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Exploring the Performance and Accuracy of Digital Twin Models
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 517 Exploring the Performance and Accuracy of Digital Twin Models Srinivas Timmapuram1, Suchitra Gandu2, Shahela3 Vidit Kumar4 1JRF , CAS DRDO, Hyderabad, Telangana, India 2JRF , CAS DRDO, Hyderabad, Telangana, India 3JRF , CAS DRDO, Hyderabad, Telangana, India 4Scientist D , CAS DRDO, Hyderabad, Telangana, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Digital twin technology has emerged as a promising approach to model and simulate real-world systems in various industries, including manufacturing, energy, and healthcare.However, the accuracy and performance of digital twin models are critical factors that determine their effectiveness in practical applications. This paper reviews the factors that affect the accuracy and performance of digital twin models, including data quality, model complexity, model validation, system changes, and integration with other systems. The paper highlights the challenges and opportunities associated with digital twin technology. Key Words: Digital twins, artificial intelligence, IoT, predictive analytics, anomaly detection, optimization, productivity, cost-effectiveness, sustainability, manufacturing, healthcare, transportation. 1. INTRODUCTION The creation of a virtual duplicate of a physical system or asset is known as a "digital twin" and is a cutting-edge technology. It can be used for a variety of applications, including monitoring, control, optimisation, and preventive maintenance. This virtual replica, or digital twin, is a highly exact and detailed digital version of the real system. Concept of digital twins has been around for several years, recent advancements in AI have enabled the development of AI-powered digital twins, which have even greater capabilities than their traditional counterparts. AI- powered digital twins can learn from data, identify patterns, and make predictions, enabling manufacturers to predict and prevent issues before they occur. Fig -1: Representation of Digital Twin There are several different digital twin types, which can often run side by side within the same system. While some digital twins replicate only single parts of an object, they're all critical in providing a virtual representation. The most common types of digital twins are the following. Fig -2: Types of Digital twin Component twins: A component twin, also known as a parts twin, is a digital representation of a single system component. These are necessary components for an asset's operation, such as a wind turbine's motor.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 518 Asset twins: Assets are described in terms of digital twins as two or more parts that cooperate to form a larger system. They simulate how the parts interact and generate performance data you may use to make wise decisions. System twins: These represent a greater level of abstraction than asset twins. A system twin demonstrates how several assets function as a unit inside a larger system. You can decide how to improve performance or increase efficiency thanks to the visibility provided by system twin technology. Process twins: The use of process twins can help to understand how an object's many parts, assets, and units interact with one another by displaying the digital environment of the entire entity. The operation of our complete manufacturing facility, for instance, might be digitally replicated by creating a "digital process twin" that combines all of its parts. 2. LITERATURE SURVEY NASA (National Aeronautics & Space Administration) was the first organization to propose the idea of utilizing a digital twin to examine a real object in the 1960s. In order to emulate the systems in space for exploratory missions, NASA reproduced its spacecraft on Earth. The Apollo 13 mission served as a notable example of this technology in action. Mission Control was able to rapidly change the simulations to fit the conditions of the damaged spacecraft and troubleshoot methods to return the astronauts safely to Earth because of the connected twins. To monitor massive facilities like power plants in the early 1970s, mainframe computers were utilized as systems to digital twins. In the 1980s, 2D CAD systems like AutoCAD were developed to create technical drawings, making it feasible to design anything using a computer. millions of designers and engineers soon embraced these systems. By the 2000s, parametric modeling and simulation enabled by 3D CAD allowed for the intelligent design of more complex assemblies, such as a database of connected objects. Time travel to the middle of the 2010s, when all major 3D CAD suppliers introduced cloud-connected solutions, primarily for collaboration and project management, and subsequently for generative design, even though CAD tools remained desktop-based. Today it is the beginning of the era of real-time 3D powered digital twins, which go beyond dashboards and 3D models to unlock data from various sources on any device or platform for better collaboration, visualization, and decision-making. 3. APPLICATIONS Digital twin technology has many potential applications across a wide range of industries. Some of the most notable applications of digital twin include: Manufacturing: Production process optimisation, downtime reduction, and product quality improvement are all possible with the usage of digital twins. Businesses may predict possible issues before they arise and immediately put remedies into action by simulating the manufacturing process. Aerospace and defense: Digital twins can be used to simulate the performance of aircraft, spacecraft, and other vehicles. This technology can help improve safety, reduce maintenance costs, and optimize performance. Fig -3: Applications of Digital twin Energy and utilities: Digital twins can be used to monitor and optimize energy usage, reduce waste, and improve efficiency. This technology can be used in power plants, oil and gas refineries, and other energy-intensive industries. Healthcare: Digital twins can be used to simulate the human body, allowing doctors to diagnose and treat patients more effectively. This technology can also be used to monitor patient health remotely and predict potential health issues before they occur. Smart cities: Digital twins can be used to simulate and optimize city infrastructure, including transportation systems, utilities, and public services. This technology can help cities become more sustainable, efficient, and responsive to the needs of residents. Overall, digital twin technology has the potential to revolutionize many industries by providing accurate, real-
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 519 time simulations that can be used to optimize performance, reduce costs, and improve safety. New concept called AI powered Digital twin can also be used in above mentioned application where AI powered digital twin can analyze the available data, identify the patterns, make predictions, enabling manufacturers to predict and prevent issues before they occur. 4. ADVANTAGES Users can profit greatly from digital twins. We'll discuss a few of these now. a) A customized response to each need: The beauty of digital twinning is that it enables businesses to create virtual, tailored versions of their web infrastructure to do predictive analysis that is appropriate for their purposes. It is a customized method of adjusting security to certain businesses. b) Higher performance: We can improve the performance of an equipment, plant, or facilities using the real-time data and insights supplied by digital twins. As problems arise, they can be resolved quickly to maintain system performance and minimize downtime. c) Predictive capabilities: Even if our manufacturing plant, office building, or facility contains hundreds of different pieces of equipment, digital twins can nevertheless provide us with a comprehensive visual and digital image of everything. Every component's output is monitored by sophisticated sensors, which immediately alert the user to any problems or errors. As opposed to waiting until a piece of equipment totally fails, we can intervene as soon as issues arise. d) Remote monitoring: Digital twins virtual nature allows for remote facility monitoring and management. Additionally, remote monitoring eliminates the need for personnel to inspect industrial machinery that might be hazardous. e) Accelerated production time: By creating digital duplicates, we can hasten the manufacturing of goods and infrastructure before they are actually created. By executing scenarios, we may examine how our facility or product responds to errors and make the necessary adjustments prior to beginning actual production. AI-powered digital twins will continue to play a key role in this new phase of manufacturing. Here are some potential benefits of AI-powered digital twins in manufacturing industry: i.Greater collaboration between humans and machines: In the manufacturing industry, humans and machines will work together in a highly collaborative and integrated manner. AI-powered digital twins can enable humans to interact with machines in a more natural and intuitive way, improving collaboration and productivity. ii.Improved customization and personalization: In the manufacturing industry, there will be a greater focus on customization and personalization of products. AI- powered digital twins can be used to simulate and optimize the production of customized products, resulting in higher quality and greater customer satisfaction. iii.Greater flexibility and agility: manufacturing industry will require greater flexibility and agility in manufacturing processes, enabling rapid response to changing customer demands and market conditions. AI-powered digital twins can be used to simulate and optimize manufacturing processes in real-time, enabling greater flexibility and agility. iv.Enhanced sustainability: manufacturing industry will require a greater focus on sustainability and environmental responsibility. AI-powered digital twins can be used to optimize resource utilization and reduce waste and energy consumption, resulting in greater sustainability and environmental benefits. v.Improved safety: manufacturing industry will require a greater focus on safety, as humans and machines work more closely together. AI-powered digital twins can be used to simulate and test safety scenarios, enabling manufacturers to identify and mitigate potential safety hazards before they occur. Overall, the use of AI-powered digital twins in the manufacturing industry has the potential to enable greater collaboration, customization, flexibility, sustainability, and safety in Manufacturing processes. 5. DIGITAL TWIN CREATION To generate digital twins, conceptual models are imported (through Building Information Modeling, Computer aided design, or Geographic information system) or physical objects are scanned in the real world for visualization and analysis with enterprise and Internet of Things (IoT) data. Real-time 3D, a computer graphics technology that creates interactive content more quickly than human perception,
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 520 can be used to power a digital twin that can filter, arrange, and present various data sources (both information and models) as realistic, interactive visualizations. Digital twins are representations in the virtual realm of the forces, interactions, and movements that assets may experience in the real world. Users can interact with dynamic content that is three-dimensional and responsive to their actions in real-time. They may efficiently mimic real-world situations, what-if scenarios, and any situation imaginable in this virtual environment, and instantaneously visualize the results on any platform, including mobile devices, PCs, and augmented, mixed, and virtual reality (AR/MR/VR) devices. The deployment of each digital twin is distinct. The complexity and business effect of deployments often increase with each stage that is added. With each of its components dynamically linked to engineering, construction, operational data, and virtual design, a digital twin can range from a product configurator of a 3D model to an exact representation of a network or system as wide as a metropolis. Digital twins guide decision-making at every stage of the life cycle as teams from several disciplines and locations plan, engineer, build, sell, and eventually run and manage complex builds. Although both digital twins and simulations are virtual model-based simulations, they have some significant differences. Typically, simulations are used for design and, in some circumstances, offline optimisation. On the other hand, digital twins are intricate virtual worlds that can engage with and change in real time. Let's take an automobile simulation as an illustration. A novice driver can engage in an immersive training session, understand how various automotive components work, and experience numerous real-world circumstances while driving virtually. The circumstances aren't related to a real car, though. A digital replica of the car is connected to the real thing and is fully informed about it, including important performance data, previously replaced parts, prospective problems as detected by the sensors, prior service history, and more. 6. PERFORMANCE, ACCURACY AND CHALLENGES OF DIGITAL TWIN The fidelity and accuracy of digital twin models are critical factors in determining the effectiveness of digital twin technology in representing and simulating physical systems. Fidelity refers to the level of detail and accuracy with which the digital twin model represents the physical system. Accuracy refers to the degree to which the digital twin model can predict the behavior and performance of the physical system under different conditions. A simulation-based study can be an effective way to evaluate the fidelity and accuracy of digital twin models. By simulating the behavior and performance of the digital twin model under different conditions, the accuracy of the model can be verified and any discrepancies can be addressed. The accuracy and performance of digital twin models can be impacted by several factors, some of which include: Data quality: Digital twins rely on accurate and high- quality data to model and simulate the real-world system they represent. If the data used to build the model is incomplete, outdated, or unreliable, it can lead to inaccurate predictions and poor performance. Obtaining real world data of every system is not feasible hence digital twins for all real world systems is challenging. Model complexity: Digital twins can be complex models that incorporate a large number of variables and require significant computational resources to run. As the complexity of the model increases, the accuracy and performance can be impacted, especially if the computational resources are insufficient. hence digital twin for complex real world systems is a challenge. Model validation: Digital twin models must be validated against real-world data to ensure they are accurate and reliable. If the model validation process is inadequate or the validation data is insufficient, it can lead to inaccuracies and poor performance. Getting Accurate validation data for real world systems is challenging. System changes: Digital twins are designed to simulate real-world systems, but these systems are constantly changing. If the digital twin model does not account for these changes or is not updated regularly, it can lead to inaccuracies and poor performance. Integration with other systems: Digital twins are often integrated with other systems such as sensors, data analytics platforms, and control systems. If the integration is not seamless or the data exchanged between systems is not accurate, it can impact the accuracy and performance of the digital twin model. Cost: Developing and implementing digital twin technology can be costly, especially for complex systems. The cost of obtaining and managing data, building and validating the model, and integrating the model with other systems can all add up.
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 521 Security: Digital twins can contain sensitive data about the real-world system they represent, making them a potential target for cyber-attacks. Ensuring the security of the digital twin system can be a significant challenge. Overall, ensuring the accuracy and performance of digital twin models requires careful consideration of these and other factors, challenges as well as ongoing validation and updating of the model as the real-world system evolves. 7. RESULT The accuracy and performance of digital twin models depend on several factors such as the quality of the data used, the complexity of the model, the validation process, system changes, and integration with other systems. To ensure the accuracy and performance of digital twin models, it is essential to carefully manage the data used in the model and to ensure that the model is validated against real-world data. The complexity of the model must also be carefully managed to ensure that it is accurate and reliable while being computationally feasible. The digital twin model must also be updated regularly to account for system changes and to integrate seamlessly with other systems. Addressing these challenges requires careful consideration of the specific system being modeled, the data and computational resources available, and the organizational context in which the digital twin is being developed and implemented. Ultimately, ensuring the accuracy and performance of digital twin models is crucial to their successful adoption and use in various industries and applications. 8. CONCLUSION In conclusion, Digital twin uses several advanced technologies to provide a digital model of an asset. These technologies are Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to enhance productivity, quality, and efficiency across various sectors, including manufacturing, healthcare, and transportation. To ensure the accuracy and performance of digital twin models, it is essential to carefully manage the data used in the model and to ensure that the model is validated against real-world data. The digital twin model must also be updated regularly to account for system changes and to integrate seamlessly with other systems. 9. FUTURE WORK The future scope of "Exploring the Performance and Accuracy of Digital Twin Models" is vast, as the use of digital twin models is expected to grow rapidly in various industries. Future work will include exploration of following: a) Development of techniques for data collection and analysis, such as the use of Internet of Things (IoT) sensors and edge computing. b) Exploration of open-source digital twin platforms for developing digital twin model and application and validation of it. Overall, the future of "Exploring the Performance and Accuracy of Digital Twin Models" is promising, with continued research and development expected to lead to significant advancements in the field. ACKNOWLEDGEMENT For his continued cooperation with the research for drafting this study at CAS, Sri BV Papa Rao, Outstanding Scientist and Director, is gratefully acknowledged by the authors. The authors thank Associate Director Sri Praveen Tandon Sc G for his unwavering support. The authors are extremely grateful to all CAS staff members for their selfless contributions of time and knowledge to this study in a variety of ways. REFERENCES [1] https://www.analyticsinsight.net/different- applications-of-digital-twin-in-different-industries-in- industry-4-0/ [2] https://www2.deloitte.com/us/en/insights/focus/in dustry-4-0/digital-twin-technology-smart-factory.html [3] https://www.wizata.com/knowledge- base/applications-and-benefits-of-digital-twin-for- industry-4.0 [4] https://www.xenonstack.com/insights/digital-twin- in-industry [5] https://www.plataine.com/blog/digital-twins-in- manufacturing-5-advantages-that-the-industry-pros- must-know/ [6] https://www.nokia.com/thought- leadership/articles/industry-4-0/network-architecture/ [7] https://www.architecture-design-industry4-0.org/ [8] https://www.techscience.com/cmc/v66n2/40642/ht ml [9] https://www.i-scoop.eu/industry-4-0/
6.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 522 [10]https://softengi.com/blog/use-cases-and- applications-of-digital-twin/ BIOGRAPHIES [1] Shri. Vidit Kumar, is presently working as a Scientist- D at CAS DRDO Hyderabad. he has more than 10 years experience in the field of communication, computer networking & software development. [2] Sri Timmapuram Srinivas is presently working as JRF in DRDO, CAS, Hyderabad. He has more than 6 years of experience in the field of embedded systems, IT networking. He has published a research paper in International Journal of Research on IoT based embedded smart lock control systems. [3] Smt G.Suchitra is presently working at DRDO, CAS, Hyderabad as a JRF. She completed her M.Tech in Computer Science & Engineering from Osmania University. She has 2 years of work experience in the field of computer science. She is certified as CISSP-Certified Information Systems Security Professional. [4] Smt Shahela is presently working as a JRF at DRDO,CAS,Hyderabad.She completed her M.Tech in VLSI.She has 1.5 years of work experience in the field of IT & Cybersecurity. She has published a paper on Quantum Circuitry in International Journal & Magazine of Engineering, Technology, Management and Research. She is certified as a ISO 27001 lead auditor.
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