Getting started with Digital Twins

A Digital Twin is a virtual model of a real-world physical object or system, serving as its nearly identical counterpart for purposes like simulation, integration, testing, monitoring, and maintenance. It is fundamental to Product Lifecycle Management and spans the entire lifecycle of the physical entity it represents.

• The term "digital twin" was later coined by John Vickers of NASA in 2010. The concept consists of three parts: the physical object or process and its environment, the digital representation, and the communication channel between them.

• the US Department of Defense's Digital Engineering Strategy defines it as an integrated simulation enabled by a Digital Thread to mirror and predict activities over the life of its physical counterpart.

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When Is a Digital Twin created?

A digital twin can be created either before, during, or after the realization of the physical system. If developed before, it serves as a virtual prototype. This is useful during the design and development phase, allowing designers to simulate and test the behaviors under various conditions, making adjustments and optimizations before actual production. For example, in the construction of a building, a digital twin can help foresee and resolve potential issues, reducing claims and keeping the cost in line with budget.

When the digital twin is created after the physical object, its primary purpose is to monitor, maintain, and optimize the performance of the real object. In this scenario, sensors installed on the physical object provide real-time data to the digital twin, enabling continuous and precise management. This approach is common in industries such as manufacturing and energy, where it is crucial to monitor operating conditions and predict potential failures.

In both cases, the digital twin acts as a bridge between the physical and digital worlds, enabling more effective management and continuous optimization of resources and processes.

How Are Digital Twins Different from Existing Simulation Techniques?

While both simulations and digital twins utilize digital models to replicate processes, digital twins are distinct in their dynamic and interactive nature: for instance they offer a complex interactive UI to the users, and in general ?Digital Twin maps a large set of diverse process, while a Simulation focuses on one. The Digital Twin is much more complex from a tech standpoint, while a simulation is mainly focused on mathematical complexity.?

Where Are We in the Lifecycle of Digital Twin Technology?

In the Hype Cycle for Advanced Technologies for Manufacturers of 2023, Gartner places Digital Twins in the 'trough of disillusionment' zone, indicating that while it's not a technologically brand-new concept, it's progressing towards the 'plateau of productivity.' Overall, it signals that Digital Twins are quietly advancing towards wider adoption, poised to deliver tangible benefits as they overcome current challenges and realize their full potential.

Examples of Digital Twin Applications

1. Urban Planning: a digital twin can simulate city infrastructure and traffic patterns, allowing planners to optimize transportation systems
2. Construction Industry: a digital twin can simulate the people flow in and out of a building, visitors flows across the floors, and simulate what happens in case of an emergency to optimize the path to emergency exits
3. Manufacturing Industry: a digital twin can map a production line, enabling real time monitoring to minimize downtime.
4. Healthcare: a digital twin of an hospital could simulate the patient flow from triage to other facilities improving the SLAs
5. Automotive: A digital twin can replicate assembly lines, facilitating real-time monitoring, to improve quality control.
6. Utility: a digital twin for an infrastructure, such as power grids or water networks, can simulate operations in real-time, helping prevent reliability issues
7. Railway: a digital twin can model track layouts, train movements, and other infrastructures, enabling to simulate what happens in congestion situations and help to better schedule the traffic
8. Retail: a digital twin can simulate store layouts, customers foot traffic, usage of parking lots, allowing to simulate what happens in peak hours to reduce the congestion
9. Aerospace. A digital twin can replicate components, assembly lines, and maintenance procedures, facilitating real-time monitoring to improve: safety, reliability and efficiency.
10. A digital twin for oil and gas facilities can simulate drilling operations, production processes, and equipment performance to maximize production efficiency, and ensure safety and environmental compliance.

This overview is not comprehensive: it aims to highlight the potential of digital twins across various industries, showcasing their ability to deal with many complex business challenges. The concept of digital twin, also serves as an ideal environment for Composite AI: the digital twin acts as a platform for the implementation of combined approaches and methodologies.

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