Current Trends in Digital Twin Development, Maintenance, and Operation:An Interview Study

Digital twins (DT) are often defined as a pairing of a physical entity and a corresponding virtual entity mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Although growing in popularity in both industry and academia, digital twins and the methodologies for developing and maintaining them differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research study with 19 professionals from industry and academia who are closely associated with different lifecycle stages of the corresponding digital twins. In this paper, we present our analysis and findings from this study, which is based on eight research questions (RQ). We present our findings per research question. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for their development and maintenance. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin's lifecycle

Current Trends in Digital Twin Development, Maintenance, and Operation: An Interview Study

Digital twins (DT) are often defined as a pairing of a physical entity and a corresponding virtual entity mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Although growing in popularity in both industry and academia, digital twins and the methodologies for developing and maintaining them differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research study with 19 professionals from industry and academia who are closely associated with different lifecycle stages of the corresponding digital twins. In this paper, we present our analysis and findings from this study, which is based on eight research questions (RQ). We present our findings per research question. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for their development and maintenance. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin's lifecycle

Models Meet Data: Challenges to Create Virtual Entities for Digital Twins

In recent years, digital twin (DT) technology has moved to the center of attention of many researchers and engineers. Commonly, a digital twin is defined based on a virtual entity (VE) that exhibits similar behavior to its physical counterpart, and that is coupled to this physical entity (PE). The VE thus forms a core part of any digital twin. While VEs may differ vastly—from ones based on simple simulation to high-fidelity virtual mirroring of the corresponding PE—they are typically composed of multiple models that may originate from multiple domains, address different aspects, and are expressed and processed using different tools and languages. Furthermore, the use of time series data—whether historical or real-time or both—from the PE distinguishes VEs from mere simulations. As a consequence of the modeling landscape complexity and the data aspect of VEs, the design of a digital twin and specifically of the VE as part of it represents several challenges. In this paper, we present our vision for the development, evolution, maintenance, and verification of such virtual entities for digital twins