CPSs is presented in [6] where four main building blocks are identified. These blocks can be mapped to certain layers of our proposed architecture, which is aligned with RAMI 4.0: The key concepts of the \"Service Platform\" are found in the Functional Layer in our architecture. The \"Physical Entity Platform\" is equivalent to the Asset and Integration Layer. In addition, our architecture captures the aspect of the communication layer. The main aspects of the \"Data Management Platform\" can be located on the Information Layer. As we propose a service-oriented architecture for the GDTA, some of the mentioned data management methods, like data processing, data cleaning, data analysis, data mining and information extraction can be carried out by specific services of the Functional Layer. The \"Virtual Entity Platform\" includes various \"Semantic Models\". This corresponds to our idea of a View on the Virtual Entity in the GDTA, consisting of various semantic simulation models in combination with context information, stored in the Shared Knowledge base.