Name: Digital Twins (Master)

Size: 10 students (with 10 different topics)

Workload:

• 2 Lectures
• One presentation delivered + attendance of the other students' presentations
• One report

Responsible Persons: Michelle Jungmann, Sanja Lazarova-Molnar

Deliverables for Grade:

• 1 report per student and topic (7-8 pages, IEEE Template, usage of Reference Manager – Zotero or EndNote)
• 25 min presentation per student plus 20 min discussion (focus on the presentation topic + presentation skills) = 45 minutes for each student

Credits: 3 credits

Format/ Structure of the Seminar:

• 2 lectures on beginning of semester 
• Students have 1 week time to provide a priority list of 5 presentation topics, distribution will be decided based on first come – first serve, ensuring that core topics are covered
• Students have time to work on the report and presentation during the semester
• Submission of all reports will be required 2 months after the intro lecture
• Presentations are done in blocks of 2 students per class, starting mid-June, presentations will be submitted at the day of the scheduled presentation

Description:

The seminar focuses on Digital Twins and data-driven modeling, with an additional goal of improving scientific research and presentation skills for Master students. The seminar targets different topics around the structure and function of Digital Twins as well as their use cases in areas like manufacturing, energy systems, healthcare and others. Additional aspects that we consider in this seminar are cognitive Digital Twins, as well as how data and human expertise can be combined in Digital Twins.

The seminar is structured as a literature review seminar so that each student can select a topic out of a predefined set. The student then writes a paper, as well as delivers a presentation on that topic, based on the provided starting literature and additional research.

Topics:

1. What is a Digital Twin? (core topic)

References:

• Fuller, Aidan, et al. "Digital twin: Enabling technologies, challenges and open research." IEEE access 8 (2020): 108952-108971.
• Tao, Fei, et al. "Digital twin in industry: State-of-the-art." IEEE Transactions on industrial informatics 15.4 (2018): 2405-2415.
• Mihai, Stefan, et al. "Digital twins: A survey on enabling technologies, challenges, trends and future prospects." IEEE Communications Surveys & Tutorials (2022).

2. Digital Twins Architectures (core topic)

References:

• Ashtari Talkhestani, Behrang, et al. "An architecture of an intelligent digital twin in a cyber-physical production system." at-Automatisierungstechnik 67.9 (2019): 762-782.
• Harper, K. Eric, Somayeh Malakuti, and Christopher Ganz. "Digital twin architecture and standards." (2019).
• Minerva, Roberto, Gyu Myoung Lee, and Noel Crespi. "Digital twin in the IoT context: A survey on technical features, scenarios, and architectural models." Proceedings of the IEEE 108.10 (2020): 1785-1824.

3. Validation of Digital Twins (core topic)

References:

• Worden, K., et al. "On digital twins, mirrors, and virtualizations: Frameworks for model verification and validation." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 6.3 (2020): 030902.
• Hua, Edward Y., Sanja Lazarova-Molnar, and Deena P. Francis. "Validation of Digital Twins: Challenges and Opportunities." 2022 Winter Simulation Conference (WSC). IEEE, 2022.
• Darvishi, Hossein, Domenico Ciuonzo, and Pierluigi Salvo Rossi. "Exploring a modular architecture for sensor validation in digital twins." 2022 IEEE Sensors. IEEE, 2022.

4. Modeling Formalisms for Digital Twins (core topic)

References:

• Magargle, Ryan, et al. "A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System." Modelica. 2017.
• Liu, Qing, et al. "A comparative study on digital twin models." AIP Conference Proceedings. Vol. 2073. No. 1. AIP Publishing, 2019.
• Li, Haobin, et al. "Three Carriages Driving the Development of Intelligent Digital Twins-Simulation Plus Optimization and Learning." 2021 Winter Simulation Conference (WSC). IEEE, 2021.

5. Digital Twins Data Requirements (core topic)

References:

• Durão, Luiz Fernando CS, et al. "Digital twin requirements in the context of industry 4.0." Product Lifecycle Management to Support Industry 4.0: 15th IFIP WG 5.1 International Conference, PLM 2018, Turin, Italy, July 2-4, 2018, Proceedings 15. Springer International Publishing, 2018.
• Qi, Qinglin, and Fei Tao. "Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison." Ieee Access 6 (2018): 3585-3593.

6. Digital Twins for Manufacturing Systems

References:

• Zhang, Chenyuan, et al. "A reconfigurable modeling approach for digital twin-based manufacturing system." Procedia Cirp 83 (2019): 118-125.
• Kritzinger, Werner, et al. "Digital Twin in manufacturing: A categorical literature review and classification." Ifac-PapersOnline 51.11 (2018): 1016-1022.
• Jaensch, Florian, et al. "Digital twins of manufacturing systems as a base for machine learning." 2018 25th International conference on mechatronics and machine vision in practice (M2VIP). IEEE, 2018.

7. Digital Twins for Energy Systems

References:

• Steindl, Gernot, et al. "Generic digital twin architecture for industrial energy systems." Applied Sciences 10.24 (2020): 8903.
• Granacher, Julia, et al. "Overcoming decision paralysis—A digital twin for decision making in energy system design." Applied Energy 306 (2022): 117954.
• Palensky, Peter, et al. "Digital twins and their use in future power systems." Digital Twin 1 (2022): 4.

8. Digital Twins in Healthcare

References:

• Alazab, Mamoun, et al. "Digital twins for healthcare 4.0-recent advances, architecture, and open challenges." IEEE Consumer Electronics Magazine (2022).
• Croatti, Angelo, et al. "On the integration of agents and digital twins in healthcare." Journal of Medical Systems 44 (2020): 1-8.
• Erol, Tolga, Arif Furkan Mendi, and Dilara Doğan. "The digital twin revolution in healthcare." 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT). IEEE, 2020.

9. Digital Twins of City Infrastructures (in Smart Cities)

References:

• Deren, Li, Yu Wenbo, and Shao Zhenfeng. "Smart city based on digital twins." Computational Urban Science 1 (2021): 1-11.
• Deng, Tianhu, Keren Zhang, and Zuo-Jun Max Shen. "A systematic review of a digital twin city: A new pattern of urban governance toward smart cities." Journal of Management Science and Engineering 6.2 (2021): 125-134.
• Mylonas, Georgios, et al. "Digital twins from smart manufacturing to smart cities: A survey." Ieee Access 9 (2021): 143222-143249.

10. Digital Twins in Logistics

References:

• Moshood, Taofeeq D., et al. "Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics." Applied System Innovation 4.2 (2021): 29.
• Agalianos, K., et al. "Discrete event simulation and digital twins: review and challenges for logistics." Procedia Manufacturing 51 (2020): 1636-1641.
• Korth, Benjamin, Christian Schwede, and Markus Zajac. "Simulation-ready digital twin for realtime management of logistics systems." 2018 IEEE international conference on big data (big data). IEEE, 2018.

11. Cognitive Digital Twins

References:

• Al Faruque, Mohammad Abdullah, et al. "Cognitive digital twin for manufacturing systems." 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2021.
• Zhang, Nan, Rami Bahsoon, and Georgios Theodoropoulos. "Towards engineering cognitive digital twins with self-awareness." 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020.
• Zheng, Xiaochen, Jinzhi Lu, and Dimitris Kiritsis. "The emergence of cognitive digital twin: vision, challenges and opportunities." International Journal of Production Research 60.24 (2022): 7610-7632.

12. Fusing Data and Human Expert Knowledge in Digital Twins

References:

• Kulkarni, Vinay, Souvik Barat, and Tony Clark. "Towards adaptive enterprises using digital twins." 2019 winter simulation conference (WSC). IEEE, 2019.
• Vogel-Heuser, Birgit, et al. "Potential for combining semantics and data analysis in the context of digital twins." Philosophical Transactions of the Royal Society A 379.2207 (2021): 20200368.
• Todorovski, Ljupčo, and Sašo Džeroski. "Integrating knowledge-driven and data-driven approaches to modeling." ecological modelling 194.1-3 (2006): 3-13.