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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Distributed Coaxial Cable Sensors for In-Situ Condition Based Monitoring of Coal-Fired Boiler Tubes

The increasing contributions of renewable energy sources present new challenges to the operation and maintenance of the existing coal-fired power plants. One of the major operational risks is the unexpected failure of superheater boiler tubes, leading to the most unplanned power plant outrages. The boiler tube failure is difficult to predict due to the harsh operating environments. Therefore, condition-based monitoring (CBM) with a reliable high temperature sensor becomes necessary to produce a meaningful assessment of the health condition of boiler tubes and their remaining lifetime. In this work, the stainless-steel and quartz coaxial cable sensor (SSQ-CCS) is proposed for in-situ distributed monitoring of the boiler tube temperatures in existing coal-fired power plants. Comprehensive tests have been conducted with an in-house testing facility at Clemson University to study and evaluate the sensors’ performance in the temperature range of 100℃ to 600℃. The results indicated that the measurement resolution of the SSQ-CCS sensor is better than 1℃, and the drift is less than 2% over long-period testing. Meanwhile, multi-physics finite element analysis has been conducted to optimize the design and evaluate the safety of the SSQ-CCS temperature sensor under various operational conditions. Based on the performance obtained in the laboratory-scale testing, a field test has been implemented at a power plant. Four SSQ-CCS temperature sensors were installed for in-situ monitoring of the temperatures of a power plant’s superheat tube assembles. The data acquisition system has been successfully set up and collected sensing signals for more than three months. The sensing signals have been post-processed, and the monitored temperature history through the SSQ-CCS temperature sensor has been validated and compared with the conventional high temperature thermal couple data.

Jiao, Xinyu↗
DOE OSTI2021

Specification of EBR-II Laser Profilometry Data

All laser profilometry data stored in FIPD were originally measured using the element laser profilometer system in HFEF/N. The irradiated fuel elements or pins were scanned by a laser beam to measure the outer diameter with position mapping of the spacer wire. The normal operation and data collection were performed automatically with HFEF/N Data Acquisition and Process Control System (DAPCS). With the digital control system, measured laser profilometry data were recorded in digital form on magnetic tapes, different from the analog contact profilometry data recorded by strip-chart record sheets. Two documents contained most of the important details about laser profilometry measurements. The first one is the operational manual of the laser profilometer (title: Element laser profilometer (ELP) operation and maintenance manual, HFEF/N OMM 4329, DOC. NO. W0171-0149-ES-01). This manual provides: (1) description of laser profilometer including the functions of each component (transceiver, retroreflector, clamping guide, shielding enclosure, electronic controller, etc.); (2) step-by-step guidance for calibrations, operations and measurements; and (3) maintenance procedures and other details of the element laser profilometer. Since first issued on 12/1/1986, several revisions/changes of the manual were developed to update the measurement accuracies and other factors for the laser profilometer system. The second document is a publicly accessible conference article titled “the element laser profilometer system in HFEF/N.” This article summarizes important functions and parameters of the laser profilometer. The range of measurements and the uncertainties reported in the article were consistent with the values reported in the operational manual.

42 ENGINEERING↗
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2025

Updating Nuclear Energy Cost Estimates for Net Zero World Initiative

Energy modeling of decarbonized scenarios in integrated energy systems requires nuclear energy parameters that are critical for forecasting, modeling and cost structure analysis. Using updated real-world data has always been a challenge to estimate current nuclear reactors costs and deployment scenarios. Given this, an updated set of parameters for overnight capital costs and operation and maintenance costs are estimated for the Net Zero World initiative using recent reports that provided a vast set of open sources data inputs. This paper follows the methodology developed in the Net Zero World report and applies the new ranges estimated in the Gateway for Accelerated Innovation in Nuclear report that address many of the current challenges in obtaining accurate cost data for advanced nuclear concepts. The final goal is to provide new estimates of the overnight capital costs and operational costs for different countries. The present paper improves the earlier capital cost estimations, building on recent literature that aims to obtain accurate data for modeling and simulation to enhance energy system evaluations and support decision-making in areas like de-carbonization and capacity expansion. Finally, the paper compares the new cost estimates with the old cost results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Meta-Analysis of Advanced Nuclear Reactor Cost Estimations

Supporting Data can be downloaded at: https://gain.inl.gov/content/uploads/4/2024/06/INL-RPT-24-77048-R1.xlsx Nuclear energy is a critical cornerstone of the current United States clean energy supply and may play a larger role in the future in support of a transition to a net-zero economy. The current fleet of nuclear reactors predominantly consists of large light-water reactors (LWRs), while many of the reactor designs under consideration are smaller and/or different technologies. Because these new designs have not yet been built, there is a high degree of uncertainty associated with their cost. This complicates energy-planning efforts because cost projections are not always standardized, consistent, and centralized in an easily accessible location. To help support energy planning in the US, this report provides advanced nuclear cost ranges using a transparent methodology along with other relevant information that can be used to help support decision making and energy planning. The purpose of this work was to conduct a methodical process for cost evaluation using only public information that was vetted with the end-goal to provide reference cost projections for nuclear energy. To provide a solid basis for these values, the approach and assumptions are explicitly laid out throughout the report allowing any user of the data to challenge or reconsider them. Because future US nuclear-reactor costs are still unknown due to little recent observed data, the report opted to compile a comprehensive list of bottom-up estimates and evaluate averages/trends within the data to identify reference ranges. This was deemed preferable to opining on the robustness or validity of one cost estimation versus another. To that end, the work evaluated thousands of lines of cost subaccounts from several bottom-up cost estimates. A wide variety of different reactor types captured in the data are of various sizes and technologies. Some of these reactors will be representative of advanced reactors under development while others will not. Thus, the results here are dependent on the data that are available and the accuracy of the estimates that are used. Each bottom-up estimate was reviewed to determine whether it was complete. Incomplete data sets were corrected to ensure an adequate basis of cross-comparison. The report is not without limitations and should be interpreted as an initial step to develop cost ranges for nuclear technology. Ultimately, future work can build upon the methodology with refined cost estimates to reduce uncertainty. US-based overnight capital cost (OCC) estimates were compiled from extensive data sets into ranges for both large and small reactor sizes for 2030. To project the cost declines over time, learning rates were sampled from literature sources. No SMRs were previously built; hence, learning rates based on bottom-up approaches (e.g., by quantifying the impact stemming from fabrication of different components, modular work, site construction, commissioning) were prioritized. For larger reactors, actual learning rates from deployments were used to project future costs (adjusted to account for standardization or lack thereof between designs). Other costs included are fixed and variable operations and maintenance costs. The final variables were capacity factors and ramp rates to support energy planning.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Specification of EBR-II Contact Profilometry Data

All contact profilometry data stored in FIPD was originally measured using the HFEF vertical contact profilometer, which was an electronic gauge to measure the diameter profile of an element or capsule continuously over the region of interest. The element could be rotated as it passed through the probes if the wire wrap was not to be removed and the element could be returned to reactor for further irradiation. If the wire wrap was removed, the element could be drawn vertically through the probes, rotated, and the process repeated. In this way any cladding ovality could be observed. Figure 1 shows the setup used to measure the fuel pin diameter profile. The ambient temperature during the measurement was ~21-23 ºC. The cladding temperature during the measurement is considered to be similar to the ambient temperature by assuming the fuel decay heat did not impact the cladding temperature. Two documents contain most of the important details about contact profilometry measurements. The first one is the operational manual of the contact profilometer (title: Element contact profilometer operation and maintenance, HFEF/N OMM 4326, DOC. NO. W0202-0007-SA-01). This manual provides: (1) description of the contact profilometer including the functions of each component (support stage, sensing head, control system, and calibration standards, etc.); (2) step-by-step guidance on calibrations, operations, and measurements; and (3) maintenance procedures and other details of the contact profilometer. The second document is a publicly accessible report entitled “A Fuel-element Profilometer for the HFEF.” This report describes the development of the contact profilometer, including the design requirements, features, and details of each component, as well as the acceptance tests for the built system. This document is important because it reports the uncertainties of the measurements with detailed deduction procedures. The reported uncertainties should apply to all hard-copy contact profilometry data in FIPD. Some key information about contact profilometry measurements was extracted from these two documents. Combining the information with other sources, a specification for interpretating the contact profilometry data in FIPD was established.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope

In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗
DOE OSTI2023

Supplement to M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0

Measurement and verification (M&V) are key to determining and confirming the continued operation of, and savings associated with, performance-based contracts. The Federal Energy Management Program (FEMP) updates these guidelines to reflect the evolution of M&V to incorporate industry best practices and ensure that savings from these performance-based projects are properly planned, communicated, and realized. FEMP released M&V Guidelines: Measurement and Verification for Federal Energy Projects Version 3.0 in 2008. This document was developed with input from industry-government working groups, previous M&V guideline authors, and national laboratory subject matter experts. The document provided the methods and guidelines, with a focus on federal energy savings performance contracts (ESPCs). When FEMP released M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0 in 2015, FEMP expanded the intended audience to include anyone (energy managers, procurement officers, and contractors) involved in implementing performance-based contracts. The streamlined M&V Guidelines Version 4.0 provided the procedures and guidelines needed to quantify savings resulting from installing energy-efficient equipment, water conservation, improved operation and maintenance (O&M), renewable energy, and cogeneration projects with a performance-based contract; however, it removed some of the associated details on topics such as developing regression models and sampling plans. The details and information provided in M&V Guidelines v. 3.0 are still pertinent to M&V for performance-based contracts. The objective is to keep FEMP M&V version 4.0 condensed, providing the reference document for specifying M&V methods and procedures and a resource for developing project-specific M&V plans; this supplement provides additional details. The content of this supplement has been updated to include pertinent revisions between M&V version 3.0 and M&V version 4.0 by providing details and updating the content to ensure relevancy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Experimental Safe Extremum Seeking for Accelerators

Here, we demonstrate the recent designs of safe extremum seeking (Safe ES) on the 1-km-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of extremum seeking (ES) which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric. Tuning is necessitated by accelerators being large complex systems, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime. Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the proton radiography (pRad) beamline of 800-MeV protons for spot size tuning; the second on a high-performance code, HPSim, for tuning the low-energy beam transport (LEBT) region that contains a beam of 750-keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

High Temperature Anode Recycle Blower for Solid Oxide Fuel Cell, Phase II (Final Report)

Broad commercialization of solid oxide fuel cells (SOFCs) requires anode offgas recycle blowers (ARCB) that are specifically designed for handling the challenging operating conditions presented by the SOFC process gases. Otherwise, they can be susceptible to frequent maintenance, low reliability, and short life. This report presents details of a DOE-funded Phase II effort conducted by Mohawk Innovative Technology, Inc. (MiTi®) for the development and testing of an oil-free, low cost, high-temperature centrifugal ARCB based on compliant foil bearing (CFB) technology for support of a 100 kW solid oxide fuel cell power plant. This Phase II builds on the results of a successful Phase I development project that resulted in the demonstration of a low TRL-6 prototype, shown in Figure 1. This Phase II final report presents design improvements over the Phase I prototype, presents the assembled test ARCBs, and discusses the advantages of this novel technology, particularly its long life and maintenance-free operation, which are derived from the use of CFBs. Also included are preliminary techno-economic analysis considerations for cost-effective deployment of the technology. The specific objectives of this project, as stated in the statement of program objectives (SOPO) were 1) to follow the methodology of design for manufacturing (or manufacturability) and assembly for implementing improvements identified as part of the Phase I effort with the purpose of reducing cost, enabling mass production, and facilitating the commercialization of a revised ARCB design, and 2) to fabricate four complete ARCB units based on the revised design and demonstrating their performance in both a laboratory setting and in an actual SOFC power plant. Execution of this SOPO would be supported by a number of technical tasks resulting in the fabrication and testing of the prototypes. To this end, MiTi continued the teaming relationship started during Phase I of the program with subcontracting partner FuelCell Energy, Inc. (FCE), which integrated (under a parallel effort funded by DOE Award DE-FE0026199) a modular 200 kWe SOFC power plant. MiTi and FCE coordinated to ultimately incorporate one of MiTi’s ARCB prototypes into one of the 100 kWe SOFC Modular Power Blocks (MPB) that constitute the core of FCE’s SOFC power plant for in-situ long-duration testing. Additionally, MiTi would explore the scalability and extendibility of the technology to other applications, as well as conduct a basic techno-economic analysis.

03 NATURAL GAS↗
DOE OSTI2022

DEVELOPMENT OF OIL FREE CENTRIFUGAL BLOWER AS ENABLING TECHNOLOGY FOR SOLID OXIDE FUEL CELL ANODE GAS RECYCLING

Broad commercialization of solid oxide fuel cells (SOFCs), requires anode offgas recycle blowers (AORB) that are specifically designed for handling the challenging operating conditions presented by the SOFC process gases. Otherwise, they can be susceptible to frequent maintenance, low reliability, and short life. This work presents details of the development and testing of an oil-free, low cost centrifugal AORB based on compliant foil bearing (CFB) technology for support of a 100 kW solid oxide fuel cell power plant. The advantages of this novel technology are discussed, particularly its long life and maintenance-free operation, which are derived from the use of CFBs. Also included are preliminary techno-economic analysis considerations for cost-effective deployment of the technology.

03 NATURAL GAS↗
DOE OSTI2023

Assessing the Potential for Improved Lubricants to Reduce Wind O&M Costs

Lubrication is a key aspect of maintaining a wind turbine in operational condition. In this study, we quantify the potential for increasing energy production and reducing maintenance costs across the current and future U.S. fleet of wind turbines as a result of improvements in lubrication performance. The modeled improvements reduce the median levelized cost of energy by 1-2% across the U.S. fleet. Extending the lubricant replacement interval has a larger impact on total cost and energy production than reducing lubricant cost.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2025

Wave energy in season: a comparative approach to feasibility of seasonal deployments for remote coastal communities

Remote coastal communities, which could be early adopters of wave energy projects, have concerns over costs, conflicts, and potential risks of development. Designers and developers are challenged to address these community concerns as they continue to develop wave energy technologies. One potential means of reducing costs, conflicts, and risks, especially for demonstration and pilot-scale projects, could be planning a deployment that operates for only a portion of the year—a seasonal deployment. Here, in this paper, we examine the impacts of a seasonal deployment in terms of cost, electricity production, operations and maintenance, environmental impacts, and community benefits. We take a holistic, comparative approach to feasibility that can be replicated for other comparative studies. We estimate electricity production using a point absorber WEC modeled near Sitka, AK, USA and optimized for the given sea conditions. We determine that, for remote community sized projects, seasonal deployments could result in small cost savings (less than 10 %), but larger decreases in annual energy production (around 30 % for our case study area). Seasonal deployments could be preferable in places with seasonal energy needs, if failures and device access become a major hindrance to wave energy technology development, or as a cautionary approach to introducing new technology to the oceans. We also determine that a highly seasonal wave resource is not necessarily a requirement for seasonal deployments to be considered. Seasonal deployments are an alternative to year-round deployments that can be considered in places where marine spatial conflict is a seasonal concern.

WEC optimizations↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

4th Big Data for Nuclear Power Plants Workshop 2023

The Ohio State University and Idaho National Laboratory organized the 4 th Big Data for Nuclear Power Plants Workshop in November, 2023 in Columbus, Ohio. Workshop topics were chosen to understand the challenges and gaps that need to be addressed to maximize the impact of data on the nuclear industry, as well as the associated applications and risks. Discussions were focused around six specific application areas: Operation and Maintenance; Machine Learning in Nuclear Materials and Advanced Manufacturing; Cybersecurity; High-Performance Computing and Massive Computation; Big Data and Digital Twins; and Nuclear Non-Proliferation. The opportunities, challenges, and risks identified in the six focus areas explored in this workshop are diverse, but some common themes emerge, such as the importance of data integrity, quality, coverage, privacy, and traceability. Big data and AI/ML tools can be leveraged to reduce costs, optimize human tasking, and reduce human error across various application areas. In order for the nuclear industry to benefit from big data and advanced analytic capabilities, it is essential to address challenges and risks, such as data privacy, model reliability, and computational resource availability. Learning from other industries that have successfully implemented big data and AI/ML technologies, like the aerospace industry, can help the nuclear industry successfully integrate these technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Conceptual Machine Learning Strategy for Maintenance of Molten Salt Heat Exchanger

This report addresses the requirements of Milestone M3.2 Define maintenance scenarios and benchmarks. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗