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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the framework provides a means to address relevant technical issues by: (1) defining a risk-informed analysis process for DI&C upgrade, that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies on nuclear power plant (NPPs).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning

Crack faults in rotating machines can cause machine shutdown or scrapping, endangering the normal operation and safety of nuclear power plants. Intelligent diagnostic techniques based on machine learning have the potential to diagnose crack faults. However, problems such as scarcity of field fault data and high noise of plant measurements pose challenges to the application of machine learning. Here this study proposes an ensemble learning approach to mitigate the negative impacts of the problems. Ensemble learning is a strategy for combining multiple machine learning models into a composite model. The basic idea of ensemble learning is that even if one model makes a mistake, other models can correct it. Case studies based on bearing and gear system fault experiments show that the proposed ensemble learning models have better diagnostic results than the single model in the presence of noise and small data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2009

High Efficiency Nuclear Power Plants using Liquid Fluoride Thorium Reactor Technology

An overall system analysis approach is used to propose potential conceptual designs of advanced terrestrial nuclear power plants based on Oak Ridge National Laboratory (ORNL) Molten Salt Reactor (MSR) experience and utilizing Closed Cycle Gas Turbine (CCGT) thermal-to-electric energy conversion technology. In particular conceptual designs for an advanced 1 GWe power plant with turbine reheat and compressor intercooling at a 950 K turbine inlet temperature (TIT), as well as near term 100 MWe demonstration plants with TITS of 950 K and 1200 K are presented. Power plant performance data were obtained for TITS ranging from 650 to 1300 K by use of a Closed Brayton Cycle (CBC) systems code which considered the interaction between major sub-systems, including the Liquid Fluoride Thorium Reactor (LFTR), heat source and heat sink heat exchangers, turbo -generator machinery, and an electric power generation and transmission system. Optional off-shore submarine installation of the power plant is a major consideration.

Juhasz, Albert J.↗
NASA NTRS2009

High Efficiency Nuclear Power Plants Using Liquid Fluoride Thorium Reactor Technology

An overall system analysis approach is used to propose potential conceptual designs of advanced terrestrial nuclear power plants based on Oak Ridge National Laboratory (ORNL) Molten Salt Reactor (MSR) experience and utilizing Closed Cycle Gas Turbine (CCGT) thermal-to-electric energy conversion technology. In particular conceptual designs for an advanced 1 GWe power plant with turbine reheat and compressor intercooling at a 950 K turbine inlet temperature (TIT), as well as near term 100 MWe demonstration plants with TITs of 950 and 1200 K are presented. Power plant performance data were obtained for TITs ranging from 650 to 1300 K by use of a Closed Brayton Cycle (CBC) systems code which considered the interaction between major sub-systems, including the Liquid Fluoride Thorium Reactor (LFTR), heat source and heat sink heat exchangers, turbo-generator machinery, and an electric power generation and transmission system. Optional off-shore submarine installation of the power plant is a major consideration.

Juhasz, Albert J.↗
NASA NTRS2019

Gas Turbine Energy Conversion Systems for Lunar and Planetary Nuclear Power Plants Including Ground Based Applications Using LiFTR "Liquid Fluoride Thorium Reactor" Technology

The background and motivation for this presentation is the Advanced Nuclear Power named in Energy Policy Act of 2005 (Gen IV Nuclear Systems Initiative), Thorium Energy Independence & Security Act of 2008 (S.3680), Gen IV Candidate Advanced Nuclear Power Plants, and high temperature gas turbine power plants offer large thermal efficiency improvement over steam plants.

Juhasz, Albert J.↗
DOE OSTI2025

Parametric and Nonparametric Models of U.S. Cost Overruns for Nuclear Power Plants

This study presents new data-driven models to estimate the effect of capacity on the percentage of cost overruns in the United States for nuclear power plant construction projects before and after the Three Mile Island accident. Parametric and nonparametric models have been developed that describe the significant shifts in nuclear energy costs during the dynamic environment. Employing a contemporary descriptive methodology and a quantitative analysis, we furnish a comprehensive overview of the alterations in cost overrun distribution and show the changes observed in other pivotal metrics alongside cost overruns. Our emphasis lies in documenting the fluctuations in cost overruns alongside nuclear reactor capacity levels and the increase of the overnight capital costs to build nuclear reactors. Our results show that increasing the size of nuclear reactors is not a factor statistically significant to decrease the percentage of cost overruns, and the probit model results provide evidence that an increase in size increases the probability of having cost overruns larger than 100% (double the estimated cost). We also compare our findings to two other regions: Asia and Europe.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

USE OF COGNITIVE WORK ANALYSIS IN DEVELOPING A NUCLEAR POWER PLANT NEW STATE VISION

The United States nuclear industry is in need of identifying and implementing a new strategy that will lower operating and maintenance costs while maintaining safety for existing plants. The industry must also have a clear and strategic vision of their transformative new state that focuses on ways in which technologies can be integrated to maximize the benefits of technology and people. While there are ongoing efforts in this area, this work discusses how the use of cognitive work analysis may further support these current efforts. This paper provides an overview of cognitive work analysis, as well as the state of current new state development efforts. The use of cognitive work analysis, as a tool to enhance existing practices, is presented in ensuring that the development of a new state vision is systematic and complete.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Alteration of the Cesium-137 soil profile by wild boar rooting after the Fukushima Daiichi Nuclear Power Plant accident

Large amounts of Cesium-137 ( 137 Cs, physical half-life ca. 30 years) were released into the environment from the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in Japan. Initially contaminants from the accident were deposited on the surface soil layers in terrestrial ecosystems, but over time vertical migration of 137 Cs has occurred, with most 137 Cs now distributed a few centimeters below the soil surface. Wild boar (Sus scrofa) rooting likely alters the distribution of contaminants in soil profiles. Such impacts may be particularly pronounced in areas contaminated from the FDNPP accident, given the increase in abundance of wild boar in these areas. Our objective was to determine whether rooting by wild boar alters the distribution of 137 Cs contamination within soil profiles through collection of soil cores from control sites and two different types of rooting samples, “new depressions” and “old depressions” within the difficult-to-return zone surrounding the FDNPP. The 137 Cs ratio [activity concentration of 137 Cs at each depth / the maximum activity concentration of 137 Cs in each core sample] rapidly decreased after reaching a peak at the control sites. However, the 137 Cs ratio at all rooted sites decreased more slowly, and 137 Cs was distributed deeper into the soil layers than control sites, across both new and old rooting sites. Moreover, the 137 Cs soil profiles varied greatly among core samples and sampling sites. Furthermore, these results demonstrate that soil disturbance from wild boar rooting can directly influence the distribution of contaminants within soil profiles. However, the extent to which deeper layers are affected by contamination depends on the unique characteristics (i.e., depth of rooting) of each wild boar rooting disturbance. Further studies are needed to examine the effects of long-term rooting on the alteration of 137 Cs in soil profiles.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Can We Use Machine Learning to Control Nuclear Power Plants?

Slides for the INL AI/ML Symposium 11.0 focused on AI/ML in instrumentation, control, and automation. This presentation was on how we can use AI/ML for control of nuclear power plants. A recording of the presentation and other INL AI/ML symposiums can be found at https://inl.gov/artificial-intelligence/.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1975

The utilization of LANDSAT imagery in nuclear power plant siting

LANDSAT imagery was used primarily to map geologic features such as lineaments, linears, faults, and other major geologic structures which affect site selection for a nuclear power plant. Areas studied include Pakistan, the South Carolina Piedmont, and Huelva, Spain.

Eggenberger, A. J.↗
DOE OSTI2021

Diagnosing nuclear power plant pipe wall thinning due to flow accelerated corrosion using a passive, thermal non-destructive evaluation method: Feasibility assessment via numerical experiments

Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Characterization of Recovery Human Action Mechanisms in Nuclear Power Plants

Recovery human action is defined as the action that prevents deviant conditions from producing unwanted effects. Analyzing recovery actions has been a critical part in human reliability analysis (HRA). However, there are a couple of limitations to treat recovery actions only depending on the current HRA methods. Representatively, the existing recovery analysis does not specifically consider recovery actions as are occurred in actual nuclear power plants (NPPs). The overall goal of this study aims to develop a novel recovery analysis method to account for human action recoveries in context of scenarios as well as complement the limitations of existing recovery analysis. In this paper, the recovery analysis in current HRA methods and their challenges are introduced. A strategy to achieve the goal is introduced with a modified recovery definition. Then, how we have researched the approach will be introduced in the paper.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1980

Applications of SLAR in nuclear power plant siting: A case history

Over 10,000 square km of side-looking airborne radar (SLAR) imagery was obtained and analyzed for the siting of the first nuclear power plant in the Republic of the Philippines. The imagery was obtained using the Motorola APS/AN-94D (X-band) real aperture system as part of an overall remote sensing program to site and evaluate potential site regions. Analysis of SLAR images, in conjunction with LANDSAT, color, black and white, and thermal infrared images, provided basic information on structure, relative geochronology, stratigraphy, geomorphology, and ground water, which facilitated field operations and data synthesis.

Siegal, B. S.↗
DOE OSTI2025

Techno-economic analysis of synthetic fuel production from existing nuclear power plants across the United States

Abstract Low carbon synfuel can reduce dependence on fossil fuels like diesel and jet fuel, and, with large-scale cost-effective production, contribute to global transportation sector decarbonization, Simultaneously, nuclear power plants are struggling economically due to falling wholesale electricity prices. Converting existing nuclear plants for synfuel production could preserve these low-carbon assets and enable large-scale synfuel production, yet no comprehensive technoeconomic analysis exists. This study evaluates the potential of integrating synthetic fuel production with five US nuclear plants, considering electricity and fuel markets and carbon dioxide source access. Such integration could enhance nuclear plant profitability by up to $792 million and offer a 10% return on investment over 20 years. The hydrogen production tax credit from the 2022 Inflation Reduction Act is crucial, comprising 75% of revenues on average. Carbon feedstock transportation has the highest cost at 35%, followed closely by synfuel production capital costs. Incentive policies are thus key for the decarbonization of the transportation sector and the economic importance of the geographic location of Integrated Energy Systems.

Garrouste, Marisol (ORCID:0000000168388644)↗
DOE OSTI2025

Trustworthiness and Trust: Identifying Factors that Drive Successful Human-AI Interaction in Nuclear Power Plant Applications

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are rapidly evolving and considered a promising tool for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may support personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for tasks such as surveillances or completing work orders. This is a fundamental shift in the way operators currently perform their tasks today. The literature of human-automation interaction indicates that trust is a crucial factor that drives successful interaction between a human operator and an automated system, like an AI-infused NPP application. This work presents the results of a literature review on key factors that relate to trust in AI/LLM technologies for NPP applications. The relevant literature of human factors and cognitive engineering has identified various factors related to trust including trustworthiness, performance characteristics, operator skill and perceived risk. This preliminary literature review will guide development and evaluation of models involving the identified factors influencing trust in AI and develop a framework for human-centered design for interface between humans and AI. By addressing trust, this work supports developing a technical basis for designing key characteristics of AI/LLM to support calibrated trust, which will ultimately support wide-scale adoption of AI/LLM technologies, as well as ensure safe, effective, and reliable use.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.

Energy & Fuels↗