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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

An Introduction to Flight Software Development: FSW Today, FSW 2010

Experience and knowledge gained from ongoing maintenance of Space Shuttle Flight Software and new development projects including Cockpit Avionics Upgrade are applied to projected needs of the National Space Exploration Vision through Spiral 2. Lessons learned from these current activities are applied to create a sustainable, reliable model for development of critical software to support Project Constellation. This presentation introduces the technologies, methodologies, and infrastructure needed to produce and sustain high quality software. It will propose what is needed to support a Vision for Space Exploration that places demands on the innovation and productivity needed to support future space exploration. The technologies in use today within FSW development include tools that provide requirements tracking, integrated change management, modeling and simulation software. Specific challenges that have been met include the introduction and integration of Commercial Off the Shelf (COTS) Real Time Operating System for critical functions. Though technology prediction has proved to be imprecise, Project Constellation requirements will need continued integration of new technology with evolving methodologies and changing project infrastructure. Targets for continued technology investment are integrated health monitoring and management, self healing software, standard payload interfaces, autonomous operation, and improvements in training. Emulation of the target hardware will also allow significant streamlining of development and testing. The methodologies in use today for FSW development are object oriented UML design, iterative development using independent components, as well as rapid prototyping . In addition, Lean Six Sigma and CMMI play a critical role in the quality and efficiency of the workforce processes. Over the next six years, we expect these methodologies to merge with other improvements into a consolidated office culture with all processes being guided by automated office assistants. The infrastructure in use today includes strict software development and configuration management procedures, including strong control of resource management and critical skills coverage. This will evolve to a fully integrated staff organization with efficient and effective communication throughout all levels guided by a Mission-Systems Architecture framework with focus on risk management and attention toward inevitable product obsolescence. This infrastructure of computing equipment, software and processes will itself be subject to technological change and need for management of change and improvement,

Gouvela, John↗
DOE OSTI2020

Techno-Economic Modelling of Tidal Energy Converter Arrays in the Tacoma Narrows

Hydrokinetic tidal energy converter (TEC) technology is yet to become cost competitive with other renewable energy sources. Understanding the interaction between energy production and the costs incurred harvesting that energy may unlock the economic potential of this technology. Although hydrodynamic simulation of TEC arrays has matured over time, including demonstration of how small and large arrays affect the resource, integration of cost modelling is often limited. The advanced ocean energy array techno-economic modelling tool ‘DTOcean’ enables designers to calculate and improve the levelised cost of energy (LCOE) of an array through parametric simulation of the energy extraction, design of the electrical network, moorings and foundations, and simulation of the installation and lifetime operations and maintenance of the array. This work presents a verification of DTOcean’s ability to simulate the techno-economic performance of TEC arrays by reproducing the hypothetical RM1 reference model, a semi-analytical model of a TEC array based in the Tacoma Narrows of Washington state, U.S.A. It is demonstrated that DTOcean can produce a reasonable estimate to the LCOE predicted by the reference model, giving (in Euro cents per kiloWatt hour) 36.69 ¢/kWh against the reference model’s 34.612 ¢/kWh for 10 TECs, while for 50 TECs, DTOcean calculated 20.34 ¢/kWh compared to 17.34 ¢/kWh for the reference model.

Topper, Mathew B. R. (ORCID:0000000347324347)↗
NASA NTRS2023

Components Refurbishment and Chemical Analysis Facility, Hot Spot 1 SWMU #041 Year 3 Annual Performance Monitoring Report Kennedy Space Center, Florida

This Year 3 Annual Performance Monitoring Report (PMR) presents the operations, maintenance, and monitoring activities for the Hydraulic Containment System (HCS) Interim Measure (IM) at the Components Refurbishment and Chemical Analysis (CRCA) facility located at John F. Kennedy Space Center (KSC), Florida. The primary objective of the HCS is to attain hydraulic control of the dissolved-phase chlorinated volatile organic compound (CVOC) plume, with the secondary objective to reduce concentrations of CVOCs in the high-concentration plume to support transition to monitored natural attenuation (MNA). CRCA has been designated Solid Waste Management Unit 041 under the KSC Resource Conservation and Recovery Act Corrective Action Program. The timeframe for activities documented in this Year 3 PMR extends from September 2021 through October 2022. Baseline sampling activities were completed in June 2019, and full-scale startup of the HCS IM was completed in July-August 2019. The operational runtime of the HCS for the Year 3 reporting period was approximately 91%, with the majority of downtime attributed to system maintenance and repair. This is generally consistent with Year 1 and Year 2 runtimes of 85% and 92%, respectively. To help reduce downtime, an anti-scaling amendment, Redux 390, has been used to reduce scaling and help maintain system design parameters. Over nine million gallons of groundwater were treated during Year 3 of HCS operations, and concentrations of the site’s contaminants of concern (trans-1,2-dichloroethene and vinyl chloride) have been reduced by over 97%. This PMR describes the activities that were performed during Year 3 to operate and monitor the HCS IM, which includes three extraction wells, seven injection wells, and conveyance piping to a modular structure containing the control panel and an air stripper. Influent and effluent sampling results from the air stripper show that the system is operating as designed and is reducing concentrations of contaminants of concern to below detection limits. In addition to HCS operation, this PMR also discusses performance monitoring that has been implemented to assess progress of the HCS IM and overall plume conditions through scheduled groundwater (quarterly and semi-annual) and sub-slab soil gas (quarterly) sampling and analysis. Two ambient air samples were also collected on a quarterly basis in the vicinity of the modular structure and the paved driveway east of the Solvent Reclamation Area during routine operation and maintenance (O&M) activities to ensure safe breathing zone air quality for on-site personnel. All sub-slab soil gas and ambient air sampling conducted during the Year 3 operational period showed results below applicable regulatory air screening limits. Predictions made during the Year 2 groundwater model updates were in close correlation to post Year 3 plume conditions. Therefore, it can be assumed that the projected path remains valid for transition to MNA in one to two years of continuous HCS operation. The contents of this Year 3 PMR were presented during the February 2023 KSC Remediation Team meeting, where Team consensus was reached on several items including continued O&M of the HCS, and continued monitoring of groundwater, ambient air, and sub-slab soil gas. Replacement of MW0019 and VMP04 was also recommended, as well as additional direct-push technology sampling to further delineate the downgradient plume and confirm overall site-wide low-concentration plume boundaries. Sampling for per- and polyfluoroalkyl substances at CRCA is ongoing and will be submitted under separate cover.

K. Alex Murphy↗
NASA NTRS1995

Universities Earth System Scientists Program

This document constitutes the final technical report for the National Aeronautics and Space Administration (NASA) Grant NAGW-3172. This grant was instituted to provide for the conduct of research under the Universities Space Research Association's (USRA's) Universities Earth System Scientist Program (UESSP) for the Office of Mission to Planet Earth (OMTPE) at NASA Headquarters. USRA was tasked with the following requirements in support of the Universities Earth System Scientists Programs: (1) Bring to OMTPE fundamental scientific and technical expertise not currently resident at NASA Headquarters covering the broad spectrum of Earth science disciplines; (2) Conduct basic research in order to help establish the state of the science and technological readiness, related to NASA issues and requirements, for the following, near-term, scientific uncertainties, and data/information needs in the areas of global climate change, clouds and radiative balance, sources and sinks of greenhouse gases and the processes that control them, solid earth, oceans, polar ice sheets, land-surface hydrology, ecological dynamics, biological diversity, and sustainable development; (3) Evaluate the scientific state-of-the-field in key selected areas and to assist in the definition of new research thrusts for missions, including those that would incorporate the long-term strategy of the U.S. Global Change Research Program (USGCRP). This will, in part, be accomplished by study and evaluation of the basic science needs of the community as they are used to drive the development and maintenance of a global-scale observing system, the focused research studies, and the implementation of an integrated program of modeling, prediction, and assessment; and (4) Produce specific recommendations and alternative strategies for OMTPE that can serve as a basis for interagency and national and international policy on issues related to Earth sciences.

Estes, John E.↗
DOE OSTI2022

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, this study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modeling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Here, the results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within 3% of measured values.

42 ENGINEERING↗
DOE OSTI2021

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.

Compressors↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2025

Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors.

LSTM↗
DOE OSTI2023

OPTOM: Optimization of Parabolic Trough - Operations & Maintenance

The US Department of Energy’s SunShot goals look to reduce the cost of Concentrating Solar Power (CSP) technology to 5¢/kWh for baseload plants. This is about a 50% reduction from current costs. To achieve this cost target, a significant reduction in operation and maintenance (O&M) costs of 40 to 50% is likely needed. Advances are needed in the O&M practices of CSP plants if the technology is to achieve the SunShot cost goals. Digitization of plant performance and O&M data has become a new best practice in the world of renewable energy asset management. Owners and operators of large photovoltaic and wind power plants are working to digitize performance and O&M data at their existing assets, to improve their management of the facilities, to increase performance, reduce O&M costs, and lower the overall life cycle cost of ownership. CSP power plants are behind the curve of other technologies on the digitization of plant information to aid in the plant asset management. This project directly addresses the objective of digitizing the O&M data of the solar field, focusing on three areas: 1) creating a framework for sharing data and information, 2) creating a system for monitoring and managing the maintenance of the solar field collectors, and 3) developing analytic tools to identify issues in the solar field. According to the NREL CSP Best Practices Study, the current practice at many CSP plants is to rely on paper lists, spreadsheets, and email for monitoring and managing problems and maintenance in the solar field. The key element to digitize solar field O&M is the creation of a centralized data archive that all users and systems can interface with. This project developed a centralized relational database framework that allows users and applications to access and share data. Conventional power plants utilize Computerized Maintenance Management Systems (CMMSs) to track the corrective, preventive (scheduled), and predictive maintenance of equipment and subsystems in the power plant. CSP plants use these systems in the power block, but while these systems specialize at tracking maintenance on up to thousands of pieces of equipment, they are not well suited for tracking the tens or hundreds of thousands of components in large commercial CSP or photovoltaic solar fields. In this project we developed a new software application referred to as FieldStatus (TM). This is a specialized database program that is used to track the status of each collector and its components. This application is designed to complement the existing CMMS to enable improved tracking and management of maintenance activities in the solar field. One of the major maintenance tasks for solar fields is maintaining the cleanliness of the mirrors. Although seemingly a relatively straight forward task, it has often proven challenging to maintain high levels of cleanliness in an efficient and cost-effective manner. This project developed new tools and metrics for monitoring and optimizing solar field cleaning resources and overall solar field cleanliness.

14 SOLAR ENERGY↗
NASA NTRS2014

A Physics-Based Modeling Framework for Prognostic Studies

Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles.

Li-ion Batteries↗
NASA NTRS1996

Lunar Limb Observatory: An Incremental Plan for the Utilization, Exploration, and Settlement of the Moon

This paper proposes a comprehensive incremental program, Lunar Limb Observatory (LLO), for a return to the Moon, beginning with robotic missions and ending with a permanent lunar settlement. Several recent technological developments make such a program both affordable and scientifically valuable: robotic telescopes, the Internet, light-weight telescopes, shared- autonomy/predictive graphics telerobotic devices, and optical interferometry systems. Reasons for focussing new NASA programs on the Moon include public interest, Moon-based astronomy, renewed lunar exploration, lunar resources (especially helium-3), technological stimulus, accessibility of the Moon (compared to any planet), and dispersal of the human species to counter predictable natural catastrophes, asteroidal or cometary impacts in particular. The proposed Lunar Limb Observatory would be located in the crater Riccioli, with auxiliary robotic telescopes in M. Smythii and at the North and South Poles. The first phase of the program, after site certification, would be a series of 5 Delta-launched telerobotic missions to Riccioli (or Grimaldi if Riccioli proves unsuitable), emplacing robotic telescopes and carrying out surface exploration. The next phase would be 7 Delta-launched telerobotic missions to M. Smythii (2 missions), the South Pole (3 missions), and the North Pole (2 missions), emplacing robotic telescopes to provide continuous all-sky coverage. Lunar base establishment would begin with two unmanned Shuttle/Fitan-Centaur missions to Riccioli, for shelter emplacement, followed by the first manned return, also using the Shuttle/Fitan-Centaur mode. The main LLO at Riccioli would then be permanently or periodically inhabited, for surface exploration, telerobotic rover and telescope operation and maintenance, and support of Earth-based student projects. The LLO would evolve into a permanent human settlement, serving, among other functions, as a test area and staging base for the exploration, settlement, and terraforming of Mars.

Lowman, Paul. D., Jr.↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
DOE OSTI2024

Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS) (Final Technical Report)

Precision agriculture, where sensing of soil, environment and crop conditions are used to precisely synchronize inputs (such as water and fertilizer) to crop needs enhances input use efficiency. This can improve yields and farm profitability while mitigating environmental losses, improving soil carbon content and substantially decreasing energy use for food, feed and fuel crops. Unfortunately, farmers are not yet able to harness the full potential of these management technologies as there is a lack of available management information, and there is therefore a need for sensors that are able to economically measure spatio-temporal variability in soil and crop properties of extremely heterogeneous farm fields precisely at high resolution and at low cost. Real-time, in-situ monitoring of agricultural soil conditions is today carried out using devices that limit the total number of nodes that can be used economically to typically one per acre or less. Higher spatio-temporal resolution sensing would enable more precise agricultural input optimization, with significant benefits to the farmer and the environment. In order to address this issue, this project focused on developing additively manufactured, biodegradable, soil sensors with predicted costs of < $\$$1 per unit to monitor crop inputs (such as water and fertilizer) that predictably, harmlessly degrade away into the soil when no longer needed. These sensor nodes should be easy to place, accurately and continuously monitor soil and crop conditions for an entire season, be read remotely using existing farm equipment, require no ongoing maintenance, not impede farm operations and produce no persistent waste. This approach could enable a >100× increase in information density over current solutions for precision farming of row and other crops, and lead to significant reductions in input energy use and provide increased yield for biofuel crops. Over the course of this project the team at the University of Colorado Boulder, University of California Berkeley, and Colorado State University/Kansas State University investigated a wide range of printable biodegradable electronic materials and sensor designs for determining soil moisture and soil nitrate concentration. These efforts expanded the available materials set for printed soil degradable electronic materials, particularly for conductors, enabling high conductivity and stability. Printed soil moisture and nitrate sensors with suitable sensitivity and selectivity were developed and characterized. Low power and passive wireless electronic systems were integrated with the soil sensors, and testing was carried out with completed sensors to understand their functionality under agricultural conditions. Additionally, other sensor types enabled by the biodegradable materials set created during this project, such as soil microbial activity sensors, were also developed and demonstrated. Project outputs include 10 peer reviewed publications, 4 patent applications, 21 technical presentations, 3 PhD thesis, 10 media reports, 8 additional grants worth over $\$$6M, and the formation of 3 start-up companies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Smart Monitoring and Diagnostic System (SMDS) for Packaged Air Conditioners and Heat Pumps for Small/Medium Commercial Buildings: Preparation for Commercialization: CRADA 478 [Abstract only]

This project will enable Pacific Northwest National Laboratory (PNNL) and industry partner, mCloud Technologies, to work collaboratively to ready the Smart Monitoring and Diagnostic System (SMDS) for commercial deployment and validate its performance under real-world conditions at field sites. This project will specifically focus on 1) implementing the SMDS algorithms in a scalable cloud-based software architecture, 2) designing new, innovative, complimentary commercial services based on the SMDS, 3) enhancing the SMDS energy and cost impact algorithms to reduce uncertainty in estimates, 4) determining the lower limits on SMDS performance degradation detection, 5) validating algorithm performance and default values of adjustable thresholds with existing data from controlled physical testing and from customer packaged air conditioners and heat pumps (commonly referred to as rooftop units or RTUs), 6) field testing to validate the system on multiple customer buildings in diverse environments, and 7) expanding field deployment to a larger set of mCloud’s customer buildings. Project results by validating, enhancing, and quantifying the performance of the SMDS will position mCloud, and potential future licensees, to implement the SMDS in commercial offerings that encourage and enable use of condition-based and predictive maintenance, leading to significant reductions in energy use and greenhouse gas emissions associated with space conditioning by RTUs. Furthermore, these enhancements will increase the value of the SMDS for users and increase the potential market for its use and impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

On the Language of Reliability: A System Engineer Perspective

In its classical definition, risk is defined by three elements: what can go wrong, what are its consequences, and how likely is it to occur? While this definition makes sense in a regulatory-based framework where for the current fleet of operating light water reactors (LWRs), the risks associated with nuclear power plants typically are characterized in terms of core damage and large early release frequency (LERF), this approach does not provide a useful snapshot of the health of the plant from a broader perspective. This is due to the very narrow context in which the term “risk” typically is defined as nuclear safety aspects that have the potential to impact public health. In this paper, we take the viewpoint of nuclear safety that is reflective of the current fleet of operating LWRs for which core damage frequency and LERF are appropriate metrics. For other advanced reactor designs, other more applicable technology neutral metrics of reactor safety metrics would be specified. A possible alternate path would start by redefining the word risk with a broader meaning that better reflects the needs of a system health and asset management decision-making process. Rather than asking how likely an event could occur (in probabilistic terms), we can ask how far this event is from occurring. Our approach starts by defining and quantifying component and system health in terms of a “distance” between its actual and limiting conditions, i.e., determination of the margin that exists between the current state/condition and the state where the component/system is no longer capable of achieving its intended function. A margin is a measure that is more reflective of the current state or performance of a component, and therefore more closely tied to decisions that are made on an ongoing basis. Further, we will show how, given the data available from plant equipment reliability and monitoring (e.g., pump vibration data) and prognostic (e.g., component remaining useful life estimation) data, a margin can be described and determined for all types of maintenance approaches (e.g., corrective or predictive maintenance). We show how classical reliability models (e.g., fault trees) can be used to quantify the system margin provided component margin values. In the approach described in this paper, the propagation of margin values through classical reliability models are not performed using classical probabilistic calculations applied to sets (as performed in a typical plant probabilistic risk assessment). Instead, we show how it is possible to propagate margin values through Boolean logic gates (i.e., AND and OR operators) through distance-based operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cybersecurity Resiliency of Marine Renewable Energy Systems Part 2: Cybersecurity Best Practices and Risk Management

Marine renewable energy (MRE) is an emerging source of power for marine applications, marine devices, and coastal communities. This energy source relies on industrial control systems and IT to support operations and maintenance activities, which create a pathway for an adversary to gain unauthorized access to systems and data and disrupt operations. Incorporating cybersecurity risk prevention measures and mitigation capabilities from inception, development, operation, to decommissioning of the MRE system and components is paramount to the protection of energy generation and the security of network architecture and infrastructure. To improve the resilience of MRE systems as a predictable, affordable, and reliable source of energy, cybersecurity guidance was developed to enable operators to assess cybersecurity risks and implement security measures commensurate with the risk. This publication is the second of a two-part series, with Part 1 addressing a framework to determine cybersecurity risk by assessing the vulnerability of an MRE system to potential cyber threats and the consequences a cyberattack would have on the end user. This Part 2 publication describes an approach to select appropriate cybersecurity best practices commensurate with the MRE system's cybersecurity risk. The guidance includes 86 cybersecurity best practices, which are associated with 36 cybersecurity domains and grouped into nine categories. The best practices follow the core functions of the National Institute of Science and Technology Cybersecurity Framework (e.g., identify, detect, protect, respond, and and recover) and insights from both maritime and energy industry guidance documents to identify security measures effective in protecting information and operational technology assets prevalent in MRE systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development and Test of High Temperature Surface Acoustic Wave Gas Sensors

The demand for sensors in hostile environments, such as power plant environments, aerospace environments, oil and gas extraction, and high-temperature metallurgy environments, has risen over the past decades in a continuous attempt to increase process control, improve energy and process efficiency in production, reduce operational and maintenance costs, increase safety, and perform condition-based maintenance in equipment and structures operating in high-temperature harsh-environment conditions. The increased reliability, improved performance, and development of new sensors and networks with a multitude of components, especially wireless networks, are the target for operation in harsh environments. Gas sensors, in particular H2 sensors, operating above 200 C are required in the instrumentation, process control and general safety of a number of industries including coal, natural gas, and nuclear power generation facilities, the aerospace and automotive industries, metallurgical production and defense-related applications. The surface acoustic wave (SAW) platform is a particularly promising option for high-temperature, harsh-environment gas sensing applications since the platform exhibits advantages, such as battery-free and wireless operation, small size, possibility for scale production using well-developed technologies from the semiconductor industry, and low cost of installation and operation. In this work, one-port SAW resonators (SAWRs) operating along five different orientations on a commercially available langasite (LGS) wafer employing Pt-Al2O3 electrodes and reflectors were designed, fabricated, and used as high-temperature H2 sensors. Two of the selected orientations were predicted and confirmed to have temperature-compensated operation above 150 C. A gas sensor test setup was developed, capable of gas cycling between N2, O2 and N2/H2 mixtures under extended high-temperature periods (up to 650 C for over 20 hours). Thin film Pt-Al2O3 was used as the electrode material for the transducers and reflectors capable of high-temperature operation, and also as H2 sensing film. In addition, yttria-stabilized zirconia (YSZ) thin films with Pt decoration were tested as sensing films aimed to enhance the SAWR sensor response to H2. The SAW devices were monitored in excess of 1700 hours in real-time during gas cycling sequences up to 600 C, leading to the following findings: i) the Pt-Al2O3 electrodes performed better for H2 sensing than the Pt-decorated YSZ sensing film, showing as much as 50% higher frequency variation response in the 200 C to 400 C range; ii) different crystallographic orientations operating on the same LGS wafer experienced different responses to H2 exposures up to 500 C; iii) the surface oxidation state of the SAWR sensors was shown to have an important impact on subsequent H2 exposure responses. In addition, a sensor system employing two LGS SAWRs, aligned along two different orientations, has been developed to simultaneously determine H2 presence and temperature. Finally, wireless interrogation of a SAWR sensor was successful within the gas cycling test fixture, and successful wireless H2 detection was achieved above 400 C.

Ayes Moncada, Armando↗