Context and challenges
Using Digital Twins to leverage performance and competitiveness
Representing a key element of Industry 4.0, Digital Twins speed up digital transformation by meeting the challenges encountered across the lifecycle of complex projects. They provide a concrete response to agility, anticipation, and performance requirements by:
- Reducing the time spent on data search and reconciliation
- Improving sharing of engineering data
- Utilising data in the most suitable tools
- Consolidating the traceability of engineering choices made, including checks and verifications (configuration management/archiving of architectural choices)
As a model, Digital Twins significantly reduce the carbon impact of a project's design and operation by modelling and simulating different scenarios.
Assystem can intervene in all or part of the Digital Twin construction process: upstream to help define the problem, then during the implementation, operation, and dismantling phases.
Our approach
Asset Information Hub (AIH)
Our Digital Twin approach involves the following:
- A set of use cases integral to the project's requirements: we have developed a knowledge base dedicated to creating Digital Twins in the shape of the Asset Information Hub (AIH). Based on a set of existing or specifiable digital software components, this body of knowledge defines a series of basic or advanced use cases.
- Technical and functional blocks that are interconnected, adapted, and adjusted in line with the project's specific requirements (BIM, MBSE modelling, artificial intelligence, IoT, Data Science, etc.). With their individual value, these engineering software solutions can be combined to create a Digital Twin.
- The construction of data models resulting from the data produced, consumed, and exchanged throughout the project's life cycle and adapted to the business objectives.
Our digital expertise for the benefit of Digital Twins
Digital Twins integrate one or more technologies within a virtual model that are adapted to a project's specific features. They are underpinned by a whole host of digital technologies in which our experts are fully proficient:
- Method (MBSE)
- Tool-based approaches: BIM, PLM
- IoT: real-time collection and tracking of data
- Augmented Reality: visualising and interacting with 3D objects
- Virtual Reality: learning and training
- Calculation & simulation tools (AI, business calculation software)
- Data science: analysis, diagnosis and prognosis of data (real-time and historic data)
- Cybersecurity
Benefits for the customer
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Gained time: fewer low value-added tasks/Automation
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Increased efficiency: better knowledge of the informational legacy/Acceleration of processes
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Optimised costs: robust tool-based ecosystem/Fewer internal loop-backs and less rework
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Decision support: anticipation and simulation of scenarios/User training
examples of realisations
Example of a project in the design phase:
We are currently developing the Digital Twin of a nuclear microreactor, devised by start-up Naarea, to model and simulate its behaviour. This Digital Twin will allow us to quickly determine the best design, provide critical elements to validate the conception, then rapidly launch the physical prototype's construction. In addition, thanks to its knowledge of strictly regulated environments, Assystem will support Naarea in the development of this new technology in compliance with the nuclear regulatory framework.
Example of a project in the construction and service deployment phase:
We were tasked with creating a complex model of an existing facility. This involved developing a digital twin via Building Information Modelling (BIM) and digitalising a 3D point cloud. The aim of this digital model was to acquire the relevant information and technical data concerning the facility's configuration to optimise its operations and information management.
Example of a project in the operating phase:
In the scope of support by the French Alternative Energies and Atomic Energy Commission (CEA), for installations and refurbishment works at Marcoule, the information's structuring via a Digital Twin ensured continuity in the data lifecycle (from its source on the ground to its subsequent use), resulting in improved reliability and time savings, notably in the use of site visits to detect anomalies and differences.
Example of a project in the dismantling phase:
We modelled a nuclear facility via a Digital Twin to simulate possible scenarios for its decommissioning. Developed as a real knowledge management tool, the twin will allow us to simulate certain parts of the dismantling process, and to obtain automated, optimised estimates to best manage the facility's configuration throughout this process.
The method we use to create an efficient Digital Twin is based on three pillars. A systemic approach, therefore global, to integrate all components. Pragmatism based on our knowledge of the issues and constraints faced by industries. And an analysis of each component's cost-benefit ratio to optimise the solution's ROI.