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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cold Testing for Characterization of Oxide Feed Impurities in Support of the ARIES Program

The Advanced Recovery and Integrated Extraction System (ARIES) Program converts surplus plutonium metal components into an oxide powder form and packages the material for final disposition. In support of the ARIES program, over a metric ton (MT) of plutonium has been removed from surplus nuclear weapons since 1998 and packaged for long-term storage in 3013 cans. The oxide production rate is anticipated to increase substantially over the next decade in support of dilute and dispose goals. Analytical chemistry data from destructive analysis of the resulting 70+ blend lots to date has been reported for contaminants in the product oxide. Changes in the Pu product specification for Pu oxide product, induced by a different pit mix, the LANL swap program and processing Alternate (non-Pit Pu) Feed Stock (AFS) metals, will affect the level and the mix of these contaminants in the upcoming years. With these changes in feed material, it will be important to provide fast and efficient measurement of elemental impurities in the oxide to be certain that the process is under control and to monitor the anticipated changes from the historical blend lot data. Non-destructive methods need to be evaluated for the characterization of the plutonium oxide product and the quantification of the impurities at the current and anticipated levels. A feasibility study has begun to investigate the applicability of new spectroscopic methods for oxide impurity characterization. To this end, here we describe the preparation of surrogate materials to support these investigations, and the results of a feasibility study investigating the use of LIBS (Laser Induced Breakdown Spectroscopy) to characterize the surrogate materials as calibration standards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1994

LET spectra of fragmented 600-MeV/A iron beam on aluminum and polyethylene: A preliminary analysis

An iron beam experiment recently conducted at the Lawrence Berkeley Laboratory Bevalac by Benton et al. provides an opportunity for verifying the new Green's function computer code (GRNTRN) and assessing the related nuclear database. The iron beam with 600 MeV/A at extraction traversed a series of beam transport elements, lead foil, and several triggering devices before impacting the target. Of these, only the 2.24 g/sq cm lead foil and target are considered in the transport analysis with an assumed (inferred from calibration) beam energy of 557 MeV/A. A thin layer of CR-39 plastic nuclear track detector (PNTD) was placed in front of the target to monitor the incident flux and a stack of four PNTD's placed behind to measure the linear energy transfer (LET) distribution of the transported beam. Test data are analyzed for three separate targets: 2 g/sq cm aluminum, 5-cm polyethylene, and 8-cm polyethylene. The two-layer GRNTRN results were mapped into the detector response function for comparison with the measured LET spectra. Reasonable agreement is obtained. Future research and analysis can be improved by using a more accurate isotope set or including other important media which significantly alter the beam. The assumption that the fragmentation cross sections are too small for aluminum is inconclusive because the production of fragments by 2 g/sq cm aluminum can be more significantly affected by the fragment contribution originating from the media in front of the target than that currently considered in the analysis.

Shinn, Judy L.↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗
DOE OSTI2024

ECAR-7932 Rev 0 Large Eddy Simulation of MARVEL Reactor Core Subchannel to Evaluate Model Uncertainty of Reynolds-Averaged Navier-Stokes Equation Based Computational Fluid Dynamics Analysis

In the previous work (ECAR-7210), the peak cladding temperature of the MARVEL microreactor has been evaluated by steady-state Reynolds-Averaged Navier-Stokes (RANS) based computational fluid dynamics (CFD) simulations. Although numerical uncertainties of RANS-based CFD simulations has been assessed in ECAR-7210, the model uncertainty of RANS turbulence models must be investigated to resolve the issues related to inaccurate prediction of turbulent heat flux and flow pulsation in a tight lattice rod bundle using the steady-state RANS simulations. Consequently, this ECAR conducted a high-fidelity CFD analysis utilizing Large Eddy Simulation (LES) to generate reference data and investigated the model uncertainty of RANS-based CFD simulations.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
NASA NTRS2007

Comparison of Integrated Radiation Transport Models with TEPC Measurements for the Average Quality Factors in Spaceflights

The purpose of this work is to test our theoretical model for the interpretation of radiation data measured in space. During the space missions astronauts are exposed to the complex field of radiation type and kinetic energies from galactic cosmic rays (GCR), trapped protons, and sometimes solar particle events (SPEs). The tissue equivalent proportional counter (TEPC) is a simple time-dependent approach for radiation monitoring for astronauts on board the International Space Station. Another and a newer approach to Microdosimetry is the use of silicon-on-insulator (SOI) technology launched on the MidSTAR-1 mission in low Earth orbit (LEO). In the radiation protection practice, the average quality factor of a radiation field is defined as a function of linear energy transfer (LET), Q(sub ave)(LET). However, TEPC measures the average quality factor as a function of the lineal energy y, Q(sub ave)(y), defined as the average energy deposition in a volume divided by the average chord length of the volume. Lineal energy, y, deviates from LET due to energy straggling, delta-ray escape or entry, and nuclear fragments produced in the detector volume. Monte Carlo track structure simulation was employed to obtain the response of a TEPC irradiated with charged particle for an equivalent site diameter of 1 micron of wall-less counter. The calculated data of the energy absorption in the wall-less counter were compiled for various y values for several ion types at various discrete projectile energy levels. For the simulation of TEPC response from the mixed radiation environments inside a spacecraft, such as, Space Shuttle and International Space Station, the complete microdosimetric TEPC response, f( y, E, Z), were calculated with the Monte Carlo theoretical results by using the first order Lagrangian interpolation for a monovariate function at a given y value (y = 0.1 keV/micron 5000 keV/micron) at any projectile energy level (E = 0.01 MeV/u to 50,000 MeV/u) of each specific radiation type (Z = 1 to 28). Because the anomalous response has been observed at large event sizes in the experiment due to the escape of energy out of sensitive volume by delta-rays and the entry of delta-rays from the high-density wall into the low-density gas-volume cavity, Monte Carlo simulation was also made for the response of a walled-TEPC with wall thickness 2 mm and density 1 g/cm(exp 3). The radius of cavity was set to 6.35 mm and a gas density 7.874 x 10(exp -5) g/cm(exp 3). The response of the walled- and the wall-less counters were compared. The average quality factor Q(sub ave)(y) for trapped protons on STS-89 demonstrated the good agreement between the model calculations and flight TEPC data as shown. Using an integrated space radiation model (this includes the transport codes HZETRN and BRYNTRN, the quantum nuclear interaction model QMSFRG) and the resultant response distribution functions of walled-TEPC from Monte-Carlo track simulations, we compared model calculations with walled-TEPC measurements from NASA missions in LEO and made predictions for the lunar and the Mars missions. The Q(sub ave)(y) values for the trapped or the solar protons ranged from 1.9-2.5. This over-estimates the Qave(LET) values which ranged from 1.4-1.6. Both quantities increase with shield thickness due to nuclear fragmentation. The Q(sub ave)(LET) for the complete GCR spectra was found to be 3.5-4.5, while flight TEPCs measured 2.9-3.4 for Q(sub ave)(y). The GCR values are decreasing with the shield thickness. Our analysis for a proper interpretation of data supports the use of TEPCs for monitoring space radiation environment.

Kim, Myung-Hee Y.↗
DOE OSTI2024

Energy Dependent Fission Product Yields (2nd year Milestone and Deliverables)

Introduction to the project: One of the Laboratory missions is to provide consistent, high-precision fission product; yield data critical for testing fission models and maintaining the safety and security of the nation’s nuclear weapons stockpile. However, high-quality, energy-differential fission product yield data is missing for certain actinides and neutron energies important to constrain the new U.S. Nuclear Data Program evaluation effort; It has been shown that the reactor antineutrino anomaly may be at least partially caused by roughly 20 fission products. The fission product yield data is missing or incomplete for many of these isotopes, thus it is necessary to accurately determine these values to better constrain the anomaly; Aitor Bracho is measuring very short-lived (seconds to minutes) fission product yields of 235 U and 239 Pu using monoenergetic neutron beams at E n = 60 and 560 keV; Aitor Bracho is using a direct approach utilizing a state-of-the-art rabbit transfer system, superior HPGe detector, and digital acquisition systems for fission decay measurements. The goals of this project: lop experimental capabilities and data analysis techniques to carry out the gamma-ray spectra analysis necessary for fission product yield calculation; Provide high-precision and energy-dependent fission product data supporting fission theory, neutrino physics, and applied physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

The current status of inelastic and capture Gamma-ray production evaluations in translated ENDF-VIII.0 GNDS files and recommended remediation actions

This report provides the status of discrete nuclear levels and inelastic gamma-ray production for neutron induced reactions in current ENDF-VIII.0 evaluations for the isotopes in the GRIN project. Different categories of issues are identified after a comparison with information from “adopted” ENSDF files. Improvement strategies are given and recommendations are described. We have included a similar, but more limited, analysis for thermal capture data where EGAF and ENSDF thermal libraries are considered. In addition, we provide a validation plan to employ different transport code simulations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Analytical Functions for 200 West Pump-and-Treat SCADA Sensor Data

Historical operations at the U.S. Department of Energy’s Hanford Site included disposal of waste fluids to the subsurface in the 200 West Area on the Hanford Central Plateau. Subsequent infiltration of fluids has resulted in groundwater contamination with carbon tetrachloride, nitrate, uranium, technetium-99, and other contaminants. A pump-and-treat (P&T) system, with an extraction/injection well network and an aboveground treatment plant, was implemented as part of interim and final remedies in the 200 West area. The HYPATIA single-page web application (part of the SOCRATES suite) is being developed to provide access to and analysis of chemistry and treatment facility sensor data for this 200 West P&T system. For the web application, analytical algorithms were developed to perform summing, differencing, smoothing, outlier detection, change-point detection, mass flow rate, and injectivity calculations on the data. Candidate algorithms were identified and tested, with the best-performing algorithms then assembled for implementation in HYPATIA. Because HYPATIA is hosted on the Amazon Web Services (AWS) cloud computing platform, algorithms were implemented in a back-end AWS Lambda function that can be called by the HYPATIA front end. The Lambda function applies the requested data processing to specified data via functions written in R, Python, and JavaScript. Development, testing, and review of the data analysis algorithms was completed under an NQA-1 quality program. This new HYPATIA functionality will provide information to support site decisions regarding P&T system performance and optimization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Thermal Spectrum Molten Salt-Fueled Reactor Reference Plant Model

This report details a fully coupled neutronics thermal hydraulics reference plant model for a thermal spectrum molten salt-fueled reactor. The Multiphysics model is developed on the Nuclear Regulatory Commission's Comprehensive Reactor Analysis Bundle available on the Idaho National Laboratory high-performance computer, which natively and seamlessly couples Griffin and Sam codes of the Multiphysics Object-Oriented Simulation Environment (MOOSE) based applications. Griffin provides the reactor physics capabilities, k-eigenvalue, delayed neutron precursor concentrations, and transient solutions for stationary and flowing fuels. Sam provides the solution of mass, momentum, and energy conservation equations of the whole system with a set of system components for one-dimensional single-phase flow. The neutronics feedback model relies primarily on fuel and moderator temperatures, fuel salt density, and delayed neutron precursors distributions. This report presents our results for the coupled steady state core and an unprotected loss of flow event. Although this model is prototypical regarding capabilities in the Comprehensive Reactor Analysis Bundle, its results are consistent with experimental data of the pump transient tests that were performed at zero power of the Molten Salt Reactor Experiment (MSRE).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

High sensitivity measurement of femtogram-level 238 Pu by thermal ionization mass spectrometry with correction of background 238 U

Low-concentration plutonium isotopic measurements provide a valuable fingerprint to identify possible material origins in the context of environmental contamination monitoring, environmental tracing, nuclear safeguards, nuclear forensics, and treaty monitoring. Thermal ionization mass spectrometry (TIMS) has long been recognized as one of the most sensitive techniques for plutonium isotopic measurement, but has not generally been utilized for 238 Pu determination, especially at low concentrations, due to an isobaric interference from 238 U, prevalent as background. Here, this work demonstrates a new analytical technique for correction of background 238 U from the 238 Pu counts collected during a TIMS analysis. The technique takes advantage of the higher ionization temperature of U relative to Pu and uses a 235 U tracer added after plutonium purification chemistry for 238 U semi-quantitative determination. This technique has been successfully applied to the determination of 238 Pu concentration and the 238 Pu/ 239 Pu ratio in sample types including single-isotope standards, isotopic certified reference materials, matrix-containing environmental reference materials, and simulated nuclear detonation debris. The results have been directly compared on a sample-by-sample basis to alpha spectrometry results. The detection limit by this new technique is 0.23 fg 238 Pu, which was previously unachievable by mass spectrometry and is possible in around 1 h of analysis time post-chemistry compared to weeks of required counting time by alpha spectrometry (at the same atom amount). We also present original, high precision data for Pu isotope concentrations in IAEA-384, Fangataufa Sediment, including 242 Pu results for the first time. The new technique allows measurement of a key isotope, 238 Pu, that was previously not measured in small environmental or nuclear forensics collections.

238Pu↗
DOE OSTI2025

Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs

The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Coupling RELAP5-3D to BISON

This report details two approaches for coupling the BISON nuclear fuel performance code with RELAP5-3D. Both approaches are shown to work well. The first is a Python-based approach, and the second combines the two applications into one executable with data passed in memory from one library to the other. This second coupling approach forms the basis for analysis of complex loss of coolant scenarios and enables future calculations of modeling uncertainties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Simultaneous isotopic analysis of fission product Sr, Mo, and Ru in spent nuclear fuel particles by resonance ionization mass spectrometry

Abstract Fission product Sr, Mo, and Ru isotopes in six 10-μm particles of spent fuel from a pressurized water reactor were analyzed by resonance ionization mass spectrometry (RIMS) and evaluated for utility in nuclear material characterization. Previous measurements on these same samples showed widely varying U, Pu, and Am isotopic compositions owing to the samples’ differing irradiation environments within the reactor. This is also seen in Mo and Ru isotopes, which have the added complication of exsolution from the UO 2 fuel matrix. This variability is a hindrance to interpreting data from a collection of particles with incomplete provenance since it is not always possible to assign particles to the same batch of fuel based on isotopic analyses alone. In contrast, the measured 90 Sr/ 88 Sr ratios were indistinguishable across all samples. Strontium isotopic analysis can therefore be used to connect samples with otherwise disparate isotopic compositions, allowing them to be grouped appropriately for interpretation. Strontium isotopic analysis also provides a robust chronometer for determining the time since fuel irradiation. Because of the very high sensitivity of RIMS, only a small fraction of material in each of the 10 μm samples was consumed, leaving the vast majority still available for other analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Machine Learning in Nuclear Thermal Hydraulics, Part 2: Opportunities and Perspectives

This paper summarizes the issues in nuclear thermal hydraulics (NTH), mainly from the three named aspects, and illustrates potential opportunities of machine learning (ML) applications in dealing with these issues. These issues are problems in simultaneously realizing computational efficiency and accuracy of system thermal hydraulic (STH) modeling and simulation, uncertainty in the scaling analysis for the best-estimate calculations of transient and accident analysis, and difficulties in optimal data production and usage for the development and assessment of evaluation models. Due to ML uncertainty, data insufficiency, and lack of cognition about how to efficiently incorporate knowledge and data, challenges of adapting ML techniques still exist. Accordingly, several perspectives are proposed to provide insights for ML applications in solving these issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Three dimensional low-mode areal-density non-uniformities in indirect-drive implosions at the National Ignition Facility

To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and high areal density at stagnation. The size of the driver and scale of the capsule required can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Significant 3D low mode asymmetries, however, are commonly observed in indirect-drive implosions and reduce the coupling of shell kinetic energy to the hotspot. To better quantify the magnitudes and impacts of shell density asymmetries, we have developed new analysis techniques and analytic models [Hurricane et al., Phys. Plasmas 27(6), 062704 (2020)]. To build confidence in the underlying data, we have also developed an analytic neutron transport model to cross-compare two independent measurements of asymmetry, which shows excellent agreement across shots for mode-1 (ℓ = 1). This work also demonstrates that asymmetry can introduce potential sampling bias into down-scattered ratio measurements causing the solid-angle-average and uncertainty-weighted-average down-scattered ratios to differ significantly. Diagnosing asymmetries beyond mode-1 (ℓ > 1) presents significant challenges. Using new diagnostic instruments and analysis techniques, however, evidence of significant Legendre mode P2 (ℓ = 2, m = 0) and additional 3D asymmetries (ℓ > 1, m ≠ 0) are beginning to emerge from the high precision activation diagnostic data (real-time nuclear activation detectors) and down-scattered neutron imaging data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Advanced Reactor Control and Operations (ARCO): A University Research Facility for Developing Optimized Digital Control Rooms

The Advanced Reactor Control and Operations (ARCO) facility was constructed in January 2018 to serve as a test bed for advanced reactor control rooms and operator support systems. Since then, it has supported human-machine interface user experience research, fault detection and mitigation technology development, control room concept of operations development, and remote operations research. ARCO serves as the control room for the Compact Integral Effects Test (CIET) facility, which replicates the primary-side flow paths and thermal-hydraulic behavior of a fluoride-salt-cooled high-temperature reactor (FHR) using simulant fluids and scaling principles. New reactor designs feature different operating conditions and scenarios than those in existing reactors. ARCO supports the research and development of digital tools for operator communications, intuitive real-time data analysis, online health monitoring and prognostics, and control room cybersecurity. By integrating these different technologies, ARCO acts as a prototypical control system to iteratively develop methods and tools of operation in advanced small modular nuclear reactors. This paper describes the features of and challenges to operating advanced small modular reactors underlying the design basis for ARCO and its operator support systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗