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Fundamentals of Digital Twins

Digital Twins Power and Energy Systems

Call it the era of Digital Twins.

The global digital twin market size was valued at $8.60 billion in 2022 and is projected to grow from $11.51 billion in 2023 to $137.67 billion by 2030.

The popularity of digital twin technology is due to its usefulness as well as its promise.

A digital twin uses virtual and augmented reality as well as 3D graphic and data modelling to build a virtual model of a process, system, service, product, or other physical object. This digital twin is an exact replica of the physical world. Its exact replica status is maintained through real-time updates.

Digital twin technology that is applicable to a wide range of environments, including the monitoring of products while they are in use and through the entire product life cycle.

Digital twins are driving the future of engineering.

Bringing together hardware, software, and data, digital twins enable engineers to optimize the design and operation of a product or service in real time.

Digital twins build on AI, IoT, and software analytics to create living digital simulations that interactively update and evolve with their physical counterparts.

Rooted in the logic of IoT systems, digital twins enable machine-to-machine learning systems in order to reduce errors. Developing and supporting digital twins requires the continuous updating of data, both in terms of ongoing operations, and in terms of adaptive analytics and algorithms.

Data empowers digital twin technology by providing networked software systems with the resources to accelerate decision-making. If manufacturing equipment is lagging, for example, data can signal the need for machinery to be fixed or upgraded before impacting operations.

Digital twin technology enables companies to test and validate a product before it even exists in the real world. By creating a replica of the planned production process, a digital twin enables engineers to identify any process failures before the product goes into production.

Since the twin system’s IoT sensors generate big data in real time, businesses can proactively analyze their data to identify problems within the system. This ability enables businesses to more accurately schedule predictive maintenance, thus improving production line efficiency and lowering maintenance costs.


Additionally, a virtual representation of a physical object can integrate financial data, such as the cost of materials and labor. The availability of a large amount of real-time data and advanced analytics enables businesses to make better and faster decisions about whether or not adjustments to a manufacturing value chain are financially sound.

Fundamentals of Digital Twins Course by Tonex

Fundamentals of Digital Twins covers the key principles of Digital Twins and how it relates to integration of digital engineering, modeling and simulations, AI/ML, 3D and integration for service and product-related data and systems. The concept of digital twins is a response to the increasing digitalization of service and product development, production, and digital products worldwide.

Digital twins are virtual replicas of physical systems, devices, services, assets, or processes used to run real time simulations. Digital twins are designed to analyze events, if scenarios, detect and prevent operational and production issues, predict performance, and optimize processes through real-time analytics to deliver and optimize business value.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

digital twin

Program Outline

Digital Twin 101

  • Key concepts behind digital engineering and digital twin
  • History of digital twin technology
  • How does a digital twin work?
  • Virtual representation of an object or system that spans its lifecycle
  • Digital twin use cases and applications
  • Advantages and benefits of digital twins
  • Key enabling technologies
  • Systems engineering and System of Systems Engineering (SosE)
  • Key concepts behind Model-based Systems Engineering (MBSE) and digital engineering
  • Modeling and simulation 101
  • Overview of UML, SysML, DoDAF and UAF
  • Data science and data analytics 101
  • AI/ML 101
  • Digital twins vs. simulations
  • Integration of 5G, VR/AR and 3D printing

Types of Digital Twins

  • Capability twin
  • System of Systems (SoS) twin
  • System or Unit twins
  • Subsystem twins
  • Component twins/Parts twins
  • Asset twins
  • Process twins
  • Performance measures
  • Master, the shadow and the twin
  • Related digital twin solutions

Digital Twin Market and Industries

  • Defense
  • Aerospace
  • Space
  • Engineering (systems)
  • Automobile manufacturing
  • Aircraft production
  • Railcar design
  • Building construction
  • Manufacturing
  • Power utilities
  • The future of digital twin

Practical Applications

  • Case studies
  • Workshops
  • Build your own digital twin using Tonex framework

Fundamentals of Digital Twins

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