A Study on Digital Twin Technology in Engineering Systems
DOI:
https://doi.org/10.62431/mdy21990Keywords:
Digital Twin Technology, Defense engineering, Predictive maintenance, Scenario Analysis, Embedded systemsAbstract
Digital Twin Technology has emerged as a transformative innovation in modern engineering systems, enabling the creation of dynamic, real-time virtual replicas of physical assets, processes, and complex systems. Unlike traditional simulation models, digital twins continuously evolve by integrating real-time data acquired from sensors, embedded systems, and connected devices. This real-time synchronization allows engineers to monitor system behavior, predict performance outcomes, and implement data-driven decisions with greater accuracy and efficiency. As engineering systems become increasingly complex and interconnected, digital twins provide a powerful framework for bridging the gap between physical and digital environments. This study presents a comprehensive analysis of digital twin technology, focusing on its fundamental concepts, system architecture, enabling technologies, and diverse applications across multiple engineering domains. The architecture of digital twin systems is examined in terms of data acquisition, data processing, modeling and simulation, and visualization layers, highlighting how each component contributes to the overall functionality of the system. The research emphasizes the role of key enabling technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, cloud computing, and big data analytics in enhancing the capabilities of digital twins. These technologies collectively enable real time data integration, predictive analytics, and intelligent automation, making digital twins a critical tool for modern engineering practices. Furthermore, the study explores practical applications of digital twin technology in industries such as manufacturing, where it is used for process optimization and quality control; aerospace, where it supports predictive maintenance and operational safety; healthcare, where it enables personalized treatment and system modeling; and smart infrastructure, where it facilitates efficient urban planning and resource management. The benefits of digital twins are discussed in detail, including improved decision-making, reduced operational costs, enhanced system reliability, and proactive maintenance strategies. The paper concludes by outlining future research directions, including the integration of digital twins with emerging technologies such as edge computing, 5G networks, and autonomous systems. It also highlights the growing importance of digital twin technology in promoting sustainable engineering practices by optimizing resource utilization and reducing environmental impact. Overall, digital twin technology is poised to play a pivotal role in the evolution of engineering systems, driving innovation, efficiency, and resilience in the digital age.
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Copyright (c) 2026 Asim Kiran Dandapat , Deepa Jain
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