ARTIFICIAL INTELLIGENCE | IT
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The Vienna International Centre, home to the International Atomic Energy Agency
simultaneously so for example reactor governance, safety, ergonomics, operation economy and grid services can be processed simultaneously, in accordance with each other.
Analytics: improving models and systems understanding AI techniques can support research. They can, for instance, speed up characterisation and validation of materials for new designs, reducing the time and cost of the necessary materials research, as well as helping develop new quality assurance practices for additively manufactured components for small and microreactors. They can optimise the design of experiments to reduce uncertainties, develop advanced dispatch and control methods for nuclear reactors for energy and process heat applications and optimise strategies for hybrid nuclear energy systems. Existing analytical models are often simplistic, in order
to be mathematically tractable, and have too little detail or accuracy to inform decision making. In contrast AI approaches can be leveraged to develop complex models and provide more accurate predictions. AI models can also be used for complex and time-
consuming statistical modelling such as for fitness-for- service assessments. In this case traditional models retain an advantage in their higher generalisation to new data given the explicitly defined relations between features and target. This explicit, direct relationship is typically lacking in AI models, impacting their performance on new data. This shortcoming can be overcome by incorporating physical concepts into the AI model, showing that expert knowledge can be used in conjunction with AI techniques to improve current modelling capabilities. Finally, AI can be used for model validation, especially
for advanced computer simulations that have proliferated over the last years and generate large amounts of data. Such approaches can support, for instance, digital twin applications.
Prediction and prognostics: informing maintenance Predicting events such as failures and assessing current asset conditions are tools that allow plant owners to plan their maintenance and outage strategies. AI allows operation data streams to be used to schedule maintenance or inspection or for highlighting abnormal conditions. Advanced simulation tools have become available this
century, but operators have to be convinced that they will provide more accurate predictions than existing tools calibrated over decades. Experts are needed to assess whether and how the advanced tools could beat legacy
tools but AI can provide mathematically rigorous and explainable algorithms to measure information content available in the simulation and experimental data using information theory principles
Insights from experiments and operation The nuclear industry has thousands of reactor years of operating experience and huge libraries of support for model validation. Data science technologies can leverage this rich experience in unprecedented ways to unlock new best practices and better inform future decisions, from conceptual design to licensing and operation. Applications include holistic assessment of maintenance
records to extract lessons or identifying the best sensor and arrangement for a class of reactors (correlated with various sources of process anomalies, undetected with existing equipment-specific sensors). In general, methods are in development and are currently
exploring how to gain insight from nuclear data. A key challenge is that the methods are both language and jargon-specific. They need an industry dictionary.
Deployment challenges It is currently challenging to provide interpretability, confidence, and robustness measures of performance for AI and to allay cybersecurity and regulatory concerns. Cyber attacks used against AI systems aim to have the model make poor decisions and this allows for a large attack surface distributed over time. It is important to proactively address these cyber fears. Regulators may find traditional assurance inapplicable and the limited transparency of AI and machine learning may make actions difficult to interpret, biases unclear and malfunctions mysterious. Moreover, it is much more fast- moving than regulatory processes. High level regulatory safety assessment principles and guidance are required and so are standards. ■
Authors ● T. Seuaciuc-Osorio, Electric Power Research Institute, Washington, USA ● I. Virkkunen, Aalto University, Espoo, Finland ● H. Miedl, TÜV Rheinland Industrie Service GmbH, Cologne, Germany ● B. Briquez, Tecnatom, Madrid, Spain ● H. Abdel-Khalik, Purdue University, West Lafayette, USA ● C. Lamb, Sandia National Laboratories, Albuquerque, USA ● E. Bradley, H. Varjonen, C. Batra, P. Dieguez Porras, J. Eiler, T. Jevremovic, B. Johnson ● Division of Nuclear Power, International Atomic Energy Agency, Vienna
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