Digital Twin: A Dynamic System and Computing Perspective

Digital twin technology is a simulation-based approach that creates a virtual representation of a physical system or process. It involves using computational models to replicate the behavior and performance of the system, allowing for analysis and prediction of its behavior. The digital twin can be represented using physics-based models or surrogate models, and can address the problem of uncertainty in measurements and modeling. Practical case studies of digital twin implementation include additive manufacturing, server farms, predictive maintenance, and smart cities.

Format: Hardback
Length: 240 pages
Publication date: 17 April 2023
Publisher: Taylor & Francis Ltd

Digital twin technology is a cutting-edge concept that has the potential to revolutionize various industries by enabling the creation of virtual replicas of physical systems. These digital twins are computer simulations that mimic the behavior and characteristics of their real-world counterparts, providing valuable insights and enabling efficient decision-making.

At its core, digital twin technology involves the use of advanced computational techniques to create detailed models of physical systems. These models can encompass a wide range of aspects, including their structure, components, and operating conditions. By leveraging these models, digital twins can simulate the behavior of these systems in real-time, allowing for the analysis of their performance and the identification of potential issues before they occur.

One of the key advantages of digital twin technology is its ability to provide a comprehensive understanding of complex systems. By capturing the intricate details of a system, digital twins can help engineers and scientists analyze and optimize the system's design, reducing costs and improving performance. For example, in the manufacturing industry, digital twins can be used to simulate the production process, allowing manufacturers to identify bottlenecks and optimize production schedules.

In addition to its computational capabilities, digital twin technology also incorporates physics-based and surrogate model representations. Physics-based models are highly accurate and can capture the complex interactions between different components of a system. However, they can be time-consuming and expensive to develop and maintain. Surrogate models, on the other hand, are simpler and faster to develop, but may not provide the same level of accuracy as physics-based models.

Despite its many benefits, digital twin technology faces several challenges. One of the most significant challenges is the problem of uncertainty in measurements and modeling. Physical systems are inherently unpredictable, and the accuracy of digital twin models can be affected by factors such as sensor errors, environmental conditions, and human intervention. To address this challenge, digital twin technology can be integrated with sensors and other monitoring devices to collect real-time data and improve the accuracy of the models.

Another challenge is the scalability of digital twin technology. As systems become increasingly complex, the computational resources required to create and maintain digital twins can become prohibitively expensive. To address this challenge, researchers are exploring the use of cloud computing and other distributed computing technologies to enable the creation and maintenance of large-scale digital twins.

In conclusion, digital twin technology is a powerful tool that has the potential to transform various industries by enabling the creation of virtual replicas of physical systems. By providing valuable insights and enabling efficient decision-making, digital twins can help engineers and scientists optimize complex systems, reduce costs, and improve performance. However, the technology faces several challenges, including uncertainty in measurements and modeling and scalability. By addressing these challenges, digital twin technology will continue to evolve and play an increasingly important role in the future of industry.

Dimension: 234 x 156 (mm)
ISBN-13: 9781032213620