Digital Twin: a winning equation for the nuclear industry

The digital twin concept is a major advance in Industry 4.0, helping us to meet the challenges of developing complex systems, optimising costs, increasing productivity, and improving safety. Here, we’ll define this technology, explore its use cases and understand its keys to success.

Digital Twin: a tool for major challenges and an asset for the construction of new nuclear programmes.

With the rapid digitalisation of industries around the world, data management remains a major challenge, and digital twins are no longer the exclusive privilege of major groups. Today, a growing number of smaller organisations in the industrial sector are adopting this technology to optimise performance and reduce risks. 

Initially used in the aeronautics and automotive industries, digital twins can be defined as the digital replica of a complex system (a current or future installation, a process, a piece of equipment, etc.). Schematically, it can be seen as an organised repository of data (technical, design, project management) related to this system. There are several kinds of digital twins:   

• Static: dedicated only to the centralisation, representation and consultation of data, in some cases linked to 3D digital models. 
• Dynamic: coupling the digital model (often from the 3D model) and the data collected in real time using sensors installed on the physical installation.  
• Simulation: modelling the operation and physical constraints applied to an environment.   

Through simulation, data collected on the evolution of the system throughout its life cycle (real and/or virtual), creating multiple scenarios to enable better understanding of operating dynamics and inform decision-making. 

Effective data management is vital to ensure the success of the construction phases of new nuclear programmes. This is especially important as the stages between the decision to commit to a nuclear project, the actual start of construction work and then the production of electricity are numerous and involve numerous stakeholders over long periods. They require the mastery of massive volumes of ever-evolving data from the study phases to commissioning over a period of 10 to 15 years.  

The benefits of digital twins are numerous: for example, better accessibility of asset data through graphical representation; prioritisation of investments by cross-referencing historical data with captured data and human expertise; greater anticipation and increased performance thanks to the simulated maintenance operations; and optimisation of operations thanks to the simulated unit shutdown scenarios.

Having successfully delivered this technology within numerous projects in recent years, Assystem’s teams have been able to identify several recurring use cases: 

• Facilitating access to data and reinforce the quality and integrity of information. For example, within the CEA’s installations and refurbishment works framework at Marcoule (France), efficient structuring of information using a digital twin enabled continuity in the data life cycle (from its source, as close to the field as possible, to its use). This reinforced the reliability of the information, particularly during site visits to detect discrepancies and gaps. This is an example of a Static Digital Twin, but with a strong interconnection with the field via dematerialised forms. 
• Identifying future problems, such as a conflict of space.  One key solution is 4D, enabling the creation of a 3D view with the time-sequencing of operations. This approach was adopted during a project to create a 3D representation of the steps involved for the installation of specific nuclear systems to consolidate the test sequence.  
• Performing calculations to optimise predictive maintenance or operation of installations by anticipating the effects of a situation or incident. This was the case in a medicine production plant where particle dispersion following a breakdown has been modeled to define when and how to trigger the ventilation systems. 
• Improving project management through better information sharing. Within dismantling operations such as the Fessenheim nuclear reactor, the use of automated BIM modelling technologies coupled with the building’s point clouds enabled the production of a static digital twin. The resulting data and subsequent simulations were key elements in the preparation of future dismantling operations with all of the teams involved. 

By centralising and modelling all of the project’s data, digital twins make it possible to control the complexity of projects and to meet the various economic, regulatory and safety challenges. This ensures better control and traceability as well as the opportunity to leverage large volumes of data to optimise the construction and operation of nuclear power plants throughout their entire life cycle. 

Defining needs, a key point in the process

Digital twins are often approached solely through technical feasibility, which makes the approach less effective. 

For Assystem, the essential question is not so much that of the technology – there will always be an appropriate solution on the market – but the purpose: what will the digital twin be used for? This step of expressing the need is crucial for extracting the useful information and data, which will then condition the technologies to be mobilised. It ensures avoidance of unnecessary complexities within the project which could result in economic or environmental overspending. 

The approach must be based on three points: a clear set of goals shared by all; strong governance capable of defining the path to these goals; and sufficient material and human resources to achieve them. Between the preliminary brainstormings, specifications, development of tools and the change management, it is necessary to provide long-term support.   

Such a project is not just about development and tools: it requires time upstream to define the strategy (what is expected, which entities and which processes are involved?) and at the end of the deployment, to accompany users through the change and identify any blockages. Pragmatism and vigilance, particularly at the start and at the very end of the project, are key to a successful transition.

Engineering coupled with digital: added value to meet current needs

Thanks to their combined competences of complex engineering projects and years of expertise in digital, their technological independence and their deep knowledge of their customers’ needs, Assystem’s teams are able to influence and support every stage of the digital twin lifecycle: from problem definition, implementation, and deployment. 

Relying on the whole range of digital technologies (MBSE, PLM, BIM, CMMS, reverse engineering, 3D scans and 3D modelling), Assystem develops methods and solutions to accelerate and meet a wide variety of needs.  

Digital twins are never “ready-to-deploy” tools: they are designed according to the needs of each project and require a very good knowledge of the environment, processes, and trades of the industries. These strategic and operational missions are at the heart of Assystem’s added value. 

Investing in the projects of the future

Assystem develops and contributes to the construction of digital twins for various innovative, complex industrial projects that contribute to the future of decarbonised energy. All of these projects are highly critical programmes with very high requirements in terms of reliability, safety, etc. Here are some examples of where Assystem experts are working on digital twins: 

• Grid Modelling [Static Digital Twin]

Based on a siloed body of paper documentation, Assystem developed and implemented a database and an optimised model of Uzbekistan’s electricity network. This model allows direct access to a descriptive set of technical data for any facility within the network, as well as the underlying documentation which has been automatically defined and structured.  

•  ESTRADE/Demologist [Digital Simulation Twin]

Using its engineering expertise, Assystem has modelled a nuclear facility using a digital twin to simulate dismantling scenarios. Developed as a real knowledge management tool, it will enable parts of the dismantling process to be simulated, also enabling automated generation of optimised estimates to manage the facility’s configuration more effectively.  

• Modelling for Fusion – UKAEA [Digital Simulation & Dynamics Twin]

To support the development of nuclear fusion, Assystem has proposed an approach to leverage value from existing engineering models by connecting them to plant and environmental data via an IIOT* platform. This allows operational risks to be analysed alongside design assumptions, thereby accelerating the design process. In parallel, modelling is also being used to support the initialisation of probabilistic and operational safety studies. 

•   XSMR [Digital Simulation Twin]

Designed by the start-up Naarea, this project is supporting the development of an innovative, molten salt micro-reactor concept which will operate like a generator. The reactor will initially be modelled in the form of a digital twin, before being prototyped. This simulator will then be used to evaluate the behaviour of the reactor to optimise its operation. 

•  CONNEXITY [Dynamic Digital Twin]

EDF’s ConnexLab pilots R&D projects aimed at designing tomorrow’s nuclear industry based on digital transformation. It regularly brings together its partners (including Assystem) to pool their R&D resources. For example, the laboratory has developed a demonstrator for a navigation app similar to ’Waze’ (to help operators navigate the complex environment of nuclear power plants by indicating work and risk areas), leading to the creation of a convincing proof of concept that is being tested in a real industrial environment.  

Digital twins are a way of addressing the complexity of a project. In reality, we refer to the digital twin as soon as we talk about organised data management for a specific functionality. It’s possible to make simple, agile deployments, provided that the needs are well expressed.

Victor Richet, Head of Digital – Inde

The digital twin market and its needs are constantly growing. Assystem is giving itself the means to move forward and strengthen its position in the service of digital engineering.

Marylène Huot-Marchand, Digital Twin Référent – BIM Expert