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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Machine Learning for Power System Disturbance and Cyber-attack Discrimination

Power system disturbances are inherently complex and can be attributed to a wide range of sources, including both natural and man-made events. Currently, the power system operators are heavily relied on to make decisions regarding the causes of experienced disturbances and the appropriate course of action as a response. In the case of cyber-attacks against a power system, human judgment is less certain since there is an overt attempt to disguise the attack and deceive the operators as to the true state of the system. To enable the human decision maker, we explore the viability of machine learning as a means for discriminating types of power system disturbances, and focus specifically on detecting cyber-attacks where deception is a core tenet of the event. We evaluate various machine learning methods as disturbance discriminators and discuss the practical implications for deploying machine learning systems as an enhancement to existing power system architectures.

cyber-attack↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
DOE OSTI2012

Adapting Dismantling and Decommissioning Strategies to a Variety of Nuclear Fuel Cycle Facilities - 12237

AREVA has accumulated over 20 years of experience in managing and operating fuel cycle facilities Decontamination and Decommissioning (D and D) projects of many different types and a variety of scales, both as facility owner (at La Hague for example) and as prime contractor to external customers such as the French Atomic Energy Commission (at Marcoule). A specific Business Unit was created in 2008 to capitalize on this experience and to concentrate - in one division - the specific skills required to be successful and cost effective in decommissioning projects. Indeed one of the key lessons learned in the past decades is that decommissioning is a significantly different business as compared to normal operations of a nuclear facility. Almost all the functions of a project need to be viewed from a different angle, challenged and adapted consequently in order to optimize costs and schedule. Three examples follow to illustrate the point: Safety management needs to take into account the ever changing configuration of a plant under D and D (a quite new situation for the authorities). Production of waste is significantly different in term of volume, activities, conditioning and disposal path. Technology is important but technical issues are often less critical than good management and planning. Further examples and lessons learned are developed through reviewing the projects experience basis. AREVA has a long and vast experience in the cleanup and dismantling of a number of very large and complex nuclear facilities. This effort focused initially on AREVA's own plants and is expanding now to other customers. The setup of a specific Business Unit in 2008 to takeover this business allowed concentration of the skills and the lessons learned in a dedicated division so as to provide the best means to optimize safety, performance, costs and schedules. Indeed transitioning from operations to D and D of a nuclear facility is a quantum leap. The assistance from specialized teams can bring significant cost savings. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Bradwell - The first UK Nuclear Site to Enter Care and Maintenance - 18152

Magnox Ltd, a Cavendish Fluor company, is currently decommissioning twelve nuclear sites on behalf of the UK Nuclear Decommissioning Authority (NDA). Ten of these sites are former Power Stations; the other two are closed Research and Development facilities. An overall strategy for decommissioning the Magnox sites has been agreed between Magnox and the NDA. For all but one of these sites the decommissioning strategy involves an extended quiescent period, known as Care and Maintenance (C and M), during which remaining radiological hazards reduce naturally through radioactive decay. After this quiescent period the sites will be restored to an agreed end state through further decommissioning and clearance work. Sites in C and M will not be permanently manned. They will be passively safe such that prompt action will not be required in response to an alarm or fault. Preparing a nuclear site for C and M involves several inter-related strands of work including definition of the intended physical configuration of the site during C and M (the C and M entry state), establishing a safety case to support that configuration, carrying out the physical works needed to deliver the C and M entry state, preparing arrangements and a team to manage the site during C and M and consolidating the knowledge and information needed to support C and M and final clearance of the site. To enter C and M there needs to be a carefully controlled transition of site, personnel and management system into the agreed C and M configuration. Bradwell, a site on the east coast of England, was selected as part of the overall decommissioning strategy for 'early' entry into C and M; Bradwell is scheduled to enter C and M in late 2018, several years in advance of the next scheduled site, with the intent of carrying learning forward from Bradwell to the subsequent sites. Bradwell is now in the final stages of preparation for C and M. Decommissioning commenced on completion of defueling in 2005. A clearly defined C and M entry state was established early in decommissioning and, through interaction with Regulators and internal and external stakeholders, developed as a shared target, thus providing certainty to the scope of the overall decommissioning project. The site has subsequently been systematically decommissioned to meet this target C and M entry state. All legacy waste has been retrieved and packaged into robust disposal containers, the reactor buildings have a new weather envelope, and much of the site is empty space with concrete hardstanding where facilities used to exist and bare rooms where plant used to operate. The remaining radiological and environmental hazards are not significant. During C and M, which is anticipated to last for 70 years, Bradwell will be managed remotely without the requirement for personnel to be present on site, with the possible exception of an ongoing security presence when the site first enters C and M. In terms of UK decommissioning, many of the projects undertaken at Bradwell were first of a kind. The company operates under a programmed organisational and delivery model, which will enable innovation and learning from Bradwell to be carried forward across the whole Magnox fleet. In addition to decommissioning tools and techniques, processes to manage decommissioning, optimise the C and M entry state to reduce the burden on the UK taxpayer and to transition a site into C and M have been developed. This, in addition to taking people from Bradwell and seeding them into Magnox and the broader UK nuclear industry, should help to provide long term cost savings and delivery efficiencies across the entire NDA estate. This paper describes the journey of Bradwell Site through decommissioning and into C and M, highlighting the innovation and challenges that have contributed to the success of delivering the first UK nuclear site into Care and Maintenance. (authors)

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

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2022

Storage Futures Study: Key Learnings for the Coming Decades

The Storage Futures Study (SFS) is a multiyear research project that explored the role and impact of energy storage in the evolution and operation of the U.S. power sector. The SFS examined the potential impact of energy storage technology advancement on the deployment of utility-scale storage and the adoption of distributed storage, and the implications for future power system infrastructure investment and operations. The research findings and supporting data were published across a series of six reports, culminating in the final, seventh publication that draws upon findings from across the study, previous work, and additional analysis to identify eight key learnings about the coming decades. This presentation is from an NREL webinar to discuss the eight key learnings.

decarbonization↗
DOE OSTI2025

Deploying and Operating CephFS for Scientific Applications at Fermilab

Fermilab has been running a Ceph cluster in production for several years to support high-throughput scientific computing. Our primary use case is CephFS, which serves interactive data analysis workloads, with growing interest in using RGW for scalable object storage of scientific datasets. In this talk, we'll share lessons learned from successfully deploying and maintaining our Ceph cluster with cephadm, including challenges faced, performance tuning, and operational practices. We'll also present custom tools we've developed to streamline monitoring and management and discuss how Ceph fits into our broader storage architecture for large-scale scientific research.

Peisker, Alison [Fermilab]↗
DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗
DOE OSTI2022

Scale-up of microwave-assisted, continuous flow, liquid phase reactors: Application to 5-Hydroxymethylfurfural production

Microwave (MW) technology can be powerful for the electrification and intensification of chemical manufacturing. However, very few examples of scaled MW processes exist. In this work, we build a scaled, MW-assisted, continuous flow reactor for the processing of liquid phase chemistries with specific application to 5-hydroxymethylfurfural (HMF) production. Here, we demonstrate a corresponding computational fluid dynamics model to simulate the reactor’s temperature profile and performance, both of which are in good agreement with experiments. We construct a surrogate model to relate operating parameters to performance for guiding experiments, without demanding simulations, via active learning. Heat recirculation is demonstrated in this scaled reactor, further extending its scale and reducing the energy demand. An HMF yield of ~55 % and a productivity of 0.1 kg/hr, 8x higher than any other reactor, at a flowrate >20x than prior work, are demonstrated while maintaining energy efficiency of >98 %. A basic economic analysis estimates a $1.85/kg cost of HMF from fructose and >60 % reduction in CO 2 emissions than a conventional system without considering the impact of green electricity. We demonstrate that MW technology is well-applied to systems larger than the laboratory scale, and strategies to further reduction in cost and CO 2 are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of a plug-and-play anti-noise module for fault diagnosis of rotating machines in nuclear power plants

The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Projected metastable Markov processes and their estimation with observable operator models

The determination of kinetics of high-dimensional dynamical systems, such as macromolecules, polymers, or spin systems, is a difficult and generally unsolved problem — both in simulation, where the optimal reaction coordinate(s) are generally unknown and are difficult to compute, and in experimental measurements, where only specific coordinates are observable. Markov models, or Markov state models, are widely used but suffer from the fact that the dynamics on a coarsely discretized state spaced are no longer Markovian, even if the dynamics in the full phase space are. The recently proposed projected Markov models (PMMs) are a formulation that provides a description of the kinetics on a low-dimensional projection without making the Markovianity assumption. However, as yet no general way of estimating PMMs from data has been available. Here, we show that the observed dynamics of a PMM can be exactly described by an observable operator model (OOM) and derive a PMM estimator based on the OOM learning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

CMS distributed data analysis with CRAB3

The CMS Remote Analysis Builder (CRAB) is a distributed workflow management tool which facilitates analysis tasks by isolating users from the technical details of the Grid infrastructure. Throughout LHC Run 1, CRAB has been successfully employed by an average of 350 distinct users each week executing about 200,000 jobs per day.CRAB has been significantly upgraded in order to face the new challenges posed by LHC Run 2. Components of the new system include 1) a lightweight client, 2) a central primary server which communicates with the clients through a REST interface, 3) secondary servers which manage user analysis tasks and submit jobs to the CMS resource provisioning system, and 4) a central service to asynchronously move user data from temporary storage in the execution site to the desired storage location. Furthermore, the new system improves the robustness, scalability and sustainability of the service.Here we provide an overview of the new system, operation, and user support, report on its current status, and identify lessons learned from the commissioning phase and production roll-out.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

equivariant_electron_density

SAND2022-3548 O This code contains scripts that allow users to generate molecular electron densities, train a machine learning model to predict electron densities, and analyze the accuracy of machine learned electron densities. It accompanies a publication titled "Cracking the Quantum Scaling Limit with Machine Learned Electron Densities.” Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rackers, Joshua↗
DOE OSTI2025

Conin

SAND2025-07645O Conin is a Python library that supports constrained analysis of probabilistic graphical models (PGMs). It enables constrained inference and learning for hidden Markov models, Bayesian networks, dynamic Bayesian networks, and Markov networks. Conin interfaces with the pgmpy library to specify general probabilistic graphical models with a variety of optimization solvers to support learning and inference. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2023

Literature Review of Advanced Reactor Cost Estimates

This report provides a comprehensive review of existing literature on advanced reactor cost estimations in the form of a ‘meta-study’. Over 30 references were evaluated and considered as part of this effort. The information is then distilled into different tiers, each with varying levels of detail and granularity. The recommendations consist of low/medium/high estimates for capital and operating costs that can be adjusted using various correction factors (based on learning, numbers of plants per site, etc.). In addition, reference breakdown in costs based on a code of account structure is provided for the main four types of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗