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DOE OSTI2024

Probabilistic Hydropower Flexibility Valuation: Case Studies for Boating Flow Regime

The optimal scheduling of hydropower generation holds significant importance to power system operation. The unique requirements of environmental constraints and the power system, depending on their respective objectives, demand distinct flow patterns. While power system stakeholders strive to optimize revenue in electricity markets, stakeholders from boating recreation seeks to identify flow ranges that optimize the boating experience. In pursuit of a win-win solution, this study aims to reconcile the interests of various stakeholders in hydropower scheduling. The maximum revenue from day-ahead electricity market is explored through an optimization process considering both plant operation constraints, boating flow constraints, water availability, and market prices. Results of real world case studies at a river in California show that the proposed approach can achieve dual objectives: maximizing market revenue while addressing boating recreation necessities. In addition, as the accuracy of electricity price forecasting and flow forecasting increase, the optimal revenue becomes increasingly advantageous to hydropower plant operators.

13 HYDRO ENERGY↗

DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Hazards and Probabilistic Risk Assessments of Advanced Nuclear Reactors Coupled with Industrial Facilities

This report provides a roadmap and tool kit for site specific risk assessments across a broad range of industrial customers co-located with advanced nuclear power plants (ANPP) that are not currently built and operating in the U.S. This report builds upon the body of work sponsored by the Department of Energy (DOE) Integrated Energy Systems Pathway that has produced industrial requirements studies and techno-economic assessments on the topics of feasibility of ANPP supported industrial processes. This report also leverages the DOE Light Water Reactor Sustainability (LWRS) program that has presented hazards assessment and generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors (LWRs) co-located with hydrogen production facilities. Many of the hazard assessments and risk assessments performed for the LWRS report are agnostic to whether the nuclear reactor is an ANPP or were adapted to the ANPP focus. The report performs hazards assessments to include industrial facilities: an oil refinery, a methanol plant, a synthetic fuel (synfuel) plant, the production of synthetic gas (syngas) as part of the methanol and synfuel plants, wood pulp and paper mills, and hydrogen production. Hydrogen production facilities are assessed in depth through prior reports in the LWRS program and the results are leveraged in this report. All these facilities are specified through industrial process and requirements research performed by national laboratories, universities, and interaction with industry. Many of the processes used in this report are pre-conceptual designs to use for decarbonization of the current technology facilities. A process of failure modes and effects analysis (what can go wrong) and accidentology (what has historically gone wrong) was used to determine the hazards presented to the nuclear power plant by the addition of the HES and the industrial customer. Chemical properties of feedstocks and products are summarized as part of the hazards assessment. Example analysis procedures are provided for each of the hazard types identified. These deterministic analyses can be used to assess adherence to licensing criteria. They can also be used to meet other safety goals like protection of the public, workers, or industrial facility equipment. A modular high temperature gas-cooled reactor (MHTGR) PRA only existing on paper was modeled and verified in modern PRA software. This will provide a tool for representative ANPP probabilistic analyses for future research.

10 SYNTHETIC FUELS↗

DOE OSTI2026

A Probabilistic Approach to Load Modeling for Central HVAC Systems in Large Commercial Buildings for Retrofit Decisions Under Uncertainty

Retrofitting central HVAC systems in large commercial buildings with advanced technologies like heat recovery chillers (HRCs) offers a significant opportunity to enhance energy efficiency. However, analyzing these retrofits is challenging with traditional whole-building simulation tools, which require intensive calibration and struggle to model innovative system configurations and controls. To overcome these limitations, this study proposes a load profilebased retrofit analysis framework that provides better decisions under uncertainty. The main focus of this paper is the development of a probabilistic load profile model that can be used in the framework by using exploratory data analysis (EDA) of measured building data to properly quantify its inherent variability. A non-parametric Gaussian Process (GP) model was employed to capture the time- and weather-dependent characteristics of the heating load while explicitly modeling its uncertainty. The model's effectiveness is demonstrated through strong predictive performance on unseen data and physically interpretable insights into load behavior. This data-driven, probabilistic load profile serves as a robust and flexible input for subsequent system simulations, enabling a more confident and statistically sound analysis of retrofit potential.

Ham, S W↗

DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗

DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

True random number generation using the spin crossover in LaCoO 3

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO 3 that is electrically biased within its spin crossover regime. The LaCoO 3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Probabilistic Analysis of Uncertainty in ATRC Flux Profiles

ATRC is a replica of the larger ATR design and is used to conduct research and obtain data such as flux measurements, excess reactivity, and loading requirements before being loaded into ATR. One method for determining the impact an experiment will have at ATR is by looking at the axial flux profile along the fuel rod in the corresponding ATRC experiment; however, flux wand measurements includes large amounts of variation which makes drawing conclusions from the data difficult. This poster describes a definitive method to propagate the uncertainty from ATRC measurements using Python code.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Development and Demonstration of a Risk Assessment Approach for Approval of a Transportation Package of a Transportable Nuclear Power Plant for Domestic Highway Shipment

The U.S. Department of Defense (DoD) Strategic Capabilities Office (SCO) has tasked PNNL to address the regulatory challenges associated with confirming the safe transport of Transportable Nuclear Power Plants (TNPPs) containing irradiated nuclear fuel. A previous report—Proposed Risk-Informed Regulatory Framework for Approval of Microreactor Transportation Packages (PNNL-31867)—determined that the expected radioactive inventory in the irradiated fuel of a TNPP would likely require shipment in an NRC-approved Type B package (or spent nuclear fuel cask) but that a TNPP “package” is unlikely to meet the entire suite of NRC requirements set forth in Part 71 of Title 10 of the Code of Federal Regulations (CFR) for a Type B package. It was therefore concluded that shipment of this initial TNPP transportation package, as well as possibly others, under existing regulations would likely require NRC approval using the 10 CFR 71.12 (“Specific exemptions”) process that relies on risk-informed decision making supported by quantitative risk assessment (i.e., Probabilistic Risk Assessment).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

A System-Level Cost Modeling Framework for Design for Remanufacturing: A Case Study of an Agricultural Machine Transmission

Remanufacturing offers significant environmental and economic benefits by restoring end-of-life products to as-new conditions. Although extensive research has been conducted on the topic, the adoption of remanufacturing practices remains limited across various industries. A primary barrier to broader implementation is the substantial upfront investment required, which necessitates reliable cost modeling to justify potential future savings. Most existing models treat components independently and ignore inter-component dependencies. We develop a probabilistic, system-level cost modeling framework that integrates reliability, reusability, and a dependency matrix to capture cascading effects across components over multiple life cycles. Our model identifies those critical components that maximize remanufacturing benefits across a product's many lives. A toy example and an industry case study (John Deere PowrQuad transmission subassembly) illustrate how design alternatives affect cumulative cost. Using a Monte Carlo simulation (MCS) to perform life cycle cost analysis on the system with different design changes, we show the normalized average cost savings after three remanufacturing cycles. Accounting for dependencies meaningfully alters cost projections and ignoring them underestimates accumulated cost by up to 20% in our examples. Furthermore, the results of our study confirm that accounting for component interdependencies is necessary to produce cost estimates that meet industry standards.

Life Cycle Analysis and Design↗

DOE OSTI2024

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Reassessing Double-Ended Guillotine Break Requirements: Evidence-Based Analysis of Regulatory Assumptions After Five Decades of Nuclear Operation

After five decades of nuclear power operation encompassing more than 20,000 reactor-years across 35 countries and 647 reactors, zero double-ended guillotine breaks (DEGBs) have been documented in commercial reactor coolant systems—despite DEGB being the fundamental design-basis assumption driving Emergency Core Cooling System (ECCS) sizing, structural protection requirements, and containment design specifications. This report examines the basis for DEGB requirements in nuclear power plant design. The DEGB postulate assumes the instantaneous, complete circumferential severance of the largest diameter pipes in reactor coolant systems, driving major design requirements under 10 Code of Federal Regulations 50.46, General Design Criterion 4 and containment design specifications. The United States (4,880 reactor-years) and France (2,505 reactor-years) contribute the largest operational datasets. Probabilistic assessments estimate direct DEGB occurrence probabilities with extremely low event frequencies, far below the 10-5/reactor-year thresholds typically used to define non-credible events in nuclear-safety analyses; i.e., events with probability this low fall into beyond-design-basis events. Current material-science knowledge demonstrates that the ductile steel materials used in nuclear piping systems exhibit stable crack-growth behavior fundamentally incompatible with instantaneous severance. International regulatory experience, particularly Germany’s comprehensive break-preclusion implementation, and successful leak-before-break (LBB) applications in almost all of U.S. pressurized water reactor units validate that alternatives can maintain safety performance while reducing economic burden. Current DEGB protection systems impose estimated lifetime costs of hundreds of millions of dollars per unit, over the life of a plant across the nuclear industry (including ongoing costs), representing substantial resource allocation toward scenarios with extremely low probability. Although this report acknowledges uncertainties regarding long-term aging effects, potential synergistic degradation mechanisms, and site-specific seismic considerations that warrant continued evaluation as regulatory policy evolves, there remains no documented evidence that a DEGB has occurred as a consequence of the conditions or mechanisms described in this report. This report acknowledges the Nuclear Regulatory Commission’s (NRC’s) recent efforts—outlined in the draft Interim Staff Guidance (ISG) NRC-DSS-ISG-2025-XX (“Treatment of Certain Loss-of-Coolant Accident Locations as Beyond-Design-Basis Accidents Draft Interim Staff Guidance”)—to reduce overly conservative requirements for large-break loss of coolant accidents through technical justifications and exemptions. However, extensive operating experience and validated methodologies—such as LBB and in-service inspection programs—demonstrate that the probability of a DEGB in reactor coolant-loop piping is extremely low, even under seismic conditions. The authors and reviewers of this report recommend that DEGB be removed as a design-basis event through formal rulemaking, rather than case-by-case exemptions, to better reflect credible failure modes, align with current data, and align with modern, risk-informed safety analysis.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Solar and Storage Integration in the U.S. Southeast: Implications for Resource Adequacy

Resource adequacy concerns may be very different in electricity systems that have higher levels of solar and storage, requiring changes to existing planning models. This study explores a novel approach to evaluating resource adequacy under future scenarios with higher solar and storage in the Southeast U.S. It uses NREL’s Probabilistic Resource Adequacy Suite (PRAS), a collection of probabilistic resource adequacy modeling tools, and compares results when interacting PRAS and a portfolio planning tool with a more traditional modeling approach. The results suggest that traditional models perform reasonably well with lower levels of solar PV, but at higher levels of solar probabilistic tools better capture the changes in resource adequacy concerns—such as winter energy availability—associated with higher solar systems. This is the final study in the Preparing Southeast Markets for Reliable and Affordable Integration of Solar into Operations and Planning project. Two prior reports can be found at: Solar and Storage Integration in the Southeastern United States: Economics, Reliability, and Operations. https://emp.lbl.gov/publications/solar-and-storage-integration Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region. https://eta-publications.lbl.gov/sites/default/files/2024-11/multiutility_fe_reserve_sharing_final.pdf

14 SOLAR ENERGY↗

DOE OSTI2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Uniformly decaying subspaces for error-mitigated quantum computation

Here, we present a general condition to obtain subspaces that decay uniformly in a system governed by the Lindblad master equation and use them to perform error-mitigated quantum computation. The expectation values of dynamics encoded in such subspaces are unbiased estimators of noise-free expectation values. In analogy to the decoherence free subspaces which are left invariant by the action of Lindblad operators, we show that the uniformly decaying subspaces are left invariant (up to orthogonal terms) by the action of the dissipative part of the Lindblad equation. We apply our theory to a system of qubits and qudits undergoing relaxation with varying decay rates and show that such subspaces can be used to eliminate bias up to first-order variations in the decay rates without requiring full knowledge of noise. Since such a bias cannot be corrected through standard symmetry verification, our method can improve error mitigation in dual-rail qubits and, given partial knowledge of noise, can perform better than probabilistic error cancellation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Hazards and Probabilistic Risk Assessments of a Light-Water Reactor Coupled with Industrial Facilities

This report provides a roadmap and toolkit for site-specific risk assessments across a broad range of industrial customers co-located with nuclear power plants (NPPs). This report builds upon the body of work sponsored by the Department of Energy (DOE) Light-Water Reactor Sustainability (LWRS) Flexible Plant Operation and Generation Pathway that presented hazards assessment and generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors co-located with hydrogen production facilities. The report expands the hazards assessments to include other industrial facilities: an oil refinery, a methanol plant, a synthetic fuel (synfuel) plant, the production of synthetic gas (syngas) as part of the methanol and synfuel plants, and wood pulp and paper mills. All these facilities are specified through industrial process and requirements research performed by national laboratories, universities, and interaction with industry. Many of the processes used in this report are pre-conceptual designs to use for decarbonization of the current technology facilities. A process of failure modes and effects analysis (what can go wrong) and accidentology (what has historically gone wrong) was used to determine the hazards presented to the NPP by the addition of the HES and the industrial customer. Chemical properties of feedstocks and products are summarized as part of the hazards assessment. Example analysis procedures are provided for each of the hazard types identified. These deterministic analyses can be used to assess adherence to licensing criteria. They can also be used to meet other safety goals like protection of the public, workers, or industrial facility equipment. The probabilistic analysis consisted of three sizes of HESs modeled in a PRA to assess the impact on the initiating events (IE) and results of the PRA. The PRA results conclude that the resulting increases in IE frequencies are below the limits required for small changes to existing NPPs under 10 CFR 50.59.

08 HYDROGEN↗