Abstract
Autonomous, safe and reliable operations of Small Modular Reactors (SMR), and advanced reactors (AR) in general, emerge as distinct features of innovation flowing into the nuclear energy space. Digitalization brings to the fore of an array of promising benefits including, but not limited to, increased safety, higher overall efficiency of operations, longer SMR operating cycles and lower operation and maintenance (O&M) costs. On-line continuous surveillance of sensor readings can identify incipient problems, and act prognostically before process anomalies or even failures emerge. In principle, machine learning (ML) algorithms can anticipate key performance variables through self-made process models, based on sensor inputs or other self-made models of reactor processes, components and systems. However, any data obtained from sensors or through various ML models need to be securely transmitted under all possible conditions, including those of cyber-attacks. Quantum information processing offers promising solutions to these threats by establishing secure communications, due to unique properties of entanglement and superposition in quantum physics. More specifically, quantum key distribution (QKD) algorithms can be used to generate and transmit keys between the reactor and a remote user. In one of popular QKD communication protocols, BB84, the symmetric keys are paired with an advanced encryption standard (AES) protocol protecting the information. In this work, we use ML algorithms for time series forecasting of sensors installed in a liquid sodium experimental facility and examine through computer simulations the potential of secure real-time communication of monitoring information using the BB84 protocol.
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References
Aoto, K., et al.: A summary of sodium-cooled fast reactor development. Prog. Nucl. Energy 77, 247–265 (2014)
Kim, J.B., Jeong, J.Y., Lee, T.H., Kim, S., Euh, D.J., Joo, H.K.: On the safety and performance demonstration tests of prototype Gen-IV sodium-cooled fast reactor and validation and verification of computational codes. Nucl. Eng. Technol. 48, 1083–1095 (2016)
Blandford, E., et al.: Kairos power thermal hydraulics research and development. Nucl. Eng. Des. 364, 110636 (2020)
Ho, M., Obbard, E., Burr, P.A., Yeoh, G.: A review on the development of nuclear power reactors. Energy Procedia 160, 459–466 (2019)
Mandal, S., Santhi, B., Sridhar, S., Vinola, K., Swaminathan, P.: Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test. IEEE Trans. Nucl. Sci. 64, 1526–1534 (2017)
Mandal, S., Santhi, B., Sridhar, S., Vinola, K., Swaminathan, P.: A novel approach for fault detection and classification of the thermocouple sensor in nuclear power plant using singular value decomposition and symbolic dynamic filter. Ann. Nucl. Energy 103, 440–453 (2021)
Mandal, S., Santhi, B., Sridhar, S., Vinola, K., Swaminathan, P.: Minor fault detection of thermocouple sensor in nuclear power plants using time series and analysis. Ann. Nucl. Energy 134, 383–389 (2019)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: Proceedings of the 30th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 28, no. 31, pp. 310–1318 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ankel, V., Pantopoulou, S., Weathered, M., Lisowski, D., Cilliers, A., Heifetz, A.: One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short-Term Memory (LSTM) Neural Networks. Argonne National Laboratory (No. ANL/NSE-20/37) (2020)
Pantopoulou, S., et al.: Monitoring of temperature measurements for different flow regimes in water and Galinstan with long short-term memory networks and transfer learning of sensors. Computation 10, 108 (2022)
Wang, P., Zhang, J., Wan, J., Wu, S.: A fault diagnosis method for small pressurized water reactors based on long short-term memory networks. Energy 239, 122298 (2022)
Miki, D., Demachi, K.: Bearing fault diagnosis using weakly supervised long short-term memory. Nucl. Sci. Technol. 57, 1091–1100 (2020)
Heifetz, A., et al.: Perspectives on secure communications with advanced reactors: ultrasonic and millimeter waves classical and quantum communications. In: ANS Annual Meeting Embedded Conference 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC-HMIT2021) (2021)
National Academies of Sciences, Engineering, and Medicine. Quantum computing: progress and prospects (2019)
Heifetz, A., Agarwal, A., Cardoso, G.C., Gopal, V., Kumar, P., Shahriar, M.S.: Super-efficient absorption filter for quantum memory using atomic ensembles in a vapor. Opt. Commun. 232(1–6), 289–293 (2004)
Nurhadi, A.I., Syambas, N.R.: Quantum key distribution (QKD) protocols: a survey. In: 2018 4th International Conference on Wireless and Telematics (ICWT), 12 July 2018. IEEE (2018)
Liu, R., Rozenman, G.G., Kundu, N.K., Chandra, D., De, D.: Towards the industrialisation of quantum key distribution in communication networks: a short survey. IET Quant. Commun. (2022)
Zhao, B., et al.: Performance analysis of quantum key distribution technology for power business. Appl. Sci. 10(8), 2906 (2020)
Raouf, A.H.F., Safari, M., Uysal, M.: Performance analysis of quantum key distribution in underwater turbulence channels. JOSA B 37(2), 564–573 (2020)
Alshowkan, M., Evans, P.G., Starke, M., Earl, D., Peters, N.A.: Authentication of smart grid communications using quantum key distribution. Sci. Rep. 12(1), 1–13 (2022)
Shor, P.W., Preskill, J.: Simple proof of security of the BB84 quantum key distribution protocol. Phys. Rev. Lett. 85, 441 (2000)
Dworkin, M.J., et al.: Advanced encryption standard (AES) (2001)
Kultgen, D., Grandy, C., Kent, E., Weatherd, M., Andujar, D., Reavis, A.: Mechanisms Engineering Test Loop – Phase I Status Report, Argonne National Laboratory, ANL-ART-148 (2018)
Wu, X., et al.: SeQUeNCe: A customizable discrete-event simulator of quantum networks. Quant. Sci. Technol. 6, 4 (2021)
Martinez-Mateo, J., Pacher, C., Peev, M., Ciurana, A., Martin, V.: Demystifying the information reconciliation protocol cascade. arXiv preprint arXiv:1407.3257 (2014)
Acknowledgment
This work was supported in part by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) Generating Electricity Managed by Intelligent Nuclear Assets (GEMINA) program under Contract DE-AC02-06CH11357, and in another part by a donation to AI Systems Lab (AISL) by GS Gives and the Office of Naval Research under Grant No. N00014-18-1-2278. Experimental data was obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory.
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Pantopoulou, M., Pantopoulou, S., Roberts, M., Kultgen, D., Tsoukalas, L., Heifetz, A. (2024). Monitoring and Secure Communications for Small Modular Reactors. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_14
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