# Generic Digital Twin Architecture for Industrial Energy Systems
## 1 Introduction
### Related Work
In addition to these very general concepts with a high level of abstraction, more detailed architectural concepts and frameworks have been proposed for the implementation of DTs. In the following paragraph, an overview of these concepts is given and summarized in Table 1. Table 1 also gives a classification based on their level of abstraction. A high level of abstraction means that a more general concept is presented, whereas a low level indicates a more concrete architecture or framework, targeting the implementation of a DT. They are also used in the discussion (Section 5) to set our proposed GDTA into perspective by comparing it with these concepts. \"Intelligent Digital Twin\"--In [14], an architecture for an \"Intelligent Digital Twin\" was proposed by applying algorithms known from Artificial Intelligence (AI). Next to the data acquisition interface, a synchronization interface is introduced to keep the simulation models of the DT in line with the physical asset, as they can otherwise differ over its life-cyle. Also, a co-simulation interface is described as a component of the architecture to enable communication with other DTs and to facilitate multidisciplinary co-simulation. Figure 1: Five dimensions of the Five-Dimensional Digital Twin (5D-DT) concept adapted from [13]. \"Reference Framework for Digital Twins\"--A reference framework for DTs in the context of the 5C architecture of CPS is presented in [6]. The main building blocks of DTs, including their properties (structure and interrelation), were specified for the proposed framework. Therefore, a systematic literature review was conducted followed by a framework analysis using grounded theory. They identified four main building blocks of a DT which are: a \"Physical Entity Platform\", a \"Virtual Entity Platform\", a \"Data Management Platform\", and a \"Service Platform\". They also identified three different types of physical entities, namely the \"Physical Objects\", \"Physical Nodes\", and \"Humans\". The \"Virtual Entity Platform\" consists of many virtual entity models, including information to mirror a certain aspect of the physical entity. It is responsible for the generation and maintenance of \"Semantic Models\" (geometric models, physical models, behavioral models, rule models, process models) of the physical entity to create its virtual representation. The \"Data Management Platform\" performs data acquisition, management (collection, transmission, storage, integration, processing, cleaning, analysis, data mining and information extraction), and storage. The \"Service Platform\" consists of service models and a service management layer to organize services for concrete applications. \"Cognitive Twin Toolbox\"--In [15], a conceptual architecture of the Cognitive Twin Toolbox (COGNITWIN) is presented with a special focus on the process industry. Three levels of twins were defined: A \"Digital Twin\" which only uses isolated models of the physical system, a \"Hybrid Twin\" which is also able to interconnect its models, and a \"Cognitive Twin\" which uses extended models that include expert knowledge for problem-solving and to handle unknown situations. The toolbox proposes five layers: \"Model Management Layer\", \"Data Ingestion and Preparation Layer\", \"Service Management Layer\", \"Twin Management Layer\", and a \"User Interaction Layer\". The specified model types are quite similar to the defined semantic models in [6] and consist of first-order principle models based on the underlying physics, empirical models, e.g., AI algorithms, and knowledge-driven models based on domain experts. The \"Service Management Layer\" is responsible for handling services, like registration and orchestration. Two types of services are distinguished. Data-driven and model-based driven services. The \"Twin Management Layer\" manages the structure of the DT. Especially, the synchronization problem caused by changes in the behavior of the physical system is handled here. The toolbox also introduces a \"User Interaction Layer\" where users can explore the COGNITWIN.