MSc thesis topic: Embed ODTP digital twins anywhere on Earth with AnywhereXR

The paradigm of the digital twin offers a novel
perspective on how to manage the data efficiently and make these systems
available more steadily at a lower cost. The Open Digital Twin Platform (ODTP)
is designed to be openly available to all interested parties to enable a common
framework and baseline for digital twin based research. ODTP uses
containerization, loose coupling, and micro-services to provide dynamically
composable digital twins. ODTP also provides tools for licensing resolution,
privacy and access control, and reproducibility. ODTP converts research
pipelines into an easy-to-use version, making it possible to initiate analytics
with one click. ODTP enables the quick adding of relevant data sources and
analytical pipelines related to any topic and make them easily usable,
accessible, and shareable to research, industry, and governance.

At the same time, visualization has long been fundamental to human communication and decision-making. Today, we stand at the threshold of integrating high-fidelity visualizations into immersive digital twins for communication, co-design, and participatory decision-making. AnywhereXR is a tool for such immersive visualisations. AnywhereXR relies on public data and in its current version is available for the Netherlands. In this thesis project, AnywhereXR will be integrated into ODTP as a final visualisation node for complex spatial data processing pipelines. AnywhereXR allows to explore spatial information from research task in its natural context environment.

The concept of Digital Twins was coined in 2003 (Grieves & Vickers, 2017) to conceptualise the matching of the physical world with a digital representation through some kind of data collection process in an industrial context. The idea stuck around and slowly emancipated itself from its industrial roots expanding to various other fields and disciplinary contexts where the simple description makes sense to abstract processes like in smart cities (Batty, 2018). While Digital Twins have found wide applications, they have remained underspecified and carry a very different meaning in different communities. For example, in aviation, Digital Twins stand for similitude of the digital representation to the highest possible physical degree (Glaessgen & Stargel, 2012). Whereas for Smart Cities, Digital Twins only require eventual consistency (Batty, 2018), i.e., representing some past abstract state of the city correctly such as traffic load or the population in contrast to properly simulating the physical movement of cars and people as similitude would require.

Are these diverse understandings epistemologically incompatible or can they be studied in a common framework that defines Digital Twins as an open standard? Previous work on Digital Twins is limited to prototypes that demonstrate the idea and variations thereof (Glaessgen & Stargel, 2012; Tao & Zhang, 2017; Boje et al., 2020; Akroyd et al., 2021; Anda et al., 2021; Raes et al., 2021). At the same time, industry is creating standards for Digital Twins that are proprietary and could become the norm by the virtue of simply being there like early web browsers and operating systems. We identified the unique opportunity to create an open standard for Digital Twins right now because we are at the crossroads where the required technologies for Digital Twins are maturing, and we have no default yet.

The ongoing debates have identified the Semantic Web (Berners-Lee & Lassila, 2001) as a core technology to make information exchangeable across systems through knowledge graphs upon which Digital Twins could be based (Akroyd et al., 2021; Boje et al., 2020). Whereas an ontology is a crucial component to interact with unknown Digital Twins based on a generic format (McGuinness & Van Harmelen, 2004) - a Digital Twin is more than just a knowledge exchange platform (Grübel et al., 2022). The systematic nature of Digital Twins makes them a natural candidate to implement FAIR (findable, accessible, interoperable, and reusable) data standards (Schultes et al., 2022) that guide ORD practices and enable reproducible research. In the best case, Digital Twins as an open standard can be a driver of development like the Internet. In the worst case, they could be bogged down in a battle of standards like HD-DVD vs BluRay that slow down adaptation because of uncertainty about which format will eventually become the default.

Industrial players from large multinational companies to consultants are keen to offer their Digital Twin solutions in the hope to fence off significant market shares from the imagined application fields of Digital Twins. The reasons why there is no open standard yet are multitude but include these wild-west assumptions of a large untapped opportunity that can be conquered and fenced off (Roest, 2019). Another issue is that the underlying technologies are only now maturing and are often still under development themselves. This makes early Digital Twins fickle and prone to breaking down. Most current actors hope to be a defining force for Digital Twins to come and therefore try to create facts on the ground. In this fast-paced environment, only few truly open initiatives have been formed. Mostly, they revolve around specific EU projects such as DUET (Raes et al., 2021) or industry platforms such as Bosch’s open IoT ecosystem DITTO. These approaches are often hardware-specific, task-specific, and platform-specific, and ultimately fail to provide a common standard that can be generalised across all Digital Twin applications from mobility over construction to industrial production.

Digital Twins are attractive for many communities because they inherently couple data collection, data analysis and data presentation and provide users ranging from policy makers and researchers to citizens with an integrated view on information. Embedding different models in a Digital Twin will allow us to make them comparable and advance the discourse through validation.
On the one hand, the Digital Twin-based ORD practices that were hoped for so far are still lacking (Roest, 2019). On the other hand, we are close to building Open Digital Twins as the constituent components of Digital Twins (see Figure 1 for a detailed explanation of our model; Grübel et al., 2022) can be implemented with existing ORD solutions.

Relevance to research/projects at GRS or other groups

One of the strategic initiatives of WUR is the creation and exploration of Digital Twins. GRS is leading efforts to create digital twins for mobility applications. ESG is in the process of establishing a landscape visualization laboratory (WANDER) to facilitate efforts for integrating advances in visualization technologies into applications.

Objectives and Research questions

Digital twins offer a broad range of visualisation tasks. Master students interested in the following topics can approach me for further details:

• Visualizing a Netherlands Digital Twin
• AnywhereXR integration in ODTP
• Build general Digital Twin pipelines
• Data visualisation (automatically visualise data)
• Visualizing other world regions
• Expanding AnywhereXR for other regions

Requirements

This topic is open to multiple students. Specific topics that students select will each have additional requirements that can be discussed on a case-by-case basis. The following requirements apply to all topics:

• Python knowledge (required)
• Unity knowledge (required)
• Software engineering (optional)
• Digital Twin background (optional)
• XR background (optional)

Literature and information

• Knuiman, B., Wallgrün, J. O., Zhao, J., Grübel, Jascha & Klippel, A. (2024). AnywhereXR - On-the-fly 3D Environments as a Basis for Open Source Immersive Digital Twin Applications. Under submission

Expected reading list before starting the thesis research

• Akroyd, J., Mosbach, S., Bhave, A., & Kraft, M. (2021). Universal digital twin-a dynamic knowledge graph. Data-Centric Engineering, 2.
• Anda, C., Medina, S. A. O., & Axhausen, K. W. (2021). Synthesising digital twin travellers: Individual travel demand from aggregated mobile phone data. Transportation Research Part C: Emerging Technologies, 128, 103118.
  Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-820.
• Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284(5), 34-43.
  Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179.
• Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1818).
• Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems (pp. 85-113). Springer, Cham.
  Grübel, J., Rios, C. V., Zuo, C., Ossey, S., Franken, R. M., Balac, M., ... & Riba-Grognuz, O. (2023, August). Outlining the open digital twin platform. In 2023 IEEE Smart World Congress (SWC) (pp. 1-3). IEEE.
• Grübel, J., Thrash, T., Aguilar, L., Gath-Morad, M., Chatain, J., Sumner, R. W., Hölscher, C., & Schinazi, V. R. (2022). The Hitchhiker’s Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities. Remote Sensing, 14(13). doi:10.3390/rs14133095
• Grübel, J., Gath-Morad, M., Aguilar, L., Thrash, T., Sumner, R. W., Hölscher, C., & Schinazi, V. (2021). Fused Twins: A Cognitive Approach to Augmented Reality Media Architecture. In Media Architecture Biennale 20 (pp. 215-220).
  McGuinness, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C recommendation, 10(10), 2004.
• Schultes, E., Roos, M., da Silva Santos, L. O. B., Guizzardi, G., Bouwman, J., Hankemeier, T., ... & Mons, B. (2022). FAIR Digital Twins for Data-Intensive Research. Frontiers in Big Data, 5.
• Raes, L., Michiels, P., Adolphi, T., Tampere, C., Dalianis, T., Mcaleer, S., & Kogut, P. (2021). DUET: a framework for building secure and trusted digital twins of smart cities. IEEE Internet Computing.
• Roest, M. (2019). An Open Source Platform for Digital Twins?. [online] LinkedIn. Available at: <https://www.linkedin.com/pulse/open-source-platform-digital-twins-mark-roest/> [Accessed 30 March 2022].
• Tao, F., Zhang, H., Liu, A., & Nee, A. Y. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on industrial informatics, 15(4), 2405-2415.
• Tao, F., & Zhang, M. (2017). Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access, 5, 20418-20427.

Theme(s): Modelling & visualisation, Human – space interaction