Physical structures like buildings, power plants, manufacturing plants or airports have traditionally been thought of as static assets. Once constructed, the only work required from then on is routine maintenance. But today, a new view is emerging. Owners are beginning to view their assets as dynamic entities that require closer monitoring and control after construction.
With the help of technology such as powerful analytics and machine learning, a two-way flow of information can be created between the physical and digital worlds. Popularly called a digital twin, this computer-created simulation of a real-world asset utilises live performance data for evaluation. If action is needed, the digital twin responds with feedback or changes to optimise performance.
Built from a huge amount of cumulative, real-life data, a digital twin simulates its real-world counterpart in a live setting, creating an evolving digital replica of the physical asset’s past, present and future behaviour, including system process performance data. It connects the physical world to the digital world through interfaces such as sensors, communications platforms and data. This virtual representation of a physical asset is the ultimate combination of design, operation, process, and system models and data, enabling owner/operators to perform tasks, such as planning, analysis and optimisation, in ways that would not otherwise be possible.
Benefits of a Twin
A digital twin can bring many benefits, but in order to make it operate effectively, a system needs to be designed and modified digitally from the outset, with sensors installed at key locations to ensure proper monitoring of both model and system performance. In addition, the digital model should be used throughout the design, construction and O&M phases.
The key components in the digital twin act as the links between tangible assets and their digital counterparts. When developing a twin, you therefore need to begin with an in-depth understanding of the physical components, the assets they build up and how they fit into the overall system and associated process. This requires a digital model of the system; a digital model of system components; a digital model of the process or system; and a digital model of the physical assets.
Building a bridge between the physical and digital worlds, however, is not small task. It requires the integration of design inputs from a number of sources, including the performance characteristics of key infrastructure components and sensors that capture status at varying locations. The capture, analysis and operationalisation of data is also required.
Challenges
Embarking on the digital twin journey takes time. Industries are at various stages of maturation. The UK is currently only driving towards BIM Level 2. Transitioning or adapting from the BIM model to a digital twin is where real value comes from, because it ensures that real-time data, through sensors, is incorporated in the model to create a real-world simulation.
Despite these possibilities, the challenge for the energy industry is that it still lacks a uniform definition of what a digital twin really is, how models are created from disparate manufacturers and how they can be implemented. Are utilities already utilising digital twins if their control systems model the physical real system and its assets geospatially, for example?
Better system integration in space optimising underperforming assets and process in near real-time are all benefits of a digital twin, as is the ability to quickly identify and locate assets in poor condition. This can give utilities advanced warnings of potential system failures, enabling action that can prevent outages or catastrophic failures.
This is naturally enabling the transition to a digital twin, but we are still in the early days of development. Companies should focus on small tangible steps when it comes to implementation, to show progress, evidence early success and demonstrate installed value.
Dive into the technological tools that build the digital twin.
