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DOE OSTI2025

The HTR-Proteus Benchmark: Analysis and Use as a Verification and Validation Case

This presentation outlines the evaluation and application of the HTR-Proteus benchmark as a verification and validation (V&V) case for advanced reactor modeling tools. The work supports the U.S. Department of Energy’s HALEU Availability Program (HAP) and the joint DOE/NRC DNCSH project, which aims to reduce criticality safety uncertainties in commercial-scale HALEU fuel cycle and transportation systems. The HTR-Proteus experiments, conducted at the Paul Scherrer Institute, provide high-fidelity data for TRISO-fueled, graphite-moderated pebble bed reactors with high neutron leakage—conditions relevant to HALEU transport scenarios. This study focuses on Cores 4.2 and 4.3 of the HTR-Proteus benchmark, analyzing key sources of uncertainty including pebble packing, TRISO particle positioning, and core height. Using Project Chrono for realistic pebble geometries and Serpent, SHIFT, and MCNP for neutronics simulations, the study quantifies the impact of these uncertainties on the effective multiplication factor (keff). Results show that a sample size of 110 pebble configurations is sufficient to converge keff, with ±30 pcm uncertainty due to packing randomness. TRISO positioning and core height variations also significantly influence keff, highlighting the importance of detailed modeling in V&V efforts. The benchmark serves as a valuable test case for validating the Griffin reactor physics code and improving confidence in HALEU system simulations.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2010

The Importance of HRA in Human Space Flight: Understanding the Risks

Human performance is critical to crew safety during space missions. Humans interact with hardware and software during ground processing, normal flight, and in response to events. Human interactions with hardware and software can cause Loss of Crew and/or Vehicle (LOCV) through improper actions, or may prevent LOCV through recovery and control actions. Humans have the ability to deal with complex situations and system interactions beyond the capability of machines. Human Reliability Analysis (HRA) is a method used to qualitatively and quantitatively assess the occurrence of human failures that affect availability and reliability of complex systems. Modeling human actions with their corresponding failure probabilities in a Probabilistic Risk Assessment (PRA) provides a more complete picture of system risks and risk contributions. A high-quality HRA can provide valuable information on potential areas for improvement, including training, procedures, human interfaces design, and the need for automation. Modeling human error has always been a challenge in part because performance data is not always readily available. For spaceflight, the challenge is amplified not only because of the small number of participants and limited amount of performance data available, but also due to the lack of definition of the unique factors influencing human performance in space. These factors, called performance shaping factors in HRA terminology, are used in HRA techniques to modify basic human error probabilities in order to capture the context of an analyzed task. Many of the human error modeling techniques were developed within the context of nuclear power plants and therefore the methodologies do not address spaceflight factors such as the effects of microgravity and longer duration missions. This presentation will describe the types of human error risks which have shown up as risk drivers in the Shuttle PRA which may be applicable to commercial space flight. As with other large PRAs of complex machines, human error in the Shuttle PRA proved to be an important contributor (~12 percent) to LOCV. An existing HRA technique was adapted for use in the Shuttle PRA, but additional guidance and improvements are needed to make the HRA task in space-related PRAs easier and more accurate. Therefore, this presentation will also outline plans for expanding current HRA methodology to more explicitly cover spaceflight performance shaping factors.

Hamlin, Teri↗

DOE OSTI2025

Molten Salt Corrosion Tests of Additively Manufactured Stainless Steel 316H

Molten salt reactors (MSRs) have drawn considerable interest due to their favorable safety features, high thermal efficiency, and compatibility with different fuel cycles. Yet, the success of MSRs hinges critically on the performance of structural materials to be used in these aggressive molten salt environments, where corrosion and material compatibility remain primary challenges to long-term reliability. Additively manufactured (AM) nuclear structural materials prompt the use of novel geometries and compositions to enhance material performance and reduce costs of constructing MSRs. The rapid solidification conditions inherent to AM processing impart distinctive microstructural features, including cellular sub-structures, dislocation densities, residual stress, and oxide inclusions, which can influence material performance in MSR components. While the mechanical properties of AM stainless steels have been widely studied, their corrosion behavior, particularly in molten salt environments, has received far less attention. Addressing these needs, the Advanced Materials and Manufacturing Technologies (AMMT) program provides a framework for systematically evaluating how unique microstructures produced by AM processes influence the performance of these materials in these demanding environments and for developing reproducible testing workflows that can support future code qualification efforts and standards development. Bridging this knowledge gap is essential for assessing the viability of AM alloys in MSRs and informing qualification strategies. A further challenge is the absence of standardized protocols for molten salt corrosion testing. Accordingly, this report provides an account of the corrosion evaluation of AM 316H stainless steel in NaCl 2 -MgCl 2 molten salt at 550 °C, with exposure times of 100 and 500 hours. It documents the experimental procedures implemented under the AMMT program, including salt preparation, exposure protocols, and post-test characterization methods, to establish reproducibility and transparency. Importantly, the study examines AM 316H samples in the as-fabricated condition, directly reflecting the surface state most relevant to engineering applications, and compares their behavior to machine-cut surfaces. Overall, preliminary evaluations have noted that surface conditions (e.g. morphology, contamination, etc.) have a noticeable impact on the corrosion resiliency. The impact of the corrosion is difficult to detect at 100h, unless, in the case of AM 316H, the specimen surface is decontaminated. After 500 h, as-fabricated surfaces of AM and wrought 316H display evidence of general versus preferential corrosion attack, respectively. Both AM and wrought 316H machine-cut surfaces exhibit a continuous Cr depletion zone, evident of general corrosion. While the estimated extent of corrosion appears within the same order of magnitude regardless of the surface condition, it is apparent that more predictable behavior is observed on machine-cut surfaces. Nonetheless, further investigation is necessary to fully elucidate the corrosion mechanism under these conditions.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Thermal Neutron Scattering Law Evaluation for Zirconium Carbide and Critical Mass Calculations

Zirconium carbide (ZrC) is a candidate material for use in advanced high temperature reactors, including space nuclear thermal propulsion applications. Thermal neutron scattering laws (TSLs) are generated for carbon bound in ZrC, C(ZrC), and zirconium bound in ZrC, Zr(ZrC), using ab initio lattice dynamics methods. These evaluations are to be submitted for inclusion in ENDF/B-VIII.1 and use the incoherent approximation for inelastic scattering as well as the new mixed elastic scattering treatment. The application of disordered alloy theory is introduced to appropriately capture the isotopic composition of Zr and C in the elastic scattering cross section. Localized higher energy vibrations in the C(ZrC) phonon density of states that are separated from lower energy modes result in quantized oscillations in the inelastic contributions to the TSL with a significant likelihood of large energy down-scattering and up-scattering interactions, where the latter increases in probability with temperature. The quanta of energy transfer during neutron thermalization is substantially greater than classically expected within the thermal neutron energy range. MC21 critical mass calculations of ZrC mixtures with high-enriched uranium demonstrate an impact of the TSLs when compared to free-gas treatment for 235 U concentrations less than 0.2 g/cm 3 . Additional MC21 critical mass calculations with homogenous mixtures of ZrC and reactor-grade graphite also demonstrate sensitivity to the ZrC TSL for thermal spectrum driven fission systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Uncertainty Quantification of a Light Water Pulsed-Neutron Die-Away Experiment to Thermal Neutron Scattering Laws

Thermal neutron scattering laws are important nuclear data for many nuclear science and engineering applications. Validation helps to ensure that a thermal neutron scattering law has a high quality and often employs critical benchmarks as integral experiments. Recently, pulsed-neutron die-away benchmarks have been used as an experiment to validate thermal neutron scattering laws. Herein, we evidence how this alternative integral experiment has a high sensitivity to these nuclear data by performing an uncertainty quantification analysis. The analysis randomly sampled the nuclear model parameters associated with hydrogen bound in light water thermal neutron scattering law and sampled other nuclear data that influenced the experiment’s integral parameter (e.g., elastic scattering, absorption in hydrogen and oxygen) from their respective covariance matrices. The thermal neutron scattering law caused an uncertainty in the integral parameter that reached 2.67%, which exceeds by an order of magnitude the uncertainties induced in commonly used thermal solution critical benchmarks. The validation performed here, although limited due to a poor description of the historical experiment, indicated that the ENDF/B-VIII.0 thermal neutron scattering law well predicted the integral parameter. These results motivate further benchmark and validation efforts using pulsed-neutron die-away experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRSDate not supplied

Molten Salt Lattice Confinement Fusion (LCF) Fast Fission Reactor for Lunar and Planetary Surface Power

Molten salt fission reactors (MSR) have been suggested for lunar and planetary surface power systems. They have the advantage of operating at high temperature, for efficient thermal-electrical conversion, low pressure, long-lived with high nuclear fuel burnup. MSR are often designed to breed fissile 233U from natural 232Th by neutron capture and  decay via: 232Th(n,)233Th(,)233Pa(,)233U Unfortunately, this process requires 233Pa isotope separation and segregation to decay to 233U. This requirement prevents additional neutron capture that interferes with 233U breeding. Instead, Wooley’s sub-critical, fast fission, molten salt reactor would use externally generated tokamak fusion neutrons1,2 to fission all actinides.We propose a simpler fusion-fast-fission sub-critical reactor that generates fast neutrons in situ from lattice confinement fusion (LCF) to fission fertile and fissile actinides. This hybrid reactor doesn't require enriched 235U fissile pins to initiate fis-sion reactions, nor 233Pa separation and segregation during operation. Like Wooley’s, this hybrid reactor “burns” natural uranium (238U) or thorium (232Th) which avoids uranium enrichment and additional fissile material launch safety and security costs. The LCF neutron source is initiated by bremsstrahlung photoneutrons (Fig. 1)3 or isotopic neutron sources in electron-screened lattices (Fig. 2)4,5. Alternatively, the electrolytic Pd-deuterium co-deposition6protocol fast fissions7 both 232Th and 238U. However, an aqueous electrolyte-based system, without pressurization similar to conventional pressurized water fission reactors, is incapable of high temperatures due to the boiling point of the electrolyte slightly over 100 C. Molten salts can be used instead as was demonstrated at the University of Hawaii8 using a variety of Ni and Pd cathodes in lithiated, hydrided and deuterated salts. These salts have melting points often exceeding 500C making them suitable to efficiently produce electrical power9 through either Advanced Stirling Genera-tors (< 100 kWe) or closed-Brayton Cycle (> 100 kWe). This hybrid reactor could power a wide range of lunar or Martian applications from unmanned in-struments, to charging vehicles and entire facilities such as human habitats or in situ resource utilization. The power conversion cycles are Carnot Cycle limited, but generally 30% efficient at best. However, waste heat on the moon or Mars is important to surviving either two-week lunar nights or Martian nights as well as providing process heat for mineral extraction and “living off the land”

Lawrence Forsley↗

DOE OSTI2023

The STAT7 Code for Statistical Propagation of Uncertainties in Steady State Thermal Hydraulics Analysis of Plate Fueled Reactors

The STAT7 software was developed to perform steady-state thermal hydraulic analyses. Application of the software is for non-power research and test reactors, including conversion to low-enriched uranium fuel of U.S. High-Performance Research Reactors such as MITR-II. Since it can be necessary to repeat analysis during fuel reloading, STAT7 accommodates flexibility in analyzing many realistic aspects of reactor fuel management. STAT7 uses a Monte Carlo approach to model common fabrication parameters and other key reactor analysis uncertainties required for research and test reactor thermal hydraulic analyses. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux or departure from nucleate boiling or onset of flow instability; but additional margin is obtained by basing the limiting safety settings on avoiding onset of nucleate boiling. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one lateral stripe of a fuel element. The stripes run the length of the fuel, from the bottom to the top. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis can be performed by analyzing the margin to onset of nucleate boiling and onset of flow instability for each axial node of each stripe of each plate of each element in the core.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Application of Machine Learning Algorithms to Identify Problematic Nuclear Data

In this work we aim to show that Machine learning algorithms are promising tools for the identification of nuclear data that contribute to increased errors in transport simulations. We demonstrate this through an application of a machine learning algorithm (Random Forest) to the Whisper/MCNP6 criticality validation library to identify nuclear data that are associated with an increase of the bias (simulated - experimental $k_{eff}$) in the calculations. Specifically, the $k_{eff}$ sensitivity profiles (w.r.t. nuclear data) of 233 U solution benchmarks are used to predict the bias and Shapley Additive Explanations (SHAP) are used to explain how the sensitivities are related to the predicted bias. The SHAP values can be interpreted as sensitivity coefficients of the machine learning model to the $k_{eff}$ sensitivities which are used to make predictions of bias. Using the SHAP values we can identify specific subsets of nuclear data which have the highest probability of influencing bias. We demonstrate the utility of this method by showing how SHAP values were used to identify an inconsistency in the 19 F inelastic scattering nuclear data. The methodology presented here is not limited to transport problems and can be applied to other simulations if there are experimental measurements to compare against, simulations of those experimental measurements, and the ability to calculate sensitivities of the model output with respect to the data inputs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Liquid–Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl 3 ) Enabled by Machine Learning Interatomic Potentials

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl 3 molten salt across varied thermodynamic conditions (T = 473–613 K and P = 2.7–23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid–vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl 3 molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl 3 that are within 10 K (∼3%) and 0.03 g/cm 3 (∼7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl 3 system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally determined molten salt structure comprising Al 2 Cl 6 dimers, as validated using Raman spectra and neutron structure factor. Furthermore, the PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl 3 system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict temperature correlations for liquid and vapor densities in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.

Ab initio molecular dynamics↗

DOE OSTI2022

Horizontal Split Table Conceptual Design for Advanced Reactor Validation

Oak Ridge National Laboratory and Lawrence Livermore National Laboratory are collaborating to develop a conceptual design for a horizontal split table for use in performing critical experiments. The goal of this design effort is to provide nuclear data testing and validation capabilities for advanced reactors such as pebble-bed high-temperature gas-cooled reactors, molten salt reactors, and heat pipe microreactor, but it could also be used for the current generation of reactors. The first concept being explored for the horizontal split table, a pebble-bed design based on the HTR-10 reactor, is described in this paper. A critical configuration corresponding to a footprint of about 4.5 m 2 was determined with SCALE/KENO-VI to fit the planned dimensions of the horizontal split table. The similarity of the pebble-bed design and the HTR-10 reactor application was assessed using SCALE/TSUNAMI, and a similarity coefficient $c_k$ of 0.9982 was obtained, proving that the concept will be useful for nuclear data validation and assimilation of pebble-bed type advanced reactors. In the proposed design, the materials with the highest $k_{eff}$ sensitivity are graphite and uranium, demonstrating that particular care must be given to carbon-related cross-section data. The effect of mechanical uncertainties between the fixed and moving tables was also assessed by calculating the reactivity change caused by vertical and horizontal gaps, as well as angular and torsion offsets between the two sides of the horizontal split table concept. The highest relative changes on the concept’s reactivity were caused by angular perturbations. The same analysis process is currently being used to create a molten salt advanced reactor type horizontal split table concept based on the Molten Salt Reactor Experiment (MSRE).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Neutron Leakage Spectra of the EUCLID Experiment [Abstract]

Integral experiments with sub-critical and critical configurations of special nuclear material are performed in support of nuclear data validation and adjustment. Different nuclear data evaluations may have different values for individual cross sections due to uncertainties in (or lack of) differential experiments, but compensating errors in these data sets can lead to the same k eff results for one application while vastly different for another application. One example of this is 239 Pu, where both ENDF/B-VIII.0 and JEFF-3.3 correctly compute k eff of the Jezebel critical assembly, but the individual contributions from each reaction are vastly different. To help resolve this specific case, the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project used machine learning to design a set of experiments to help resolve the compensating errors in 239 Pu. A total of six responses were measured during the experimental campaign, which constrain the data in ways that k eff alone cannot and will be used for adjustment of the nuclear data. One of these responses is the neutron leakage spectrum, which recent work has shown to be useful for constraining the prompt fission neutron spectrum and inelastic scattering. The neutron leakage spectra were measured utilizing a 3 in. right cylinder EJ-301D detector. The measured signal in the detector was deconvoluted using spectrum unfolding techniques, which are presented and compared to simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Verification of Upcoming MCNP Features For Estimating Nuclear Data Sensitivities in Fixed Source Simulations [Abstract]

Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Thermal neutron scattering measurements and modeling of yttrium-hydrides for high temperature moderator applications

In this work, thermal neutron scattering measurements of yttrium hydride from 5 to 1,200 K were conducted to determine the change in inelastic scattering as a function of temperature and to probe for anharmonic effects. Additionally, measurements on samples of YH x from x = 1.62 to 1.90 were done to determine the effects of varying hydrogen concentration on the inelastic spectra. Changes in temperature affected the inelastic spectra in unanticipated ways, indicating that there are anharmonic effects, whereas hydrogen concentration did not significantly affect the inelastic spectra. These measurements were compared against the ENDF/B-VIII.0 thermal scattering files of YH 2 , as well as new thermal scattering files created by using the stochastic temperature-dependent effective potential (s-TDEP). Both libraries were found to be in good agreement with the experimental data at lower temperatures. At higher temperatures, the s-TDEP method is better at predicting the experimentally observed softening of phonon modes in acoustic and optical regions.

36 MATERIALS SCIENCE↗

NASA NTRS2015

Reliability and Probabilistic Risk Assessment - How They Play Together

Since the Space Shuttle Challenger accident in 1986, NASA has extensively used probabilistic analysis methods to assess, understand, and communicate the risk of space launch vehicles. Probabilistic Risk Assessment (PRA), used in the nuclear industry, is one of the probabilistic analysis methods NASA utilizes to assess Loss of Mission (LOM) and Loss of Crew (LOC) risk for launch vehicles. PRA is a system scenario based risk assessment that uses a combination of fault trees, event trees, event sequence diagrams, and probability distributions to analyze the risk of a system, a process, or an activity. It is a process designed to answer three basic questions: 1) what can go wrong that would lead to loss or degraded performance (i.e., scenarios involving undesired consequences of interest), 2) how likely is it (probabilities), and 3) what is the severity of the degradation (consequences). Since the Challenger accident, PRA has been used in supporting decisions regarding safety upgrades for launch vehicles. Another area that was given a lot of emphasis at NASA after the Challenger accident is reliability engineering. Reliability engineering has been a critical design function at NASA since the early Apollo days. However, after the Challenger accident, quantitative reliability analysis and reliability predictions were given more scrutiny because of their importance in understanding failure mechanism and quantifying the probability of failure, which are key elements in resolving technical issues, performing design trades, and implementing design improvements. Although PRA and reliability are both probabilistic in nature and, in some cases, use the same tools, they are two different activities. Specifically, reliability engineering is a broad design discipline that deals with loss of function and helps understand failure mechanism and improve component and system design. PRA is a system scenario based risk assessment process intended to assess the risk scenarios that could lead to a major/top undesirable system event, and to identify those scenarios that are high-risk drivers. PRA output is critical to support risk informed decisions concerning system design. This paper describes the PRA process and the reliability engineering discipline in detail. It discusses their differences and similarities and how they work together as complementary analyses to support the design and risk assessment processes. Lessons learned, applications, and case studies in both areas are also discussed in the paper to demonstrate and explain these differences and similarities.

Safie, Fayssal↗

DOE OSTI2023

Reactivity Coefficient Measurements to Aid in Reducing Compensating Errors in Plutonium Nuclear Data

Compensating errors between several nuclear data observables in a nuclear data library can adversely impact application simulations. The primary goal of the EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) is to reduce compensating errors between fast (0.1–5 MeV) 239Pu nuclear data for prompt fission neutron spectra (PFNS), average prompt fission neutron multiplicities, and neutron induced fission, capture, elastic, and inelastic cross sections. This work will focus on the design and execution of void reactivity coefficient measurements in the EUCLID experiment, performed on the Planet vertical lift critical assembly machine at the National Criticality Experiments Research Center (NCERC). Two different base configurations were designed and measured, one with high neutron leakage, and one with low neutron leakage. Both were primarily made up of plutonium metal (Zero Power Physics Reactor plates) without interstitial moderators and reflected by half-inch aluminum. Design optimization showed that void reactivity coefficient measurements in three locations per configuration was most impactful to reduce nuclear data uncertainties due to the varying impacts from elastic and inelastic scattering, as well as fission and capture. The locations for measurements were chosen based on preliminary studies which balanced measurement uncertainty and measurement practicality. The measurements were also selected to have sensitivities maximally complementary to previous arrangements. Comparisons across nuclear data libraries highlight the potential impact.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Draft Methodology for Cybersecurity Analysis for Adoption of Wireless Technology in Nuclear Power Plants

The report is delivered as DOE-NE Cybersecurity Program Milestone M2CT-22IN1104014-Industry cybersecurity guidance adoption status. The milestone is specifically to document the process and the status of the efforts towards developing guidance in support of industry’s adoption of wireless communication technologies. To transform the operation of nuclear power plants, including domestic light-water reactors, advanced reactors, microreactors, and fission battery, for grid and non-grid applications, secure and resilient wireless capabilities are required. In addition, to realize new operational concepts such as autonomous operation and remote monitoring, advanced sensor, and instrumentation, wireless technology is essential. However, industry implementation is low, and there is no technical basis for how to understand and address potential risks for wireless communications for critical plant functions. A methodology with a technical basis for implementing secure wireless communication is crucial. This wireless adoption methodology is intended to assist a licensee in identifying an appropriate technological approach for securing wireless communications in nuclear power plant. However, this methodology does not provide guidance for addressing wireless design criteria or for addressing all the cybersecurity commitments in licensees’ cybersecurity plans (CSPs) including addressing security impact analysis that a licensee must perform before an update to a Critical Digital Asset (CDA). This methodology guides a licensee through a process of evaluating a proposed wireless implementation and drafting a plant change notification for an example use case. The example is a generic/nonproprietary version of the first test case of the methodology, which, if successfully implemented, is part of the consideration for protecting other functions’ use of wireless in a cybersecure manner.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗