Digital Twin
Using simulation, machine learning, and reasoning to aid in decision-making, a digital twin is a virtual version of an object or system that spans its lifecycle and is updated from real-time data.
What Is The Process Of A Digital Twin?
A virtual representation created to faithfully represent a physical object is called a digital twin. The object being researched, such as a wind turbine, is equipped with a variety of sensors that are connected to key functioning regions. These sensors generate information about a variety of performance characteristics of the physical device, including energy output, temperature, environmental conditions, and more. The processing system then applies this information to the digital copy.
After receiving this information, the virtual model can be used to run simulations, investigate performance problems, and produce potential enhancements, all with the aim of producing useful insights that can later be applied to the original physical device.
Simulations Versus Digital Twins:
While both digital twins and simulations use digital models to reproduce a system's many operations, a digital twin is truly a virtual world, making it far richer for research. The main distinction between a digital twin and a simulation is scale is:
A digital twin can run as many meaningful simulations as necessary to explore multiple processes, whereas a simulation normally only studies one specific process. There are yet more variances. For instance, real-time data is typically not advantageous for simulations. However, digital twins are built around a two-way information flow that begins when object sensors give the system processor pertinent data, and continues when the processor shares insights with the original source object.
Digital twins are able to study more problems from far more vantage points than standard simulations can because they have better and constantly updated data related to a wide range of fields, combined with the added computing power that comes with a virtual environment, which has a greater potential to improve products and processes in the long run.
Digital Twins' Various Forms (Types)
Digital twins come in a variety of forms depending on how magnified the product is. The field of application is where these twins diverge the most. It is typical for various kinds of digital twins to coexist in a system or process. Let's look over the different sorts of digital twins to see how they differ and are used.
Component twins/Parts twins
The fundamental building block of a digital twin and the simplest illustration of a working component are component twins. Parts twins are essentially the same thing, although they relate to significantly less significant parts.
Asset twins
An asset is created when two or more components operate well together. With asset twins, you can examine how these elements interact, producing a wealth of performance data that can be analyzed and transformed into useful insights.
Unit or System twins
System or unit twins, which let you see how various assets combine to create a whole, functional system, are the next degree of magnification. System twins offer visibility into how assets interact and may make performance suggestions.
Twin processes
The macro level of magnification, called process twins, reveals how systems interact to build a whole manufacturing plant. Are all of those systems synced to run as effectively as possible, or will delays in one system have an impact on others? The specific timing schemes that eventually affect overall efficacy can be found with the use of process twins.
Technology Of Digital Twins: History
With the release of Mirror Worlds by David Gelernter in 1991, the concept of digital twin technology was first presented. But Dr. Michael Grieves, who was then a professor at the University of Michigan, is recognized as having introduced the idea of digital twin software and using it in manufacturing for the first time in 2002. Finally, in 2010, NASA's John Vickers coined a new phrase: "digital twin."
The fundamental concept of using a digital doppelganger to analyze a physical object, however, can be seen far earlier. In fact, it is accurate to say that NASA was the first to use digital twin technology during its space exploration missions in the 1960s, when each travelling spacecraft was precisely replicated in an earthbound version that NASA personnel serving on flight crews used for study and simulation purposes.
The fundamental concept of using a digital doppelganger to analyze a physical object, however, can be seen far earlier. In fact, it is accurate to say that NASA was the first to use digital twin technology during its space exploration missions in the 1960s when each traveling spacecraft was precisely replicated in an earthbound version that NASA personnel serving on flight crews used for study and simulation purposes.
Benefits And Advantages Of Digital Twins:
Improved R&D
Utilizing digital twins produces a wealth of data regarding expected performance results, facilitating more efficient product research and creation. Before beginning production, businesses can use this data to gain insights that will help them make the necessary product improvements.
Higher effectiveness
Digital twins can aid in monitoring and mirroring production systems even after a new product has entered production, with the goal of reaching and maintaining peak efficiency throughout the whole manufacturing process.
Product life-cycle
Digital twins can even assist manufacturers in determining how to handle products that have reached the end of their useful lives and require final processing, such as recycling or other actions. They can decide which product materials can be harvested by utilizing digital twins.
Markets And Industries: Digital Twins
Despite the benefits that digital twins provide, not every company or every product made needs to employ them. Not all objects are intricate enough to require the continuous and intensive influx of sensor data that digital twins demand. Additionally, it is not always advantageous financially to devote a considerable amount of resources to the development of a digital twin. (Remember that a digital twin is an exact clone of a physical object, thus making one might be expensive.) However, using digital models for a variety of applications does have certain distinct advantages:
• Physically substantial projects:
Buildings, bridges, and other intricate structures subject to stringent engineering regulations.
• Mechanically complex project:
Such as jet turbines, and aircraft. Digital twins can contribute to increased productivity in massive engines and intricate machinery.
• Power apparatus:
This covers both the electricity generation and transmission systems.
• Manufacturing initiatives:
Similar to industrial settings with cooperative machine systems, digital twins are excellent at enhancing process efficiency.
Therefore, those sectors that work on large-scale items or projects have the most success with digital twins:
• Engineering (systems)
• automobile production
• aircraft manufacturing
• Railcar design
• building of structures
• Manufacturing
• power utilities
Market For Digital Twins: Poised For Expansion
The market for digital twins is growing quickly, which suggests that even if they are already used in many different industries, demand will persist for a while. The market for digital twins was worth USD 3.1 billion in 2020. It may continue to grow rapidly until at least 2026, rising to a projected USD 48.2 billion1, according to certain industry observers.
Utilizing Digital Twins To Increase Production Productivity
Owner/operators can increase productivity while reducing equipment downtime by using end-to-end digital twins.
Applications
The following applications already make substantial use of digital twins:
Apparatus for generating power
The usage of digital twins is extremely advantageous for large engines, such as jet engines, locomotive engines, and power-generation turbines, especially when determining whether routine maintenance is required.
Systematics and structures
Digital twins can improve massive physical structures, especially during the design phase, such as tall buildings or offshore drilling platforms. Additionally helpful for creating the HVAC systems that run within those structures.
Industrial processes
It is not surprising that digital twins have proliferated in all phases of manufacturing, guiding products from design to finished product, and all steps in between, given that they are intended to mirror a product's entire lifecycle.
Medical services
Patients seeking medical treatments can be profiled using digital twins just like products can. The same kind of sensor-generated data system can be utilized to track different health markers and produce important insights.
Automobile sector
Digital twins are widely utilized in the car industry to improve vehicle performance and production economy. Cars represent a variety of intricate, interconnected systems.
Urban design
The usage of digital twins, which can display 3D and 4D spatial data in real time and also include augmented reality systems into constructed environments, is very helpful to civil engineers and other people involved in urban planning activities.
The Digital Twin: Future
There is little doubt that current operational paradigms are undergoing a major transformation. Asset-intensive businesses are experiencing a disruptive digital reinvention that is redefining operating models and necessitating an integrated physical plus digital view of assets, equipment, buildings, and processes. A crucial component of that realignment is digital twins.
Due to the ongoing allocation of more and more cognitive resources to their utilization, the potential of digital twins is almost endless. Digital twins are able to continue to produce the insights required to improve products and streamline operations since they are always acquiring new knowledge and abilities.
Digital Twin: Transforming Asset Operations
As physical assets grow more complicated to build and more software enabled, a new model of asset operations is needed. However, this model will not be entirely feasible without the development of digital asset replicas. Digital twins are these exact copies. Digital twins have a variety of definitions, but the most basic is "a digital representation of a real asset." All of the parts necessary to operate and maintain that asset may be included in this digital duplicate, including failure codes, bills of materials, and 3D computer-aided design (CAD) representations.
Organizations employ digital twins to analyze how their physical assets perform in various scenarios or to track asset performance in real time. Digital twins enable the development of reliable failure models by using data from sensors attached to the physical assets. They aid organizations with comprehending asset criticality, right down to the component pieces of each piece of equipment, and then distributing that knowledge throughout teams. As a result, digital twins play a crucial role in transforming businesses and completing digitization projects.
The many components of the Internet of Things (IoT) will be integrated with existing ISO, Six Sigma, and total quality management (TQM) aligned procedures for assuring reliability, and many firms wish to develop models that show how this will be done. To do this, they must be rigorous in defining the what, when, how, and why of their IoT deployment in particular areas. These choices are consistent with the Uptime® Elements' Internet of Things knowledge domain:
1. Source:
The onboarding of sophisticated equipment will be transformed by the easy accessibility of digital twins. Onboarding is frequently an inefficient, paper-intensive, and human error-prone process. Plans for maintenance must be created when industrial equipment is purchased based on numerous discussions with original equipment manufacturers (OEMs). Stocking is based on presumed part criticality and allotted budgets, and materials lists must be manually prepared and entered into systems.
The procedure is inefficient and can take months to complete because each stage is supervised by a distinct staff. A full oil refinery, for instance, could require five or six months to digitize due to its size and complexity. This presupposes that everything about it, from its structure to its equipment inventory, operations, and failure modes, would be based on physical designs and paper documentation. The time of plant engineers would be entirely consumed by a project like this.
This procedure can be sped up using digital twins, allowing the system to be operational right away. The digital twin uses pre-existing digital copies of the various parts that make up the bigger system, as opposed to slow, manual methods. (The proprietors of the components would have produced these digital assets throughout the development process.)
2. Connect:
According to many in the business, the cost of IoT sensors keeps falling yearly. This is intended to persuade businesses to start utilizing IoT in their operations right away. However, consumers frequently misunderstand the numerous requirements that come with connecting their machinery, business, and sector. Application programming interfaces (APIs) and other development methodologies are also widely available. Additionally, there are a lot of questions that need to be addressed regarding issues like access, information flow, and storage before a company can create a digital nervous system that is both purposeful and truly secure.
Every point of connection creates a potential cyber-attack vector. As a result, every single connection needs to be conceived, developed, and deployed with security in mind. Without a platform that promotes uniformity, this is a lot of work. Secure working environments and communications are necessary for digital twins that are employed in real-time operations with live data as well as in collaborative development scenarios with vendors, operators, and other third parties.
Otherwise, it will be impossible to maintain the integrity of the digital twins and the data they contain. Organizations can get closer to a fully functional operating twin that replicates the experiences of the actual assets by connecting with asset sensors. However, it requires that the system be supported and secured given that it is shared and changed by numerous people.
3. Gather:
According to Gartner, by 2020 there will be 20.4 billion IoT devices, producing more than 500 zettabytes of data annually. Without a question, businesses are having trouble deciding what data to gather, where to store it, and how to make it useful. With the help of digital twins, IoT data may be organized logically and used in many ways. Enterprises can manage their data requirements by shaping the data set depending on physical, digital, and electromechanical aspects.
Digital twins are managed by operational and exception procedures like asset updates and adaptations, anomaly detection, and exception handling. They reduce the amount of operating data that must be gathered, cleaned, stored, reused, and taken action on by filtering the enormous volumes of data that travel through the company. By definition, the digital twin includes this phase of data gathering, gathering all the information it is aware of regarding the physical asset.
4. Analyze:
As was indicated in the section on data collection, data analysis is more efficient the more data there are. However, it is less effective since it requires managing, combining, and cleaning up large amounts of data to produce data sets that analysts and data scientists can use. Teams can compartmentalize their work, formulate hypotheses, and present results to non-technical or non-analytical peers by using digital twins to categorize, visualize, and contextualize data.
It is simpler to anticipate failure, flow, and feasibility with the addition of statistical analysis tools and models that carry out a multitude of calculations on a rich model with various properties. While digital twins allow physics to direct the analysis in a pseudo-realistic sense, modelling tools that simulate a problem or improvement in a future state are already prevalent in the virtual reality arena.
Therefore, before making significant investments or reducing existing investments, entire manufacturing lines, factories, vehicles, and systems can be examined. This brings up the advantage artificial intelligence (AI) can have for enhancing the use of physical assets. The data that is gathered and put into the AI models to find important insights is stored in the digital twin.
5. Do:
A digital twin, when created properly, enables owners and operators to simulate and what-if-analyze their physical assets. Remote and autonomous operation are made possible by IoT-enabled physical assets that take commands to actuate on the asset. The asset of the future is created by fusing the IoT with AI for anomaly detection and prediction. The goal of the digital twin is to make smart decisions possible, promote continuing industry transformation, and add value to both physical and digital operations.
The digital twin will address many problems, but you can't realize its potential unless you digitize your physical assets and their component parts. Together, you must create guidelines for the organization of digital twin resources in order to establish uniformity across consumption platforms. Making wise decisions feasible, encouraging ongoing industry transformation, and enhancing both physical and digital operations are the objectives of the digital twin. The digital twin will solve many issues, but you can't fully utilize it until you digitize your physical assets and the elements that make them up.
To ensure consistency across consumption platforms, you must set rules for the organization of digital twin resources. Despite the difficulties, there is no denying that digital assets are increasingly becoming equally as important as physical ones, even in sectors that depend heavily on physical assets (such as energy and utilities, oil and petroleum, manufacturing, and industrial products). A ground-breaking method for ensuring that businesses run effectively and efficiently while continuing to provide value to their customers is the use of digital twins.