Digital Twins: Technologies and Applications

Dear Colleagues,

Digital Twins (DTs) have rapidly ascended as a pivotal instrument in the digital replication of Physical Systems, Processes, or Objects, capturing their essence across varying conditions, whether existing or speculative. The main distinction between DTs and other technological innovations is their dynamic nature; these virtual models are designed for perpetual, two-way interaction with their physical counterparts. Such interaction not only facilitates real-time monitoring and analysis of both the holistic behavior and individual components of the physical entity but also enables the implementation of diverse strategies, modifications, or actions when required. This capability stems from recent advancements in the fields of the Internet of Things (IoT), Industry 4.0, Analytics, Big Data Management, and Telecommunications, complemented by the integration of Actuators and Sensors for comprehensive data acquisition, mapping, and processing. Leveraging a suite of methodologies and technologies from Artificial Intelligence, Optimization, Analytics, Big Data Management, and Simulation, DTs efficiently assess, quantify, and forecast the evolving dynamics of Physical Systems.

The applicability of DTs spans an extensive array of domains that include, but are not limited to, Manufacturing, Aviation, Energy, Smart Cities, Industry, Telecommunications, Construction, Healthcare, Vessels, and Asset management. Furthermore, their utility extends to Lifecycle management, Traffic management, Project management, Education, and the exploration of human behavior in conjunction with environmental interactions. The developed methodologies are versatile, ranging from overarching frameworks to bespoke, application-specific solutions. This adaptability ensures that DTs can be tailored to meet the unique requirements and challenges of each domain or application.

The Special Issue on Digital Twins (DTs) is relevant to applied sciences fields, including:

1. Developing software and algorithms that underpin DTs.
2. Mechanical, electrical, civil, and software engineering for designing and analyzing physical systems and their digital shadows.
3. Utilizing big data, machine learning, and statistical methods to interpret DT-generated data.
4. Infrastructure and communication protocols required for DTs’ interaction with physical entities.
5. DTs in manufacturing, process optimization, and supply chain management.
6. DTs for monitoring and managing environmental systems and sustainability.
7. Patient-specific models and healthcare systems management.
8. DTs in the design, management, and optimization of urban environments.
9. Development and management of energy systems, RESs, and smart grids.
10. Precision farming, crop management, and resource optimization.
11. Management of traffic, fleet, and logistics operations.
12. DTs for educational and research purposes.
13. Design, simulation, maintenance, and management of vessels.
14. Ship design, navigation, and port management.
15. Utilization of DTs in the construction, management, and maintenance of buildings.

Dr. George J. Tsinarakis
Dr. George Arampatzis
Dr. Nikolaos Sifakis
Guest Editors

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• digital twins
• modelling
• cyber-physical systems
• industrial systems
• industry 4.0
• internet of things
• simulation
• sensors
• actuators