Digital Twin technology applications in AEC Industry with Design & construction projects
What is a digital twin?
A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It spans the object's lifecycle, is updated with real-time data, and uses simulation, machine learning, and reasoning to help make decisions
How does a digital twin work?
The studied object—for example, a wind turbine—is outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical object’s performance, such as energy output, temperature, weather conditions, and more.?The processing system receives this information and actively applies it to the digital copy.
After being provided with the relevant data, the digital model can be utilized to conduct various simulations, analyze performance problems and create potential enhancements. The ultimate objective is to obtain valuable knowledge that can be used to improve the original physical entity.
Digital twins versus simulations
Although simulations and digital twins both utilize digital models to replicate a system’s various processes, a digital twin is actually a virtual environment, which makes it considerably richer for study. The difference between a digital twin and a simulation is largely a matter of scale: While a simulation typically studies 1 particular process, a digital twin can?run any number of useful simulations to study multiple processes.
The differences don’t end there. For example, simulations usually don’t benefit from having real-time data. But digital twins are designed around a two-way flow of information that occurs when object sensors provide relevant data to the system processor and then happens again when insights created by the processor are shared back with the original source object.
By having better and constantly updated data related to a wide range of areas, combined with the added computing power that accompanies a virtual environment, digital twins can study more issues from far more vantage points than standard simulations can, with greater ultimate potential to improve products and processes
Types of digital twins
There are various types of digital twins, depending on the level of product magnification. The biggest difference between these twins is the area of application. It is common to have different types of digital twins co-exist within a system or process. Let’s go through the types of digital twins to learn the differences and how they are applied.
Component twins are the basic unit of a digital twin, the smallest example of a functioning component. Parts twins are roughly the same thing, but pertain to components of slightly less importance.
When two or more components work together, they form what is known as an asset. Asset twins let you study the interaction of those components, creating a wealth of performance data that can be processed and then turned into actionable insights.
The next level of magnification involves system or unit twins, which enable you to see how different assets come together to form an entire functioning system. System twins provide visibility regarding the interaction of assets and may suggest performance enhancements.
Process twins, the macro level of magnification, reveal how systems work together to create an entire production facility. Are those systems all synchronized to operate at peak efficiency, or will delays in one system affect others? Process twins can help determine the precise timing schemes that ultimately influence overall effectiveness
History of digital twin technology
The idea of digital twin technology was first voiced in 1991, with the publication of?Mirror Worlds, by David Gelernter. However, Dr. Michael Grieves (then on faculty at the University of Michigan) is credited with first applying the concept of digital twins to manufacturing in 2002 and formally announcing the digital twin software concept. Eventually, NASA’s John Vickers introduced a new term—“digital twin”—in 2010.
However, the core idea of using a digital twin as a means of studying a physical object can actually be witnessed much earlier. In fact, it can be rightfully said that NASA pioneered the use of digital twin technology during its space exploration missions of the 1960s, when each voyaging spacecraft was exactly replicated in an earthbound version that was used for study and simulation purposes by NASA personnel serving on flight crews
Advantages and benefits of digital twins
Better R&D
The use of digital twins enables more effective research and design of products, with an abundance of data created about likely performance outcomes. That information can lead to insights that help companies make needed product refinements before starting production.
Greater efficiency
Even after a new product has gone into production, digital twins can help mirror and monitor production systems, with an eye to achieving and maintaining peak efficiency throughout the entire manufacturing process.
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Product end-of-life
Digital twins can even help manufacturers decide what to do with products that reach the end of their product lifecycle and need to receive final processing, through recycling or other measures. By using digital twins, they can determine which product materials can be harvested
Digital twin markets and industries
While digital twins are prized for what they offer, their use isn’t warranted for every manufacturer or every product created. Not every object is complex enough to need the intense and regular flow of sensor data that digital twins require. Nor is it worth it from a financial standpoint to invest significant resources in the creation of a digital twin. (Keep in mind that a digital twin is an exact replica of a physical object, which could make its creation costly.)
Alternatively, numerous types of projects do specifically benefit from the use of digital models:
Therefore, the industries that achieve the most tremendous success with digital twins are those involved with large-scale products or projects:
Digital twin market: Poised for growth
The rapidly expanding digital twin market indicates that while digital twins are already in use across many industries, the demand for digital twins will continue to escalate for some time. In 2022, the global digital twins market was projected to reach USD 73.5 billion by 2027
The future of digital twin
A fundamental change to existing operating models is happening. A digital reinvention is occurring in asset-intensive industries that are changing operating models in a disruptive way, requiring an integrated physical plus digital view of assets, equipment, facilities, and processes. Digital twins are a vital part of that realignment.
The future of digital twins is nearly limitless because increasing amounts of cognitive power are constantly being devoted to their use. So, digital twins are constantly learning new skills and capabilities, which means they can continue to generate the insights needed to make products and processes more efficient.
In this article on transforming asset operations with digital twins, learn how change impacts your industry
Digital twins technology is revolutionizing the construction industry by providing a virtual representation of physical assets, enabling real-time monitoring, analysis, and optimization throughout the project lifecycle.
Design and Planning: Digital twins allow architects, engineers, and designers to create a detailed virtual model of the building or infrastructure project. This digital representation enables stakeholders to visualize the project in 3D, simulate different design scenarios, and identify potential issues before construction begins. By integrating data from various sources, such as BIM models, IoT sensors, and historical project data, digital twins facilitate collaborative design and planning processes
Construction Monitoring: During the construction phase, digital twins provide real-time insights into the progress of the project. By integrating data from sensors embedded in the construction site, drones, and other IoT devices, project managers can monitor key metrics such as progress, quality, and safety. This real-time monitoring enables proactive decision-making, early issue detection, and improved project coordination
Asset Management: Once the construction is complete, digital twins continue to add value during the operation and maintenance phase. By linking the digital twin to the physical asset through IoT sensors, maintenance records, and performance data, facility managers can track the asset's condition, predict maintenance needs, and optimize its performance over time. This proactive approach to asset management helps extend the lifespan of the asset and reduce operational costs
Simulation and Optimization: Digital twins enable construction companies to simulate different scenarios and optimize project performance. By running simulations based on real-time data, stakeholders can identify potential bottlenecks, optimize resource allocation, and improve overall project efficiency. This data-driven approach to decision-making helps construction companies deliver projects on time and within budget
Risk Management: Digital twins provide a comprehensive view of the project, allowing stakeholders to assess and mitigate risks effectively. By analyzing data from various sources, such as weather forecasts, supply chain disruptions, and site conditions, project managers can anticipate potential risks and develop contingency plans. This proactive risk management approach minimizes project delays, cost overruns, and safety incidents
All things considered, digital twin technology gives construction organizations an effective instrument to increase project outcomes, stakeholder engagement, and industry innovation. Construction professionals may efficiently mitigate risks, expedite asset management, simulate various scenarios, optimize design and planning procedures, and track construction progress in real-time by utilizing digital twins throughout the project lifecycle
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