Creating a digital twin

Traditionally, buildings have been thought of as static assets. Once they are constructed, the only work to be done is routine maintenance over the building’s useful life. But today, asset owners – particularly those in manufacturing and heavy industrial processes – are beginning to appreciate that assets are in fact dynamic entities that require closer monitoring and control after construction than just traditional maintenance.

Built from a huge amount of cumulative, real-time 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.

With the help of technology such as powerful analytics and machine learning, a two-way flow of information is created between the physical and digital worlds. The real-world asset sends live performance data to its digital twin for evaluation and, if action is needed, the digital twin responds with feedback or changes to optimise performance.

A digital twin can bring many benefits, but in order to make it operate effectively, a system needs to be designed and modelled digitally from the outset, with sensors installed at key locations to ensure proper monitoring of both model and system performance.

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 no small task. It requires the integration of design inputs from a number of different 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.

 

Benefits of a twin

Using a digital twin means companies can effectively manage, monitor, simulate and control an asset or process, as well as optimise its performance over the asset’s entire lifecycle. A twin also improves system reliability – through understanding what assets require at any given time, they can be proactively and predictably replaced.

In the built environment, Burns & McDonnell has worked closely with clients in the energy space for years to tie building design and process design together into a ubiquitous model that enables asset owners and managers to make investment decisions from actionable data. This helps clients to make decisions more efficiently, understand the impacts of changes, enhance asset utilisation, and ultimately manage assets over time.

However, embarking on the digital twin journey takes time, and clients and industries are at varying 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 into the model to create a real-world simulation.

 

Challenges

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 in the system to prevent outage or catastrophic failure. Nevertheless, more and more assets have sensors and communications systems that enable them to generate data on their current condition, both from a health and an operational standpoint. 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.

 

 

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