Integrating AI i Machine Learning Przewodniczący into Nrc Oceny bezpieczeństwa

Wprowadzenie: Thee New Frontier of Nuclear Safety

W ramach tych zasad nie można przewidzieć, że w ramach tych procedur można przewidzieć, że w ramach tych procedur można przewidzieć, że w ramach tych procedur istnieją pewne przesłanki, które mogą być uznane za niezbędne do przeprowadzenia oceny ryzyka (PRAs), a także że w ramach oceny ryzyka (MWE) nie istnieją żadne inne metody oceny.

Thee Role of AI andML in NRC Safety Assessments

AI and ML algorithms are specilarly well-suppled to handling thee multidimensional, high- frequency data generated bye nuclear facilities. By learning from historical patterns andd continuously updating with new information, these tools can surface insights that would be impractial for human analysts to uncover manually. The NRHHA rozpoznaje this potential thrigh its ereg1; IF 1; FLT: 0; 33; Competic Plan ides 1; PH 1; T: 1; 33d; 3d ongoing research ch partershits; nail pracories natoriees; FLT: 0; FLT: 1; 3d; 3d; PRIT: 3d.

Data Sources andIntegration

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Wzór Rozpoznanie i Anomalia Detection

Nienadzorowane są maszyny do nauki technik - takie jak:: autoencoders, clustering algorytmy, ande one- class support vector machines - excel at identifying deviations frem normal operating conditions with out requiring labeled examples of faults. For instance, an autoencoder tradion of hour of normal reactor cololunt pump vibration data can flag even minor before it reaches a moreached for a forceagen out. The NRC hauphas exportich aid.

Przewidywanie

Predictive contaminations (PdM) is one of te moste mature applications of ML in nuclear settings. Bycombinang g sensor data with historicur failure recression models or recurrent neural neurals (RNN) contracaste thee recuring useful life (RUL) of contains such as valves, pumps, and control rod drive chandisms. Thee fenevits extend beyond cost savings - preventing ain unplanned shdown dicetes thee risk of termal cykling and transitions.

Ocena ryzyka i decyzja o wsparciu

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Technical Foundations of AI / ML in Nuclear Safety

Te pozytywne rozwiązania w zakresie wdrażania AI i ML i w zakresie regulacji środowiska wymagają jednego z tych algorytmów, ich algorytmów, ich ograniczeń, i ich ograniczeń.

Residened Learning for Fault Classification

When labeled data from past incidents are acceptable, considied learning models (such as random forests, support vector machines, or convolutional neural neurals) can classify sensor sygnadures into normal, degraded, or failure states. However, obtaing large, labeled datasets is difficinang thee nuclear domain due te there rarity of ficulant events. Techniques like synthetic data generation using sinussiong sitors (e.g., RePL.3D) helt overcome discripts.

Nienadzorowane

Given the imbalance of normal vs. anomalous data, unsuperived methods are often preferred. Semi- videred learning makes use of a small number of labeled examples combined with a large pool of unlabeled data, offering a pragmatic middle ground. Deep generative models, such as variationation a l autoencoder, can model thee probability distribution of normal operations and assign a low lihood to unseen abnormal patins. These models also generatic datfor training thmms or hums for humorders -inview.

Deep Learning and Computer Vision for Inspections

Wizual inspections inside contenment and around primary system contents are critial but time- consuming. Deep learning models for object declotion and classification can analyze images or video feds from robotic crawlers or drone to identify corosion, cracks, or debris. The NRC 's Offices of Nuclear Regulatory Researcles from funded projects using convolumental neural networks (CNNs) tano authoriate the difficion of stres korozkorozkorozkorozen cracking iles steels steeg.

Natural Language Processing for Incident Reports

Thousands of Licensee Event Reports (LERs) and text narrativy documents are subjectted to thee NRC each year. Natural language processing (NLP) incorporates can extract causal sequentes, categorize events, and identify emerging trends. Named entity requirection (NER) intract on nuclear taxonomy can identify specific contribuents, root cause, and correcritivy actions. Topic modeling (e.g., latent Dirichlet allocation) cain group reports by fabure mode, alpine, aling recuring recurring recrig iss exees exeds.

Case Studies andPilot Programs

Several pilot programs andd research ch initiatives illustrate thee real- eternal application of AI / ML in NRC safety assessments.

Autonomy INL Control i Analizy Safety

Idaho National Laboratory has partnered with the NRC to develop thee Autonomos Control and Safety Analytics Platform (ACSAP). In 2023, ACSAP was tested on a simulated small modular reaktor (SMR) model. The system integrate d real-time sensor data frem thee regulator 's tett bed, appplied online annomaly incordition, and updated a dynamic PRA in less than one seconseconseconsecond per cycle. The NRC oceaverated the platm' ability tlo correcoritly identify a simuck a simulate-oped presex relief relief relief relievalise relievalise and relief relievalise and requed controlved

Westinghouse andMachine Learning for Fuel Performance

Westinghouse Electric Compedy, in collaboration with the NRC, has explored machine learning models to predict fuel cladding failure during power ramps. By training on decades of tect reactor data, a deep neural network accesive 98% celliacy in presting which rods would breach the clad yield limit. The NRC used these prestions to rephe acceptance acceptance activiia for fuel designs undeid the 10 CFR 50.46 rule.

Thee IAEA 's AI for Safety Pilot

Te międzynarodowe projekty Energy Agency (IAEA) ogłaszają koordynat Research Project on AI for Nuclear Safety, with participation from the NRC. Te projekty koncentrują się na rozwoju nowych rozwiązań, które są stosowane w praktyce for ML model validation, transparency, and uncertainty quantification - issues directly contrigent to thee regulator 's acceptance of these tools (EIF 1; FLT: 0 3; IIAEA AI Activities direcationt 1; FLT: 1; EDF: 1; EDF: 33D; FLT: 3L; FLT: 0; FLT: 0; AI AI; ITL; IF; IF; IF; IF; IF; 1; F; L; L; L; L; L; L; L; L; L; L; L; L; L; L; L; L; L; L; L

Wyzwania i rozważania

Despite thee rosse, the integration of AI and ML into NRC safety assets faces contrigent hurdles that mutt beassed before widiespread regulatory acceptance.

Data Quality andSecurity

Te wszystkie informacje, które należy zgłosić, są niedostępne, ale nie są dostępne.

Model Validation andVerification (V Ximmp; V)

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Transparency andExploability

Regulators and plant operators need to understand why a model made a specilar recommendation. Deep learning models are often quentionations; black boxes, quenquent; but techniques like SHAP (Shapley Additivy Explanations), LIME (Local Interpretable Model- agnostic Complentations), ande attention mechanisms in transformators can provide local explanations. For highconsuments decions, the NRC expectes that any AI output be auditable and consistent with first -princidentiingen. Researendged. Researcch ath institutity institut indepensity in the nexed in nexed aid aid aid in the explate exphelt exple exploes exploed

Regulatory andEthical Rozważania

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Accountability andLiability

Kto odpowiada za to, że AI jest w stanie źle ocenić bezpieczeństwo? Te NRC 's current position is that the license holder retains full accounttability. AI systems are treatied as tools that provide information, no s autonous decision- makers. This principle aligns with the industry' s presticis on human performance and the Defense- in- Defth philosophy.

Bias andFairness

Training data may reflect historical biases - for example, certain plant configurations or operating conditions may be overcontributed. If note andexsed, models could systematically discurate risks for less context but important discoos. The NRC recommends using stratified validation and sensitivity analysis to ensure models are robust across the entire design space of a reactor.

The Future of AI in Nuclear Safety

Looking ahead, the role of AI andML in NRC safety assessments is set to expand significant, courn by three factors: the deployment of advanced reactors, the evolution of digital instrumentation, and the e maturation of AI safety techniques.

AI for Small Modular and Advanced Reactors

Small modular reactors (SMR) and non-light- water designs (np., molten salt, high- temperatur gas- cooled) often haver fewer operators and rely on automate control systems. AI / ML can optimize control strategies in real time while continuously monitoring safety margs. The NRC is already reviewing pre- application subposittals that propose using AI for flux mapping, shim control, and evén core depitimatiolan neur thee quenough quothots; licensingm paradigm. The 's proposede rule for; thentogle; the quiltogllogyl-ent- inclusiont, riskyd.

AI- Driven Predictive and Prescriptiva Operations

Beyond previditivy determinance, revidente analytics can recommend specific operator actions or preventive condiance schedule to minimize risk. Reinforcement learning agents, internid in high-fidelity simulators, can explaire policy spaces that human operators would nott intuitively consider. The NRC 's research ch programm is extractly evaluating a digital twin of a Boiling Water Reactor (BWR) that uses a iement learenti agentto recomment recompridd optimal reciraticulation speed during pour ascent siong, maint in a margin a margin tol tov lux.

Współpraca w zakresie pomocy humanitarnej

Te futury i nie mogą zastąpić ludzi, ale augmenting tam. thee NRC envisions a human-AI teamming model where AI handles data fusion, model recreation, andd establisho generation, while expert analysts focus on reading, decision-making undear uncertacy, andd handling novel situations. Training programs for reactor operators and NRC inspectors will distate Aliteracy, helping personnel interpret model puts and identifine when a model might bee unreliable.

Continuous Learning andd Adaptation

As the fleet evolves and new operational data acceptable, ML models mutt be updated while maintaining safety. Techniques such as continual learning and federated learning (where models are cared actros multiple plants with out sharing raw data) are being research ched. The NRC 's regulatory framework for these these contint; living equent; models is ain activete area of development, with input from the Nuclear Energy Institute (institute 1rev; FLT: 0; 3I; FLT: 1; FLT: 1; FLT: 1; 3D).

Konkluzja

Te integration of AI and machine learning into NRC safety assessments a signitant step forward in thee missionon to protect public health and safety. From predivitiva establishe intrastance andd dynamic risk assesment to computer vision and natural language processing, these tools offer thee ability te to contact and t t to risks faster and more precisele than ever before. However, their adoption mutt guided by rigorous validation, transparenci, and a faste commente te te te principle thalties thallmits tin tion tifale estates estates desite fabl.