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Microreactor Core Transportation Cask Model Description for Criticality Safety Validation Basis Assessment (Rev. 2)

Criticality safety analyses are completed on transportation casks used for microreactor whole core shipment to provide examples of models and analyses to industry, regulators, and nuclear community at large to be used in verification and validation analyses of similar applications. The microreactors considered are based on a Gas-Cooled Microreactor (GCMR) and a Heat-Pipe Microreactor (HPMR), both utilize HALEU fuel in the form of TRISO particles and various other design options considered in industry microreactor designs. Variant design options of GCMR and HPMR were also investigated to provide a wider application range for each technology. Criticality safety analyses for the GCMR and HPMR packages were performed using the CSAS6 sequence of SCALE 6.3.2 with the ENDF/B-VII.1-based continuous energy neutron libraries. Different scenarios were investigated, including normal operation and water flooded conditions to represent nominal and hypothetical accident scenarios. Sensitivity and similarity analyses are also performed using the TSUNAMI sequence of SCALE 6.3.2, and the similarity analysis uses all the experiments from the ICSBEP Handbook with High Enriched Uranium (HEU), Intermediate and mixed Enriched Uranium (IEU) and Low Enriched Uranium (LEU) systems. Many ICSBEP experiments were found marginally similar, with similarity index (ck) values greater than 0.8, to the GCMR and HPMR cask models, especially in flooded conditions that may be more constraining due to reduced reactivity margins. Some experiments using TRISO fuel particles and graphite moderator, including the recent THETA and Deimos experiments from LANL, together with some IRPhEP experiments, were also used for the similarity analyses. These TRISO-fueled and graphite moderated experiments behaved similarly to the GCMR models, with maximum ck value greater than 0.9, but they are less similar to the HPMR models. This observation indicates that additional critical experiments might be needed to further validate the criticality safety models, especially for heat-pipe based microreactor transport packages and for non-flooded transportation configurations.

22 GENERAL STUDIES OF NUCLEAR REACTORS

Microreactor Assembly Transportation Cask Model Description for Criticality Safety Validation Basis Assessment (Rev. 3)

Criticality safety analyses are completed on a transportation cask used for microreactor assembly shipment to provide an example of model and analysis to industry for reproducing this type of study on their microreactor fuel shipment. The fuel assembly considered is based on a gas-cooled microreactor (GC-MR), which utilizes HALEU fuel in the form of TRISO particles and utilizes various design options considered in industry designs. Various versions of this GC-MR assembly were studied, with and without YH 2 moderator, providing similar conclusions. The shipment cask design is revised based on an existing ES-3100 design, developed by Y-12 for the transport of highly enriched uranium (HEU), but is enlarged to hold the GC-MR fuel assembly. Criticality safety analysis for the cask/GC-MR fuel assembly package was performed using the CSAS6 sequence of SCALE6.3.2, utilizing the ENDF/B-VII.1 based continuous energy neutron library, and the analysis strictly follows the guideline from NRC reference reports. Different scenarios, e.g. normal operation, undamaged cask with water flooded, damaged cask with optimal water moderation, have been analyzed and it could be concluded the package would always have a large margin of subcriticality even packed in an infinite array. Sensitivity and similarity analyses are also performed using the TSUNAMI sequence of SCALE6.3.2, and the similarity analysis uses all the experiments from the ICSBEP Handbook with Highly Enriched Uranium (HEU), Intermediate and Mixed Enriched Uranium (IEU) and Low Enriched Uranium (LEU) systems, together with additional ones that are sponsored by the DNCSH program, and selected IRPhEP experiments using TRISO fuel and graphite moderator. These similarity analyses indicate that the dry nominal design has no similar benchmark experiments (ck values greater than 0.8), which may become problematic if more assemblies are shipped together (or a fully loaded core is shipped) and margin to criticality is reduced. However, the cask models with flooded assemblies exhibited similarities to many experiments with c k values greater than 0.8.

22 GENERAL STUDIES OF NUCLEAR REACTORS

Operating Experience Data Analysis for Digital Instrumentation and Control System Reliability and Risk Assessment in Nuclear Power Plants

The implementation of advanced digital instrumentation and control (DI&C) systems in U.S. nuclear power plants (NPPs) can bring significant advancements in reliability, monitoring, and control capabilities. However, these systems also introduce new challenges, particularly in assessing risks such as common-cause failures (CCFs) and establishing robust reliability estimates for DI&C components. Addressing these challenges is critical for ensuring the safe and efficient operation of NPPs. Recently, Idaho National Laboratory was tasked by the U.S. Nuclear Regulatory Commission (NRC) to conduct a DI&C reliability study using operating experience data from the nuclear industry. The two operating experience data sources for the study are the Institute of Nuclear Power Operations’ Industry Reporting and Information System (IRIS) and the NRC’s Licensee Event Report database which is hosted at Idaho National Laboratory at https://lersearch.inl.gov/LERSearchCriteria.aspx. This report provides a comprehensive examination of DI&C systems, including their architecture, operational advantages, and associated challenges. It reviews existing industry DI&C studies and failure mode taxonomies, along with reliability data from various industries. Through a detailed analysis of these databases, the study provides insights into DI&C system performance. Considerations should be given to incorporate DI&C failure data into the NRC's Integrated Data Collection and Coding System and updating the Reliability and Availability Data System to support ongoing DI&C reliability studies. Recommendations are also provided for modeling DI&C reliability and CCF in probabilistic risk assessment, thereby supporting risk-informed decision-making and enhancing the reliability and safety of NPPs.

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Digital Real-Time Simulation and Power Quality Analysis of a Hydrogen-Generating Nuclear-Renewable Integrated Energy System

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets.

22 GENERAL STUDIES OF NUCLEAR REACTORS

Decarbonizing Industrial Heat and Electricity Applications Using Advanced Nuclear Energy

Idaho National Laboratory (INL) is investigating the technical pathways to assist industrial heat and electricity users to meet their decarbonization goals through integration with advanced nuclear power plants (NPPs). This project will deliver a library of process models and accompanying documents that guide specific industries in choosing potential nuclear technologies based on their needs. Considerations in providing this guidance include specific hazards from the industrial facility, heat transport requirements and associated technologies, and feasibility with site-specific demand profiles. The library of facility process models will be based on real data from industrial facilities in the United States. The industrial processes will be identified in this project based on the following: (1) operational heat characteristics that nuclear systems can provide, (2) sufficient energy requirements to merit the capital investment for nuclear plant construction, and (3) environmental benefits of replacing existing energy production with carbon-free nuclear power. Other decarbonization opportunities considered are the addition of nuclear-powered electrolysis processes or high-temperature electric heating where the thermal requirements exceed nuclear generation conditions. In addition to assessing the technical feasibility, INL is evaluating the impact of hazards introduced by the industrial facilities on the siting requirements of advanced NPPs. Site characterization of an industrial plant is essential to determine the feasibility and suitable integration methods for each industry. The assessment of siting and technical data will reveal opportunities for a single-use nuclear integration as well as integration of multiple industrial facilities with a single NPP.

02 PETROLEUM

RIM: Reliability and Integrity Management/ASME BPVC Section XI Division 2

Prepared at the request of the Nuclear Energy Institute (NEI) and the Joint Committee on Nuclear Risk Management's SubCommittee on Risk Applications, this slide set presents an update on some recent Reliability and Integrity Management (RIM) Subgroup work to respond to industry questions on American Society of Mechanical Engineers Boiler and Pressure Vessel Code Section XI Division 2. If approved, the briefing will be given at an upcoming workshop of the Nuclear Energy Institute (February 18-20, Washington, DC) and at an upcoming meeting of the Joint Committee on Nuclear Risk Management's SubCommittee on Risk Applications (February 25-26, College Park, MD).

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ACCELERATED DEPLOYMENT OF NOVEL MATERIALS BASED ON RELIABILITY INTEGRITY MANAGEMENT USING CUMULATIVE DAMAGE MODELING

There is currently no widely agreed, detailed general method for licensing a novel plant incorporating novel materials (or materials being deployed in novel environments); in many such situations, there are no directly applicable engineering code cases for decision-makers (including regulators) to rely on. This paper discusses a framework for solving this problem that is based on the Reliability and Integrity Management (RIM) approach delineated in ASME BPVC Section XI Division 2. NRC Regulatory Guide 1.246, Rev. 0, endorses, with conditions, the subject portion of the 2019 ASME Code. The proposed framework is meant to support development of a licensing case by addressing certain remaining technical challenges. The framework discussed here is compatible with the Licensing Modernization Project, but applying it in a specific case will call for advances in the state of practice, if not the state of the art. The RIM approach calls for applicants to (a) allocate reliability targets to plant structures, systems, and components (SSCs), (b) show that they are able to relate the currently observed physical condition of each SSC in the program to its failure probability well enough to determine whether the target reliability allocations are being satisfied, allowing for uncertainty related to the novelty of the materials/designs/operating environments, and (c) be able to demonstrate that the proposed program of surveillances will reliably detect unacceptable degradation of an SSC before SSC failure occurs. A modeling approach potentially applicable to item (b), based on cumulative damage modeling rather than failure rates, is briefly illustrated.

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Ship Motion Testing Needs Evaluation

The American Bureau of Shipping performed an evaluation of national maritime nuclear testing capabilities and determined there currently is an inability to test maritime nuclear power plants in a ship motion environment. A Ship Motion Test Facility (SMTF) is proposed to fill this gap. To inform design of this facility, this report documents an evaluation performed by a panel of technical experts that identifies key physical phenomena associated with maritime nuclear power plant operation that are important to safety and reliability and have a low state of knowledge. This evaluation concluded that all of the major test needs to understand a nuclear plant's behavior in a ship motion environment are for thermal-hydraulics related phenomena. Based on this finding, the evaluation panel concluded that the SMTF would not be required to use nuclear fuel to provide a heat source for the fluids. This would significantly decrease the cost, timeline, and complexity for this test facility to enable the advancement of maritime nuclear technology.

22 - GENERAL STUDIES OF NUCLEAR REACTORS

Serpent - Bison - THM Preliminary Multiphysics Modeling of a Nuclear Thermal Propulsion System Fuel Assembly

This work demonstrates the Monte Carlo neutronic and Thermo-Hydraulic coupling scheme using the Serpent code and MOOSE application Bison and Thermo Hydraulic Module. The coupling scheme is then applied to the reference BWX Technologies Nuclear Ther- mal Propulsion system at he fuel assembly level where it’s used to perform an analysis of the isothermal material coefficients and potential material reactivity worth. A method is developed to isolate which feedback effects should be considered for proceeding with reduced order deterministic neutronic modeling where branch off analysis must be con- ducted. The convergence behavior of the coupling scheme is demonstrated where it fol- lows the standard Picard iteration approach. Verification studies for the method of deduc- ing relevant feedback effects is also demonstrated.

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Multi-agent AI collaboration for digital twin development and assessment

Developing a digital twin (DT) model involves different steps that encompass formulating requirements, model development, implementation, and assessment with respect to real applications. Human expertise is required to coordinate and implement different steps in the DT development and assessment process. However, certain parts of this process can be automated using artificial intelligence (AI) agents for efficient workflow development. In this work, we test and analyze a multiagent AI collaboration with humans in the loop to automate different elements of the DT development and assessment process. To implement the workflow for multiagent AI DT development and assessment, we use Autogen, a multiagent framework developed by Microsoft. Autogen offers a modular and flexible framework for configuring and designing task-specific multiagent workflows. In this framework, large language models (LLMs) form the core intelligence of the AI agents where the quality and performance of the automated element is governed by the inherent capabilities and knowledge base of the LLM. We use retrieval augmented generation to supplement the LLM with relevant domain-specific information for DT requirement formulation. We illustrate this multiagent workflow using a case study on a thermal energy storage system, focusing on how AI agents can collaborate with humans to expedite and optimize different elements of DT development and assessment process.

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A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

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