Monitoring and Secure Communications for Small Modular Reactors

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.