Digital Twins (DT) are defined as validated virtual models of physical entities and processes, with the capability to be (seamlessly) connected, in right time throughout their lifecycles, enabling simulation, performance optimisation, and informed decisionmaking.

A System of Systems (SoS) DT is a model that integrates multiple DTs of individual systems, subsystems, and components, providing a holistic, real-time view of the entire system behaviour, performance, and responses to various scenarios and conditions.

The development of advanced fully coupled DT simulation capabilities is necessary to support the design of complex, high-fidelity aerial systems, and to facilitate the generation of certification-relevant data.

This framework should comprise aerodynamic, structural, flight control system, general system, embedded software, and design capabilities from level zero to high fidelity modelling, offering the possibility to provide close and loosely coupled multidisciplinary simulations, and provide full design gradients for multi-disciplinary numerical optimisation.

The multidisciplinary analysis and optimisation capability is a design methodology for fast and reliable design space exploration, trade-offs, and requirements sensitivity assessment, hence, a key technology for modern aircraft development.

DTs need to capture the complexity of the system being modelled and its surrounding environment, including the capabilities of Allied  Nations.

Potential benefits of DTs for military applications include:

• Increase fleet availability and reliability by enabling better maintenance planning and reducing the occurrence of unanticipated damage findings.
• Improve product development and reduce lead time for the new military systems.
• Ensure fleet safety by providing better information on the condition of each individual asset.
• Incorporate added capabilities to provide operational superiority.
• Reduce maintenance costs by increasing maintenance interval, reducing inspections and maintenance labour.

The research also aims to examine the flow of digital data across different stages of the lifecycle, as well as between various domains, such as:

• Lifecycle phases: How digital data can be seamlessly shared and utilised across different stages, e.g., from design to software development to mechanical engineering.
• Application domains: How digital data can be integrated and leveraged across different areas, such as system operation, logistics, and maintenance.
• Information spaces: How digital data can be shared and utilised across different information systems and platforms, ensuring interoperability, and reducing silos.

To address the interoperability challenges of DTs in a global context, it is essential to develop a robust reference architecture that can handle  the complexities of exchanging, sharing, and reusing information across diverse systems and nations.

The key issues to be addressed are the following:

• Effective Information Exchange is challenging because of use of diverse data formats and standards, variations in modelling techniques and simulations tools (esp. considering sensor networks). This should be addressed through improvement of standardisations, implementation of middleware solutions and data catalogues.
• Coordination and Enrichment of Simulations: the main challenge is the need for faster than real time data integration from multiple sources and ensuring data consistency. These should be addressed through the creation of a federated architecture, data orchestration and cross-domain ontologies.
• Security of Information Exchange to protect against cyber threats. Data should be secured both during transfer and storage. Therefore, following technologies must be considered: encryption, access control, Intrusion Detection Systems or use of block chain technologies.
• Coherent Data Analysis, Storage, and Discovery where the main issues are about managing large volumes of heterogeneous data, handling high-resolution sensor data from multiple sources, and ensuring its quality and consistency. The proposals must address the use of cloud storage for raw data, structured data as well as big data analysis, metadata management, and use of AI and Machine Learning for data cleaning, integration and predictive analytics.
• Development and Validation of Models where the main challenges lie in ensuring accuracy, reliability of physics based and data-driven models and calibration with real-world data. The following technologies and approaches should be considered: Hybrid modelling approaches, model validation frameworks, continuous learning, and collaborative platforms.
• Explore the DT potential in non-technical areas, such as managing cost overruns, reporting progress, and coordinating multi-national capabilities. A common model database, featuring constructive entities and terrain data, can facilitate data sharing and reuse, enhance collaboration, and boost efficiency. This requires understanding data ownership, sovereignty, and sharing concepts to ensure effective management and protection of critical data assets.

The proposals must address the study and design of a SoS DT, in a modular way (sub-systems level), in order to enable a gradual and progressive development.
Different levels of interdisciplinary coupling strategies are required depending on the relevant involved disciplines. Depending on the aerial system type, exploiting the interaction between aerodynamics, structure, control laws, general systems, control software, manufacturing and performance analysis (mission and point performance) is crucial, in order to achieve an optimal design and significantly de-risk the programme overall.

The focus of this activity should encompass the entire spectrum of the systems development life cycle. Therefore, a demonstration of a concept for real-time interconnection of individual DTs of military assets with a monitoring and diagnostic dashboard is essential. This should include a framework for data transfer and feedback loops.