This module provides students with a comprehensive understanding of digital twin technology, equipping them with the essential knowledge and skills to explore and develop digital twin systems. Students will learn how virtual models are created and connected to physical systems through data integration, simulation, and analytics. The module offers practical experience in designing and implementing digital twin representations using data-driven methods and established frameworks. It also enables students to critically evaluate applications across sectors such as healthcare, manufacturing, smart cities, and energy systems, while fostering awareness of the ethical, security, and operational considerations related to digital twin development and deployment.
A. Integrate the key principles of digital twins, including data integration, predictive modeling and simulation, and lifecycle management. B. Evaluate the applications of digital twin technologies across diverse industrial sectors such as healthcare, manufacturing, and energy systems. C. Implement data-driven models for predictive analytics in digital twin environments. D. Develop digital twin representations using a digital twin platform.
This module is delivered through a combination of lectures, tutorials, and lab sessions to develop both theoretical knowledge and practical skills. Lectures introduce core concepts and technologies in digital twin engineering, tutorials support discussion and problem-solving, and labs offer hands-on experience with relevant tools and platforms. Independent study is emphasized, with students expected to spend 10–12 hours per week on readings, assignments, and project work. Where applicable, industry involvement enhances learning through applied activities and real-world context.