Digital Twins in Large-Scale Scientific Infrastructure Projects

Denis V. Kosyakov; Mikhail A. Marchenko

doi:10.14529/jsfi230308

Authors

Denis V. Kosyakov Russian Research Institute of Economics, Politics and Law in Science and Technology, Moscow, Russian Federation; Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russian Federation
Mikhail A. Marchenko Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russian Federation

DOI:

https://doi.org/10.14529/jsfi230308

Keywords:

digital twins, neural networks, supercomputing modelling, large-scale scientific infrastructure, Siberian Circular Photon Source

Abstract

The article provides an overview of publications on the topic of Digital Twins of largescale scientific infrastructure. History, basic concepts and definition of Digital Twins are given. Main terminology in the field of big science and large-scale scientific infrastructure is also described. In Russian practice, the large-scale scientific infrastructure projects are often referred to as "megascience installations". Such installations usually include facilities for research in areas such as astronomy and high-energy physics. The research infrastructure is a complex of construction facilities, engineering systems, precise control and measuring equipment, characterized by high complexity and strict requirements for all operational parameters. In addition, these facilities are associated with high operating costs, are sensitive to minor changes in their condition and environmental conditions, and carry the risk of data loss during long-term and unique experiments. Then, information about the use of Digital Twins in large scale astrophysical projects and also for particle accelerators control and tuning is provided. Potential areas of application of Digital Twins in large projects of scientific infrastructure are summarized. Necessary information about the Siberian Circular Photon Source (SKIF, in Russian) is given. On the basis of the review, and goals and objectives for the Digital Twin of the SKIF are determined. An analysis of the necessary computing resources and data storage volume is also carried out.

References

Grieves, M.: Digital Twin: Manufacturing Excellence Through Virtual Factory Replication. White Paper, pp. 1–7 (2014).

Grieves, M., Vickers, J.: Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. pp. 85–113. Springer (2016). https://doi.org/10.1007/978-3-319-38756-7_4

Tuegel, E.J., et al.: Reengineering Aircraft Structural Life Prediction Using a Digital Twin. Int. J. Aerospace Eng. Article 154798. (2011). https://doi.org/10.1155/2011/154798

Glaessgen, E.H., Stargel, D.S.: The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles (2012).

Tao, F., et al.: Digital Twin in Industry: State-of-the-Art. IEEE Trans. Ind. Inform. 15(4), 2405–2415 (2019). https://doi.org/10.1109/TII.2018.2873186

O’Connell, C.: CAD/CAM (Computer-Aided Design/Computer-Aided Manufacturing). Sci. & Tech. Libraries, Routledge 7(4), 127–154 (1987). https://doi.org/10.1300/J122v07n04_13

Borrelli, A., Wellmann, J.: Computer Simulations Then and Now: An Introduction and Historical Reassessment. N.T.M. 27(4), 407–417 (2019). https://doi.org/10.1007/s00048-019-00227-6

Boschert, S., Rosen, R.: Digital Twin-The Simulation Aspect. In: Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers. pp. 59–74. Springer (2016). https://doi.org/10.1007/978-3-319-32156-1_5

Fuller, A., et al.: Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access 8, 108952–108971 (2020). https://doi.org/10.1109/ACCESS.2020.2998358

Atzori, L., Iera, A., Morabito, G.: The Internet of Things: A Survey. Comput. Netw. 54(15), 2787–2805 (2010). https://doi.org/10.1016/j.comnet.2010.05.010

Sisinni, E., et al.: Industrial Internet of Things: Challenges, Opportunities, and Directions. IEEE Trans. Ind. Inform. 14(11), 4724–4734 (2018). https://doi.org/10.1109/TII.2018.2852491

Lee, J., Bagheri, B., Kao, H.-A.: A Cyber-Physical Systems Architecture for Industry 4.0-based Manufacturing Systems. Manuf. Lett. 3, 18–23 (2015). https://doi.org/10.1016/j.mfglet.2014.12.001

Philip Chen, C.L., Zhang, C.-Y.: Data-intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data. Inf. Sci. 275, 314–347 (2014). https://doi.org/10.1016/j.ins.2014.01.015

Lermer, M., Reich, C.: Creation of Digital Twins by Combining Fuzzy Rules with Artificial Neural Networks. In: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal, October 14-17, 2019. pp. 5849–5854. IEEE (2019). https://doi.org/10.1109/IECON.2019.8926914

Tarkhov, D.A., Malykhina, G.F.: Neural Network Modelling Methods for Creating Digital Twins of Real Objects. Journal of Physics: Conference Series 1236(1), 012056 (2019). https://doi.org/10.1088/1742-6596/1236/1/012056

Kaur, M.J., Mishra, V.P., Maheshwari, P.: The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action. In: Farsi, M., Daneshkhah, A., Hosseinian-Far, A., Jahankhani, H. (eds) Digital Twin Technologies and Smart Cities. Internet of Things. pp. 3–17. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18732-3_1

Rathore, M.M., et al.: The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities. IEEE Access 9, 32030–32052 (2021). https://doi.org/10.1109/ACCESS.2021.3060863

Tao, F., et al.: Digital Twin-driven Product Design, Manufacturing and Service with Big Data. Int. J. Adv. Manuf. Tech. 94(9-12), 3563–3576 (2018). https://doi.org/10.1007/s00170-017-0233-1

Kritzinger, W., et al.: Digital Twin in Manufacturing: A Categorical Literature Review and Classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018). https://doi.org/10.1016/j.ifacol.2018.08.474

Errandonea, I., Beltran, S., Arrizabalaga, S.: Digital Twin for Maintenance: A Literature Review. Comput. Ind. 123, 103316 (2020). https://doi.org/10.1016/j.compind.2020.103316

Mohammadi, N., Taylor, J.E.: Smart City Digital Twins. In: 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, HI, USA, November 27 – Dec. 1, 2017. pp. 1–5. IEEE (2018). https://doi.org/10.1109/SSCI.2017.8285439

Deng, T., Zhang, K., Shen, Z.-J.M.: A Systematic Review of a Digital Twin City: A New Pattern of Urban Governance Toward Smart Cities. J. Manag. Sci. Eng. 6(2), 125–134 (2021). https://doi.org/10.1016/j.jmse.2021.03.003

Deng, M., Menassa, C.C., Kamat, V.R.: From BIM to Digital Twins: A Systematic Review of the Evolution of Intelligent Building Representations in the AEC-FM Industry. J. Inf. Technol. Constr. 26, 58–83 (2021). https://doi.org/10.36680/J.ITCON.2021.005

Lu, Q., et al.: From BIM Towards Digital Twin: Strategy and Future Development for Smart Asset Management. Stud. Comput. Intell. 853, 392–404 (2020). https://doi.org/10.1007/978-3-030-27477-1 30

Sepasgozar, S.M.E., et al.: Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction. Sustainability (Switzerland) 13(1), 1–22 (2021). https://doi.org/10.3390/su13010161

Regis, A., et al.: Physic-based vs Data-based Digital Twins for Bush Bearing Wear Diagnostic. Wear 526-527, 204888 (2023). https://doi.org/10.1016/j.wear.2023.204888

Sarkar, P.: Digital Twin Modeling Using Machine Learning and Constrained Optimization. Medium (2022). https://towardsdatascience.com/digital-twin-modeling-usingmachine-learning-and-constrained-optimization-401187f2a382, accessed: 2023-04-10

Jacob, M., Hallonsten, O.: The Persistence of Big Science and Megascience in Research and Innovation Policy. Sci. Public Policy 39(4), 411–415 (2012). https://doi.org/10.1093/scipol/scs056

Borner, K., Silva, F.N., Milojevic, S.: Visualizing Big Science Projects. Nat. Rev. Phys. 3(11), 753–761 (2021). https://doi.org/10.1038/s42254-021-00374-7

Cramer, K.C., Hallonsten, O.: Big Science and Research Infrastructures in Europe. 2020, pp. 1–264. Edward Elgar Publishing, Cheltenham, UK (2020). https://doi.org/10.4337/9781839100017

D’Ippolito, B., Ruling, C.-C.: Research Collaboration in Large Scale Research Infrastructures: Collaboration Types and Policy Implications. Res. Policy 48(5), 1282–1296 (2019). https://doi.org/10.1016/j.respol.2019.01.011

Johnston, S., et al.: Science with ASKAP: The Australian Square-Kilometre-Array Pathfinder. Exp. Astron. 22(3), 151–273 (2008). https://doi.org/10.1007/s10686-008-9124-7

Bednarz, T., et al.: Digital Twin of the Australian Square Kilometre Array (ASKAP). In: SIGGRAPH Asia 2020 Posters. Article 15. ACM (2020). https://doi.org/10.1145/3415264.3425462

Jonas, J.L.: MeerKAT - The South African Array with Composite Dishes and Wide-Band Single Pixel Feeds. Proc. IEEE 97(8), 1522–1530 (2009). https://doi.org/10.1109/JPROC.2009.2020713

Taljaard, C., Chrysostomou, A., Van Zyl, A.N.: Sculpting a Maintenance Twin for SKA. In: Modeling, Systems Engineering, and Project Management for Astronomy IX, vol. 11450, pp. 114500F. SPIE (2020). https://doi.org/10.1117/12.2562337

Nan, R., et al.: The Five-Hundred-Meter Aperture Spherical Radio Telescope (FAST) Project. Int. J. Mod. Phys. D 20(6), 989–1024 (2011). https://doi.org/10.1142/S0218271811019335

Li, Q.-W., et al.: Prognostics and Health Management of FAST Cable-Net Structure Based on Digital Twin Technology. Res. Astron. Astrophys. 20(5) (2020). https://doi.org/10.1088/1674-4527/20/5/67

Wen, J., et al.: Rapid Modeling Method for The Digital Twin of Five-Hundred-Meter Aperture Spherical Radio Telescope. IAENG Int. J. Comput. Sci. 49(2) (2022).

Zhang, Q., Wu, P., Zhao, Z.: Design and Application of Digital Twin System Architecture for Large Radio Telescope. Jisuanji Jicheng Zhizao Xitong/Comput. Integr. Manuf. Syst. CIMS 27(2), 364–373 (2021). https://doi.org/10.13196/j.cims.2021.02.005

Predehl, P., et al.: The eROSITA X-ray Telescope on SRG. Astron. Astrophys. 647 (2021). https://doi.org/10.1051/0004-6361/202039313

Seppi, R., et al.: Detecting Clusters of Galaxies and Active Galactic Nuclei in an eROSITA All-Sky Survey Digital Twin. Astron. Astrophys. 665 (2022). https://doi.org/10.1051/0004-6361/202243824

Jones, B.J.P.: The Status of the MicroBooNE Experiment. J. Phys.: Conf. Ser. 408 (2013). https://doi.org/10.1088/1742-6596/408/1/012028

Kafkes, D., Schram, M.: Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster. In: Proc. of the 12th International Particle Accelerator Conference. pp. 2268–2271. JACoW Publishing, Geneva, Switzerland (2021). https://doi.org/10.18429/JACoW-IPAC2021-TUPAB327

Abramowicz, H., et al.: Higgs Physics at the CLIC Electron-Positron Linear Collider. Eur. Phys. J. C 77(7), article 475 (2017). https://doi.org/10.1140/epjc/s10052-017-4968-5

Doytchinov, I., et al.: Thermal Effects Compensation and Associated Uncertainty for Large Magnet Assembly Precision Alignment. Precis. Eng. 59, 134–149 (2019). https://doi.org/10.1016/j.precisioneng.2019.06.005

Neumann, A., et al.: bERLinPro Becomes SEALab: Status and Perspective of the Energy Recovery Linac at HZB. In: Proc. 13th International Particle Accelerator Conference. pp. 1110–1113. JACoW Publishing, Geneva, Switzerland (2022). https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT048

Mahshid, M.Z., et al.: Realization of an Energy System-Informed Digital Twin of the KARA Accelerator at KIT in a Real-Time Simulation Environment: the ACCESS Project. In: 14th International Particle Accelerator Conference (2023).

Giles, A., et al.: Operation the Accelerator Test Facility Linac Transport Beamline by Using Artificial Intelligence and Machine Learning Methods. In: 14th International Particle Accelerator Conference (2023).

Ansel, A., et al.: A New Product Lifecycle Management Platform for CERN’s Accelerator Complex and Beyond. In: 14th International Particle Accelerator Conference (2023).

Edelen, A.: AI/ML and Its Operational Challenges at SLAC’s Accelerators and Collaborating Facilities (2021).

Schaefer, K.I. (ed.): Technological Infrastructure of the SKIF Center for Common Use. Novosibirsk (2022). https://disk.yandex.ru/d/1SBhHph2rgbeBg, accessed: 2023-04-10

Scientific Program of the SKIF Center for Common Use: Key Areas of Research at the First Stage Experimental Stations and the Concept of Infrastructure Development until 2035. Novosibirsk, 439 p. (2023). https://disk.yandex.ru/d/gxEIdsjIalIvHw, accessed: 2023-04-10

GOST R 57700.37-2021: Computer Models and Modeling. Digital Twins of Products. General Provisions.

Wilkinson, M., Dumontier, M., Aalbersberg, I., et al.: The FAIR Guiding Principles for Scientific Data Management and Stewardship. Sci. Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

Schwarz, N., Campbell, S., Hexemer, A., et al.: Enabling Scientific Discovery at Next-Generation Light Sources with Advanced AI and HPC. In: Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, SMC 2020, Comm. Comp. Inf. Sci., vol. 1315, pp. 145–156. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63393-6_10

Wang, C., Steiner, U., Sepe, A.: Synchrotron Big Data Science. Small 14(46), 1802291 (2018). https://doi.org/10.1002/smll.201802291

Wagner, H.: Data Handling and Storage. Synchrotron Radiation News 32(3), 2–3 (2019). https://doi.org/10.1080/08940886.2019.1618682

Ponsard, R., Janvier, N., Kieffer, J., et al.: RDMA Data Transfer and GPU Acceleration Methods for High-Throughput Online Processing of Serial Crystallography Images. J. Synchrotron Radiat. 27(5), 1297–1306 (2020). https://doi.org/10.1107/s1600577520008140

Prokhorov, A., Lysachev, M., Borovkov, A. (eds.): Digital Twin. Analysis, Trends, World Experience. First Edition. Moscow, Alliance Print, 401 p. (2020).