Technical scope

Digital twin core technologies

Fundamental principles and architectures: Digital twin concepts, theories, frameworks, rules, strategies and surveys
Data governance: Scalable data pipelines, data cleaning and fusion, database management, and semantic modelling
Key enabling technologies: AI integration, ICT/IoT connectivity, edge-cloud computing, big data analytics, blockchain, and AR/VR
Modelling and simulation: Physics-based, data-driven, and hybrid modelling techniques for dynamic system representation, model validation and verification
Control and optimisation: Closed-loop control strategies, predictive maintenance, and decision-support systems
Safety and security: Cyber-physical security protocols, anomaly detection, and risk mitigation in digital twin ecosystems
Smart services: Location-based services, real-time monitoring, fault diagnosis, product lifecycle management, personnel training, etc
Platforms and Tools: Platform architectures, open-source vs. proprietary solutions, standards and practices (e.g., ISO 23247).

Power and energy systems

Grid resilience: digital twins for fault prediction, grid restoration, and black-start scenarios
Renewable integration: twin-based optimisation of hybrid systems and grid synchronisation
Substation automation: Virtualisation of protection schemes, condition monitoring, and cyber-secure communication architectures
Energy transition: impact of EVs, heat pumps, and microgrids on digital twin design.

Process system engineering

Process modelling and simulation: modelling and simulation of batch processes, continuous manufacturing, and transient operations in chemical, petrochemical, and pharmaceutical industries
Process optimisation and control: real-time optimisation (RTO), closed-loop model predictive control (MPC), AI-driven anomaly detection
Safety and risk management: leak detection, hazard simulation, and emergency response planning.

Mechanical engineering

Smart manufacturing: Twin-enabled production line optimisation, robotic assembly, and quality control
Heavy machinery: Vibration analysis, fatigue prediction, and remanufacturing strategies
Energy systems: Turbine performance monitoring and hydrogen infrastructure simulation.

Transportation and logistics

Logistics and supply chains: Twin-driven inventory management, route optimisation, real-time visibility and traceability, and warehouse automation.
Civil and construction engineering
Smart buildings: HVAC optimisation, energy efficiency, and occupant behaviour modelling.

Environmental monitoring