Digital Twin: Helping Sustainability Goals and Smart Rail Operations Within Reach  

The fast-increasing digital twin industry suggests that demand for digital twins will continue to rise for some time, taking the Digital twins market to USD 73.5 billion by 2027.

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A digital twin is an artificially generated and virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and assists in decision-making through simulation, machine learning, and reasoning.

The working

A digital twin is a virtual model designed and developed to replicate a physical object and phenomenon precisely. The device under investigation, such as a wind turbine, comes equipped with different sensors relevant to vital and critical areas of functionality. These sensors generate information regarding multiple facets of a physical object’s performance, such as energy output, temperature, weather conditions, etc. This information, after that, is transmitted to a processing system and applied to the digital copy. Once such data is readily accessible, the virtual model may be used to run simulations, explore and examine issues with performance, and generate possible and conceivable modifications, all to develop valuable and significant conclusions that may be brought back to the original physical device.

Digital twins and simulations

Although both simulations and digital twins use digital models to simulate and replicate a system’s different functions and processes, a digital twin is truly a virtual world, making it far more prosperous and more explorative for study and analysis. The primary distinction between a digital twin and a simulation is one of scale: While a simulation typically examines a single process, a digital twin may perform numerous meaningful simulations to explore multiple procedures. The distinctions do not end there. For example, simulations rarely benefit from real-time data. However, digital twins are built on a two-way information flow that commences when object sensors offer and deliver relevant data to the system processor and continues when insights generated by the processor are exchanged back with the source object. Digital twins can study more issues from far more vantage points than standard simulations because they have better and constantly updated data related to a wide range of areas, combined with the added computing power of a virtual environment.

Types of digital twins

Various types of digital twins exist depending on the extent of product magnification. The primary distinction between these twins is their field of application. It is common and typical for multiple kinds of digital twins to coexist within a system or process.

  1. Component twins/Parts twins: Component twins are the fundamental unit of a digital twin, representing the minor example of a working component. Parts twins are roughly the same, except they refer to significantly fewer essential components.
  2. Asset twins: When two or more components work and function together, they generate and develop what is known as an asset. Asset twins allow for investigation of the interaction of those components, leading to the creation of a wealth of performance data that can be evaluated and transformed into meaningful insights.
  3. System or Unit twins: The next degree of magnification involves system or unit twins, which allows us to understand better how various assets interact to build a fully functional system. System twins provide visibility into asset interactions and may identify performance improvements.
  4. Process twins: Process twins, the macro level of magnification, demonstrate how systems interact and work together to generate an entire facility for manufacturing and production.  Process twins can assist in determining the specific timing schemes that influence overall effectiveness, whether all of those systems are synchronised to run at peak efficiency, or will delays in one system will impact others.

History of Digital Twin Technology

The concept of digital twin technology was initially put forward in 1991 with the introduction and publication of David Gelernter’s Mirror Worlds. Dr. Michael Grieves (then on the faculty at the University of Michigan) is credited for introducing the notion of digital twins to manufacturing for the first time in 2002 and formally announcing the digital twin software concept. In 2010, NASA’s John Vickers coined the phrase; digital twin.’ However, the fundamental concept of using a digital twin to study and examine a physical thing can be witnessed much earlier. NASA can claim to have pioneered digital twin technology during its space exploration missions of the 1960s when each voyaging spacecraft was precisely replicated in an earthbound version that NASA personnel serving on flight crews used for study and simulation.

Advantages and benefits of digital twins

  • Better R&D: Using digital twins allows for more effective product research and creation, with a wealth of data generated concerning expected performance outcomes. This data can lead to insights that can help businesses make necessary product improvements before going into production.
  • Greater efficiency: Even after a new product goes into production, digital twins can assist in mirroring and monitoring production systems to achieve and maintain optimal efficiency throughout manufacturing.
  • Product end-of-life: Digital twins can even assist producers in determining what to do with products that have reached the end of their product lifecycle and require final processing, such as recycling or other measures. They can use digital twins to decide which product materials can be harvested and assembled.

While digital twins are valuable for what they provide, their utilisation is only appropriate for some manufacturers or products. Only some objects are complicated enough to need the constant and intensive flow of sensor data required by digital twins. Investing significant resources in producing a digital twin is sometimes only financially worthwhile. (It is important to note that a digital twin is an exact reproduction of a physical thing, which may need a high cost of production.)

On the other hand, many other types of projects benefit significantly from the use of digital models:

  • Buildings, bridges, and other complicated constructions and structures must adhere to rigid engineering requirements.
  • Mechanically complicated projects, Automobiles, jet turbines, and aircraft. Digital twins can help enhance efficiency in complex machinery and massive engines.
  • Electrical and power equipment. This comprises both power generation and transmission mechanisms.
  • Projects involving manufacturing. Digital twins excel in streamlining process efficiency, as seen and witnessed in industrial settings with co-functioning machine systems.

As a result, the industries that benefit the most from digital twins are those that deal with large-scale products or projects:

  • Engineering (systems)
  • Automobile manufacturing
  • Aircraft production
  • Railcar design
  • Building Construction
  • Manufacturing
  • Power utilities

Digital twin market: Poised for growth

While digital twins are currently in use across many industries, the fast-increasing digital twin industry suggests that demand for digital twins will continue to rise for some time. The global digital twins market had been projected to reach USD 73.5 billion by 2027 in 2022.


Digital twins are already widely employed in the following areas:

  • Power-generation equipment: Large engines, such as jet engines, locomotive engines, and power-generation turbines, benefit significantly from using digital twins, particularly in establishing schedules for routine maintenance.
  • Structures and their systems: Large physical structures, such as high-rise buildings or offshore drilling platforms, can benefit from digital twins, especially during the design phase. It is also helpful in the design of systems that operate within those structures, such as HVAC systems.
  • Manufacturing operations: Given that digital twins are intended to mirror a product’s entire lifecycle, it’s no surprise that they’ve become commonplace in all manufacturing stages, guiding things from design to final product and all processes.
  • Healthcare services: Patients receiving services like products can be profiled using digital twins. The same sensor-generated data system can track various health indicators and offer crucial insights.
  • Automotive industry: Cars have a wide range of complicated, co-existing systems, and digital twins are widely employed in car design to optimise vehicle performance and increase production efficiency.
  • Urban planning: Using digital twins, which can display 3D and 4D spatial data in real time and embed augmented reality systems into constructed environments, greatly assists civil engineers and others involved in urban planning operations.

The future of digital twin

Existing operational models are undergoing substantial upheaval. In asset-intensive businesses, a digital revolution is taking place that is transforming operating patterns and necessitating an integrated physical and digital perspective of assets, equipment, facilities, and processes. Digital twins are an essential component of that readjustment. Given that larger quantities of cognitive power are constantly being deployed to their usage, the future of digital twins is almost endless. As a result, digital twins continually acquire new skills and capabilities, allowing them to generate the insights required to improve goods and processes.

Digital Twin in the Railway Sector

In railways, the digital twin includes the construction and development of a digital model that represents the actual assets and activities of the railway system. The digital twin combines data from different sources, including sensors, signalling systems, maintenance records, and historical data, to reflect railway assets’ real-time state and behaviour. The use of digital twins in the railway industry has various advantages. Here are some significant applications for digital twins in railways:

  • Asset Monitoring and Maintenance: Real-time monitoring of train components, tracks, signalling systems, and other infrastructure is possible with digital twins. Predictive maintenance algorithms can detect probable defects or breakdowns in advance by collecting and analysing sensor data. This proactive strategy aids in the optimisation of maintenance schedules, the reduction of downtime, and the enhancement of asset performance.
  • Operations and Simulation: Using digital twins enables railway operators to simulate and optimise train operations. Operators can identify bottlenecks, optimise scheduling, and increase overall system efficiency by simulating the behaviour of trains and railway infrastructure. Digital twins can simulate many scenarios, such as changes in train routes, timetables, or infrastructure upgrades, to analyse the impact on operations.
  • Safety and Security: Digital twins can improve railway safety and security. Anomalies or potential security concerns can be recognised in real-time by analysing data from various sensors and monitoring systems. Digital twins also make testing and validating railway safety protocols, emergency response plans, and training simulations easier.
  • Passenger Experience: Using digital twins can help improve the entire passenger experience. Operators can optimise seating arrangements, estimate crowd density, and provide real-time information to passengers regarding delays or disturbances by integrating data from multiple sources, such as ticketing systems, passenger flow sensors, and train schedules.
  • Infrastructure Planning and Design: Digital twins can help plan and design railway infrastructure. Engineers and planners can simulate multiple scenarios, assess capacity, optimise layouts, and analyse potential implications on existing infrastructure by generating virtual models of planned tracks, stations, and signalling systems.

The use of digital twins in the railway sector improves operating efficiency and safety, reduces maintenance costs, and improves the overall performance of the rail and train network.


The digital twin continues to provide benefits from design concept to operation. It improves complex design processes by stimulating layouts, configurations, operational circumstances, and risk scenarios. It establishes a living repository for engineering data to spot potential clashes, optimises resources and construction, and enables continuous handover. The platform provides visibility into the design process for all stakeholders, sets deliverable expectations, and ensures seamless handover, reducing onsite activities and improving communication between EPCs and operators or across many EPCs.

By incorporating procurement planning into the design process, digital twins help to reduce lead time and promote transparency by cross-referencing design documentation with procurement and commissioning. It connects the design, procurement, and construction phases for concurrent activity. To identify possible issues, optimise resources, eliminate rework, enhance construction deadlines and schedules, and create virtual reproductions of construction sites. Additionally, Digital Twin employs digital documentation and deliverables to expedite commissioning and ensure a smooth transition to operations. It uses engineering data in operations to improve safety, sustainability, and agility while lowering costs. Maintains EPC partnerships and operates through engineering data upkeep or equipment refinement. Thus, digital twin technology is helping make sustainability goals attainable and within reach.

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