Digital Twin for Sustainable Energy: Enhancing Fusion Powerplant's Lifetime Value
Technology Category
- Analytics & Modeling - Digital Twin / Simulation
- Functional Applications - Computerized Maintenance Management Systems (CMMS)
Applicable Industries
- Electrical Grids
- Transportation
Applicable Functions
- Maintenance
- Product Research & Development
Use Cases
- Digital Twin
- Virtual Prototyping & Product Testing
Services
- System Integration
- Testing & Certification
About The Customer
Assystem is an international engineering and digital services group that focuses on low-carbon projects to accelerate the transition to clean energy. The group is committed to the development of decarbonised electricity, including fusion energy, renewables, and electricity grids, and clean hydrogen. Assystem is also driving the use of decarbonised electricity in industrial sectors such as transportation. The group was contracted by the United Kingdom Atomic Energy Authority (UKAEA) to work on fusion energy-related programs, particularly the development of physics-based digital twins for their operational powerplants.
The Challenge
Assystem, an international engineering and digital services group, was contracted by the United Kingdom Atomic Energy Authority (UKAEA) to develop physics-based digital twins for their operational fusion powerplants. The challenge was that fusion powerplants required complex digital simulation models during the design assessment phase. The inspection and maintenance intervals and total life of these powerplants were defined based on the expected loading on the as-designed model, which often differed from the actual loads the plant was subjected to. This discrepancy provided a scope for programs aimed at improving the plant’s lifetime value or quantifying the effects of higher-than-expected usage. Assystem wanted to leverage the expensive design models to create a digital twin by inputting the sensor data that was livestreamed from the plant. This would help engineers understand the plant’s structural integrity and further optimize inspection and maintenance schedules.
The Solution
The solution involved the use of the hosted version of Altair SmartWorks IoT, which formed the backbone and analytic front end of the solution. It allowed automation through serverless backend functions, data storage, edge connectivity to the operational systems, and advanced dashboarding capabilities that provided users with immediate insight. The pilot phase used synthetic data to simulate the real-time data stream from operating plants into the IoT platform. Defined Python scripts triggered related physics models, which then processed the data and returned the results back to the IoT platform. A configured front end provided visual analytics highlighting event repercussions for better decision-making. Finite element analysis (FEA) models from multiple vendors, a commercial-grade fatigue analysis solver, and real-time weather data from OpenWeather were all connected to the IoT platform. The Simulation Process Data Management (SPDM) system at UKAEA was linked to store the data models. Email and SMS were also integrated to alert the operators to exceptional events.
Operational Impact
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