Seminar Name: Applications of Digital Twins

Size: 10 students (with 10 different topics)

Workload: 

    •     2 Lectures:

     o   Introduction to Digital Twins and topic distribution

     o   “How to Give Effective Presentations” lecture

    •     10 student presentations (each 45 minutes in total)

    •     10 student reports

Responsible Person: Hui Min Lee, Sanja Lazarova-Molnar

Deliverables for Grade:

    ·       1 Report per student and topic (8 pages, including references, IEEE Template, compulsory usage of Reference Manager – Zotero or EndNote)

    ·       25 mins presentation per student plus 20 min discussion (focus on the presentation topic + presentation skills) = 45 minutes for each student

Credits: 3 credits = 90 hours 

Format/ Structure of the Seminar (Draft):

    ·       2 Lectures at the beginning of the semester

    ·       Students have 1 week time to provide a priority list of 5 topics, distribution will be decided based on first come – first serve, ensuring that core topics are covered

    ·       Q&As can be asked and answered over mails or ad-hoc appointments

    ·       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 for ensuring fairness

    ·      Presentations are done in blocks of 2 students per class, starting mid-June, presentations will be submitted at the day of the scheduled presentation

Approximate Time Consumption for Students (Draft):

   ·       Lectures: 3 hours

   ·       Student Presentations: 7.5 hours

   ·       Topic Subscription: 1 hour

   ·       Presentation Preparation: 15 hours

   ·       Paper Writing and Literature Review: 63.5 hours

Description:

The seminar focuses on applications of Digital Twins and data-driven modeling, with an additional goal of improving scientific research and presentation skills for Master students. The seminar covers the diverse applications and use cases of Digital Twins in different domains such as manufacturing, energy systems, healthcare and many more, offering students an in-depth understanding of the role of Digital Twins in transforming the industries.

The seminar is structured as a literature review seminar. Each student can select a topic out of a predefined set, conduct further research and then write a comprehensive research paper. Students will also deliver presentations, synthesizing insights from both the provided starting reference literature and their own additional research.

By the end of the course, students will not only have a solid understanding of the current applications of Digital Twins and emerging trends but also be well-prepared to present their findings in an academic setting.

Topics:

1.      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. (96 citations)

·       Kritzinger, Werner, et al. "Digital Twin in manufacturing: A categorical literature review and classification." Ifac-PapersOnline 51.11 (2018): 1016-1022. (1934 citations)

·       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. (73 citations)

2.      Digital Twins for Energy Systems

References:

·       Steindl, Gernot, et al. "Generic digital twin architecture for industrial energy systems." Applied Sciences 10.24 (2020): 8903. (78 citations)

·       Granacher, Julia, et al. "Overcoming decision paralysis—A digital twin for decision making in energy system design." Applied Energy 306 (2022): 117954. (33 citations) -> focus on interactive digital twins

·       Palensky, Peter, et al. "Digital twins and their use in future power systems." Digital Twin 1 (2022): 4. (37 citations)

3.      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). (26 citations)

·       Croatti, Angelo, et al. "On the integration of agents and digital twins in healthcare." Journal of Medical Systems 44 (2020): 1-8. (163 citations)

·       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. (106 citations)

4.      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. (110 citations)

·       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. (192 citations)

·       Mylonas, Georgios, et al. "Digital twins from smart manufacturing to smart cities: A survey." Ieee Access 9 (2021): 143222-143249. (99 citations)

5.      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. (71 citations)

·       Agalianos, K., et al. "Discrete event simulation and digital twins: review and challenges for logistics." Procedia Manufacturing 51 (2020): 1636-1641. (74 citations)

·       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. (64 citations)

6.      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. (28 citations)

·       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. (22 citations)

·       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. (92 citations)

7.      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. (22 citations)

·       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. (16 citations)

·       Todorovski, Ljupčo, and Sašo Džeroski. "Integrating knowledge-driven and data-driven approaches to modeling." ecological modelling 194.1-3 (2006): 3-13. (80 citations)

8.      Digital Twins for Multi-agent / Complex Systems

References:

·       Pretel, Elena, Alejandro Moya, Elena Navarro, Víctor López-Jaquero, and Pascual González. "Analysing the synergies between Multi-agent Systems and Digital Twins: A systematic literature review." Information and Software Technology (2024): 107503. (2 citations)

·       Mariani, S., Picone, M., Ricci, A. (2022). About Digital Twins, Agents, and Multiagent Systems: A Cross-Fertilisation Journey. In: Melo, F.S., Fang, F. (eds) Autonomous Agents and Multiagent Systems. Best and Visionary Papers. AAMAS 2022. Lecture Notes in Computer Science, vol 13441. (11 citations)

·       Marah H, Challenger M. Adaptive hybrid reasoning for agent-based digital twins of distributed multi-robot systems. SIMULATION. 2024;100(9):931-957. (0 citations – new articles)

9.      Digital Twins for Energy Systems

References:

·       Kabir, Md Rafiul, Dipal Halder, and Sandip Ray. "Digital Twins for IoT-driven Energy Systems: A Survey." IEEE Access (2024). (0 citations – new articles)

·       Brosinsky, Christoph, Rainer Krebs, and Dirk Westermann. "Embedded Digital Twins in future energy management systems: paving the way for automated grid control." at-Automatisierungstechnik 68, no. 9 (2020): 750-764.(21 citations)

·       Song, Zhao, Christoph M. Hackl, Abhinav Anand, Andre Thommessen, Jonas Petzschmann, Omar Kamel, Robert Braunbehrens, Anton Kaifel, Christian Roos, and Stefan Hauptmann. "Digital twins for the future power system: An overview and a future perspective." Sustainability 15, no. 6 (2023): 5259. .(32 citations)

·       Mostafa, Omar & Lazarova-Molnar, Sanja. (2024). Enhancing Reliability of Energy Systems with Digital Twins: Challenges and Opportunities. (0 citations – new articles)

10.  Digital Twins in Transportation and Automotive

References:

·       Schwarz, Chris, and Ziran Wang. "The role of digital twins in connected and automated vehicles." IEEE Intelligent Transportation Systems Magazine 14, no. 6 (2022): 41-51. (91 citations)

·       Bhatti, Ghanishtha, Harshit Mohan, and R. Raja Singh. "Towards the future of smart electric vehicles: Digital twin technology." Renewable and Sustainable Energy Reviews 141 (2021): 110801.  (422 citations)

·       Almeaibed, Sadeq, Saba Al-Rubaye, Antonios Tsourdos, and Nicolas P. Avdelidis. "Digital twin analysis to promote safety and security in autonomous vehicles." IEEE Communications Standards Magazine 5, no. 1 (2021): 40-46. (135 citations)

11.  Digital Twins for Environment and Sustainability

References:

·       Tzachor, Asaf, Soheil Sabri, Catherine E. Richards, Abbas Rajabifard, and Michele Acuto. "Potential and limitations of digital twins to achieve the sustainable development goals." Nature Sustainability 5, no. 10 (2022): 822-829. (110 citations)

·       Corrado, Casey R., Suzanne M. DeLong, Emily G. Holt, Edward Y. Hua, and Andreas Tolk. "Combining green metrics and digital twins for sustainability planning and governance of smart buildings and cities." Sustainability 14, no. 20 (2022): 12988. (36 citations)

·       Kim, Byungmo, Jaewon Oh, and Cheonhong Min. "Development of a simulation model for digital twin of an oscillating water column wave power generator structure with ocean environmental effect." Sensors 23, no. 23 (2023): 9472. (3 citations)

12.  Digital Twins in Agriculture

References:

·       Peladarinos, Nikolaos, Dimitrios Piromalis, Vasileios Cheimaras, Efthymios Tserepas, Radu Adrian Munteanu, and Panagiotis Papageorgas. "Enhancing smart agriculture by implementing digital twins: A comprehensive review." Sensors 23, no. 16 (2023): 7128. (60 citations)

·       Escribà-Gelonch, Marc, Shu Liang, Pieter van Schalkwyk, Ian Fisk, Nguyen Van Duc Long, and Volker Hessel. "Digital Twins in Agriculture: Orchestration and Applications." Journal of Agricultural and Food Chemistry 72, no. 19 (2024): 10737-10752. (12 citations)

·       Verdouw, Cor, Bedir Tekinerdogan, Adrie Beulens, and Sjaak Wolfert. "Digital twins in smart farming." Agricultural Systems 189 (2021): 103046. (494 citations)