Results for “FLEETS” — Page 55

NASA NTRS2021

m:N and Human Autonomy Teaming Concepts for High Density Vertiport Operations

This report focuses on the role of the Fleet Manager (FM) and, in particular, the ways in which automation could support their position as they manage multiple aircraft and operators in a highly dynamic, advanced air mobility (e.g., air taxi) environment, specifically "high-density vertiport" (HDV) regions. Similar to terminal area operations for traditional aviation, operations involving HDVs will need to be highly structured while also remaining resilient to the various contingencies that can happen in that environment. The role of the FM is consistent with an “m:N” architecture, where “m” number of operators cooperatively manage “N” number of vehicles (where “N” is always larger than “m”). In such a paradigm it is critical to provide the operator with the tools and information necessary to manage their fleet safely and navigate the known pitfalls with highly automated, complex systems (e.g., brittleness, insufficient situation awareness, skill degradation). As the field of m:N has expanded as an area of study, a set of higher-level automation concepts have emerged—namely “plays,” “working agreements,” and “human-autonomy teaming” (HAT)—that could support operators in this new role.

human autonomy teaming↗

NASA NTRSDate not supplied

Noise Impact Analysis for Urban Air Mobility in Dallas-Fort Worth Metroplex

This study supports re-design of airspace and procedures for management of Urban Air Mobility (UAM) fleet operations in a given metropolitan area. A goal of UAM is to maintain geographic separation of UAM flight tracks from densely populated areas in order to reduce the potential negative community impact of UAM and manage flight trajectory in anticipation of allowable noise levels and current land use. A UAM route inventory and a demographic-data-optimized topographic map are developed for the Dallas-Fort Worth metroplex to identify routes or baseline route segments for reduction of noise impact. Noise contours are computed for baseline route and alternative route design given the desired UAM demand level. The impact of UAM fleet noise is predicted based on predicted number of people and noise-sensitive facilities located inside the noise contours.

Urban Air Mobility↗

NASA NTRSDate not supplied

Noise Impact Analysis for Urban Air Mobility in Dallas-Fort Worth Metroplex

This study supports re-design of airspace and procedures for management of Urban Air Mobility (UAM) fleet operations in a given metropolitan area. A goal of UAM is to maintain geographic separation of UAM flight tracks from densely populated areas in order to reduce the potential negative community impact of UAM and manage flight trajectory in anticipation of allowable noise levels and current land use. A UAM route inventory and a demographic-data-optimized topographic map are developed for the Dallas-Fort Worth metroplex to identify routes or baseline route segments for reduction of noise impact. Noise contours are computed for baseline route and alternative route design given the desired UAM demand level. The impact of UAM fleet noise is predicted based on predicted number of people and noise-sensitive facilities located inside the noise contours.

Urban Air Mobility↗

NASA NTRSDate not supplied

Human-Autonomy Teaming Research in Support of m:N Operations

Since 2019, the NASA and industry partners have been involved in research focused on a novel paradigm for operations of remotely piloted aircraft. This paradigm involves multiple people sharing a fleet of multiple vehicles between them. Referred to as m:N (pronounced “em-to-en”), this configuration describes a ratio where m is the number of operators and N is the number of vehicles. Through force and asset multiplication, the m:N concept seeks to enable a scalable and resilient operation of remotely crewed vehicles. The primary means of obtaining such a robust operation is through allowing a flexible crew of variable size to dynamically attend to the needs of assets in while performing real-time operator workload management. It is in that sense that assets are shared between operators: as needed (such as in events of elevated workload) an operator in an m:N context can “handoff” the responsibility for some amount of assets, nh < N, to be absorbed by the m – 1 crew members on staff. At some time later, these nh assets could be returned to their original owner or they may be further distributed to other crew if called for by the mission. During this panel, I will elaborate on the research activity undertaken by the Human-Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center over the previous three years. The studies conducted by the HAT Lab range from interviews with subject matter experts, a cognitive walkthrough, a task analysis, and two simulation experiments to-date. During experimentation, pilots made use of an advanced Ground Control Station developed by the HAT Lab and industry partners to simulate m:N operations in two large, metropolitan areas of Southern California: Los Angeles and San Diego. Further experimentation planned over the next few years. Results from our research to-date indicates that pilots of a moderately sized fleet of about a dozen remotely crewed aircraft adequately maintained safety performance and situation awareness of their aircraft, even when presented with unexpected situations of heightened workload.

multi-vehicle control↗

NASA NTRSDate not supplied

Theoretical and Simulated Capacity of Urban Air Mobility Airspace Characteristics

As interest in urban air mobility grows, as are the optimistic predictions for potentially hundreds of simultaneous operations within a given metropolitan airspace. This high density of traffic poses a challenge for safe, efficient, and timely operations, particularly when realistic airspace restrictions, fleet sizes, and fluctuating demand are considered. Routes for urban air mobility aircraft, similar to automotive roadways more than other types of airways, are one proposed solution for traffic management. This paper identifies several potential components of urban air mobility routes and compares their numerically predicted maximum capacity with what is achieved in a realistic simulation that includes airspace restrictions, parking limitations, fleet sizes, and uneven demand.

Urban Air Mobility↗

NASA NTRSDate not supplied

Theoretical and Simulated Capacity of Urban Air Mobility Airspace Characteristics

As interest in urban air mobility grows, as are the optimistic predictions for potentially hundreds of simultaneous operations within a given metropolitan airspace. This high density of traffic poses a challenge for safe, efficient, and timely operations, particularly when realistic airspace restrictions, fleet sizes, and fluctuating demand are considered. Routes for urban air mobility aircraft, similar to automotive roadways more than other types of airways, are one proposed solution for traffic management. This paper identifies several potential components of urban air mobility routes and compares their numerically predicted maximum capacity with what is achieved in a realistic simulation that includes airspace restrictions, parking limitations, fleet sizes, and uneven demand.

Urban Air Mobility↗

NASA NTRSDate not supplied

Dynamic Assurance of Autonomous Systems through Ground Control Software

Assurance cases are being increasingly acknowledged as a way to build trust in complex systems with autonomous capabilities [1]. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for the specific mission and operating environment. Such justifications for systems with autonomous capabilities are often based on various probabilistic quantifications [2]. Due to the dynamic nature of the environmental conditions in which these systems operate, as well as the changing nature of the autonomous systems themselves, these probabilistic quantifications cannot be simply estimated once during design time. Rather, they need to be continually evaluated during systems operations to ensure that the assurance case justifications are valid. We refer to the assurance case that combines both the static and dynamic elements as a Dynamic Assurance Case (DAC). Such complex systems with autonomous capabilities are often deployed with a Ground Control Software (GCS) component to enable remote operation. Whether the system is composed of a single unit or a fleet of units, deployed distributed or in remote environments, GCS acts as a window into the behavior of the deployed system. It receives telemetry from the system, issues commands to the system and provides various functionalities to visualize the system performance. We propose a dynamic assurance framework where the GCS acts as a relay between the autonomous system and its DAC. GCS can be used to measure both unit-specific as well as system-wide probabilistic quantifications using the incoming telemetry. We embed these quantifications throughout the DAC as variables that can be updated by external sources. We use the GCS to periodically update these variables, which allows us to continually evaluate the formally defined assurance case justifications. We demonstrate our dynamic assurance framework in the NASA Ames project Troupe1 that aims at developing a fleet of rovers capable of au- tonomously mapping their environment. The rovers work cooperatively, each collecting data for different parts of the environment. Each rover runs an identical core Flight System (cFS) [4] application. Troupe1 uses OpenC3 Cosmos [5] as the ground system, and AdvoCATE [3] to capture the system DAC. We show how we can measure both rover-specific and system-wide quantifications in Cosmos using its Ruby scripting editor and pass them into the DAC modelled in AdvoCATE. Then, we show how these incoming variables can be embedded in different parts of the DAC and how effects of their updates can be observed

Irfan Sljivo↗

NASA NTRSDate not supplied

Differential Mobility Spectrometry on the ISS: Successes and Current Concerns

With the construction of the International Space Station (ISS), crew members moved from the relatively short flights of the Space Shuttle (~ 2 weeks) to missions lasting months to a year. As such, environmental monitoring needs also changed, transitioning from a reliance on archival sampling to a combination of archival sampling and in-flight data collection. In the case of trace volatile organic compound (VOC) monitoring, this in-flight monitoring was performed early on by the Volatile Organic Analyzer (VOA), a gas chromatograph-ion mobility spectrometer, which operated on the ISS for 7 years. This large, fixed-position analyzer was replaced by Air Quality Monitors (AQMs), a pair of small gas chromatograph-differential mobility spectrometers, in the spring of 2013. During their initial half decade of use, and covering multiple sets of units, the AQMs performed well, meeting all of their validation criteria and generally showing excellent accuracy when compared to the now-reduced archival samples. These instruments were also used to perform a survey of the US Operating Segment (US Lab Module, European Columbus Module, and Japanese Pressurized Module), confirming that the atmosphere in this segment is well-mixed. The AQMs were also able to provide important information in contingency situations, such as confirming that no ammonia was present on the ISS following alarms suggesting that the coolant loops had leaked in January 2015. As the fleet of AQMs has aged, though, issues of concern have arisen. These have ranged from pervasive electronics board problems to loss of sensitivity due to carbon dioxide to incorrect identification of compounds. Some of these issues are unavoidable due to the nature of the AQM’s use and preparation prior to flight. However, with the extension of the ISS to 2030 (well past its original 2024 lifetime), the fleet of AQMs will be asked to perform longer, requiring solutions to some of the inherent problems. In this presentation, we will discuss the history of the AQM on the ISS with a focus on more recent results. These results show both the continued usefulness of the AQM for atmospheric monitoring and also the current concerns, including: 1) the incorrect identification of benzene, 2) reporting of elevated isopropanol concentrations, and 3) the loss of the reactant ion peak (RIP) that must be resolved in order for the instrument to continue to serve as the primary trace VOC monitor on the ISS.

W T Wallace↗

NASA NTRSDate not supplied

Human Factors Research Considerations for Terminal Area Urban Air Mobility Operations

In this presentation, we discuss the human factors research challenges from introducing greater levels of automation in a future air transportation concept called Urban Air Mobility (UAM). UAM is an air transportation concept that aims to provide air transportation services to the daily commuter, as well as emergency response and package delivery. The principal innovation over current day large air transport system is the greater distribution of important safety functions to automated and human agents; these functions include air traffic management, traditionally an air traffic controller responsibility. A central aspect of UAM is the development of an automated air traffic manager, whose primary responsibility is to approve airspace access for vehicle operators. Vehicle operator roles may include onboard and remote pilots, as well as a human manager who will supervise an entire fleet. Alternatively, both fleet manager and vehicle operators can be merged into a single role – a feasible option if UAM aircraft are autonomous. In lieu of tower controllers, vertiport managers, with the assistance of automation, will manage arrival and departure schedules between vertiports, as well as supervise surface operations. Our approach here will be to introduce use cases currently being developed by NASA, and then provide preliminary definitions for each of the roles introduced above and how coordination between them can be configured to support the operations within the use cases described. Subsequently, we review the tools and interfaces being developed to support the various roles. To conclude, we present current human factors work related to defining the roles above and suggest future work to advance the UAM concept.

trial planning↗

NASA NTRSDate not supplied

Differential Mobility Spectrometry on the ISS: Successes and Current Concerns

With the construction of the International Space Station (ISS), crew members moved from the relatively short flights of the Space Shuttle (~ 2 weeks) to missions lasting months to a year. As such, environmental monitoring needs also changed, transitioning from a reliance on archival sampling to a combination of archival sampling and in-flight data collection. In the case of trace volatile organic compound (VOC) monitoring, this in-flight monitoring was performed early on by the Volatile Organic Analyzer (VOA), a gas chromatograph-ion mobility spectrometer, which operated on the ISS for 7 years. This large, fixed-position analyzer was replaced by Air Quality Monitors (AQMs), a pair of small gas chromatograph-differential mobility spectrometers, in the spring of 2013. During their initial half decade of use, and covering multiple sets of units, the AQMs performed well, meeting all of their validation criteria and generally showing excellent accuracy when compared to the now-reduced archival samples. These instruments were also used to perform a survey of the US Operating Segment (US Lab Module, European Columbus Module, and Japanese Pressurized Module), confirming that the atmosphere in this segment is well-mixed. The AQMs were also able to provide important information in contingency situations, such as confirming that no ammonia was present on the ISS following alarms suggesting that the coolant loops had leaked in January 2015. As the fleet of AQMs has aged, though, issues of concern have arisen. These have ranged from pervasive electronics board problems to loss of sensitivity due to carbon dioxide to incorrect identification of compounds. Some of these issues are unavoidable due to the nature of the AQM’s use and preparation prior to flight. However, with the extension of the ISS to 2030 (well past its original 2024 lifetime), the fleet of AQMs will be asked to perform longer, requiring solutions to some of the inherent problems. In this presentation, we will discuss the history of the AQM on the ISS with a focus on more recent results. These results show both the continued usefulness of the AQM for atmospheric monitoring and also the current concerns, including: 1) the incorrect identification of benzene, 2) reporting of elevated isopropanol concentrations, and 3) the loss of the reactant ion peak (RIP) that must be resolved in order for the instrument to continue to serve as the primary trace VOC monitor on the ISS.

William T. Wallace↗

NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗

NASA NTRSDate not supplied

Design of Safe Separation Bounds for Temporally Deconflicted Trajectories Under Bounded Uncertainties

This paper explores the derivation of safe separation bounds for a heterogeneous group of~$n$ Uncrewed Aerial Systems (UAS) that are assigned temporally deconflicted trajectories. Compared to spatially deconflicted trajectories, temporal deconfliction can lead to higher traffic capacities and a more efficient use of the available airspace. One challenge with this type of deconfliction is that collisions can occur if some cooperating UAS are behind or ahead of schedule. To overcome this risk, this paper derives a lower bound on the safety distance between two heterogeneous UAS in the presence of bounded uncertainties. This safety distance can be leveraged to inform trajectory generation algorithms. The proposed bound establishes a rigorous safety margin when the fleet deviates from the planned trajectories, both temporally and spatially. For its derivation the paper assumes the UAS implement a distributed coordination algorithm that allows the fleet to maintain their schedules synchronized within a bounded temporal error, and a path-following algorithm that lets the vehicles track a target that moves along the planned trajectory with a bounded spatial error.

autonomy↗

NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗

NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗

NASA NTRSDate not supplied

MISTRAL: Concept and Analysis of Persistent Airborne Localization of GHG Emissions

In this study, we propose a concept for localizing and mitigating greenhouse gases and other emissions from areas of various sizes, using a multiscale, airborne localization fleet, supported by information from satellites. Our objective is a rapidly implementable capability for the reduction of atmospheric methane by empowering the communities in proximity to emissions to actively contribute to methane mitigation via fleet operations by local governments, first responders, and individuals . Fully autonomous operations are economically desirable and technically feasible now. However, the development of regulations for autonomous operations is in early stages. Since rapid impact is critical to reducing emissions, the concept starts with remotely controlled vehicles. As regulations for autonomous operations reach maturity, the concept can be easily transitioned from the remotely piloted to autonomous mode. We report on a preliminary system analysis of the concept. Localization of methane emissions from the Permian Basin oil and gas production site serves as the design reference mission; however, the airborne concept is applicable to a broad range of use cases, and we describe several in this analysis. We consider leveraging satellite and ground-based resources within the airborne concept of operations. The study analyses uninhabited aerial system localization operations under the most conservative assumptions on resources and available information. Even under these assumptions, the concept is shown to be viable for local community operations.

airborne methane localization↗

DOE OSTI2013

Fuel Cycle System Analysis Implications of Sodium-Cooled Metal-Fueled Fast Reactor Transuranic Conversion Ratio

If advanced fuel cycles are to include a large number of fast reactors (FRs), what should be the transuranic (TRU) conversion ratio (CR)? The nuclear energy era started with the assumption that they should be breeder reactors (CR > 1), but the full range of possible CRs eventually received attention. For example, during the recent U.S. Global Nuclear Energy Partnership program, the proposal was burner reactors (CR < 1). Yet, more recently, Massachusetts Institute of Technology's "Future of the Nuclear Fuel Cycle" proposed CR [approximately] 1. Meanwhile, the French company EDF remains focused on breeders. At least one of the reasons for the differences of approach is different fuel cycle objectives. To clarify matters, this paper analyzes the impact of TRU CR on many parameters relevant to fuel cycle systems and therefore spans a broad range of topic areas. The analyses are based on a FR physics parameter scan of TRU CR from 0 to [approximately]1.8 in a sodium-cooled metal-fueled FR (SMFR), in which the fuel from uranium-oxide-fueled light water reactors (LWRs) is recycled directly to FRs and FRs displace LWRs in the fleet. In this instance, the FRs are sodium cooled and metal fueled. Generally, it is assumed that all TRU elements are recycled, which maximizes uranium ore utilization for a given TRU CR and waste radiotoxicity reduction and is consistent with the assumption of used metal fuel separated by electrochemical means. In these analyses, the fuel burnup was constrained by imposing a neutron fluence limit to fuel cladding to the same constant value. This paper first presents static, time-independent measures of performance for the LWR [right arrow] FR fuel cycle, including mass, heat, gamma emission, radiotoxicity, and the two figures of merit for materials for weapon attractiveness developed by C. Bathke et al. No new fuel cycle will achieve a static equilibrium in the foreseeable future. Therefore, additional analyses are shown with dynamic, time-dependent measures of performance including uranium usage, TRU inventory, and radiotoxicity to evaluate the complex impacts of transition from the current uranium-fueled LWR system, and other more realistic impacts that may not be intuited from the time-independent steady-state conditions of the end-state fuel cycle. These analyses were performed using the Verifiable Fuel Cycle Simulation Model VISION. Compared with static calculations, dynamic results paint a different picture of option space and the urgency of starting a FR fleet. For example, in a static analysis, there is a sharp increase in uranium utilization as CR exceeds 1.0 (burner versus breeder). However, in dynamic analyses that examine uranium use over the next 1 to 2 centuries, behavior as CR crosses the 1.0 threshold is smooth, and other parameters such as the time required outside of reactors to recycle fuel become important. Overall, we find that there is no unambiguously superior value of TRU CR; preferences depend on the relative importance of different fuel cycle system objectives.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Online Monitoring of Plant Assets in the Nuclear Industry

Today’s online monitoring technologies provide opportunities to perform predictive and proactive health management of assets within many different industries, in particular the defense and aerospace industries. The nuclear industry can leverage these technologies to enhance safety, productivity, and reliability of the aging fleet of existing nuclear power plants. The U.S. Department of Energy’s Light Water Reactor Sustainability Program is collaborating with the Electric Power Research Institute’s (EPRI’s) Long-Term Operations program to implement online monitoring in existing nuclear power plants. Proactive online monitoring in the nuclear industry is being explored using EPRI’s Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software, a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. This paper focuses on development of asset fault signatures used to assess the health status of generator step-up transformers and emergency diesel generators in nuclear power plants. Asset fault signatures describe the distinctive features based on technical examinations that can be used to detect a specific fault type. Fault signatures are developed based on the results of detailed technical research and on the knowledge and experience of technical experts. The Diagnostic Advisor of the FW-PHM Suite software matches developed fault signatures with operational data to provide early identification of critical faults and troubleshooting advice that could be used to distinguish between faults with similar symptoms. This research is important as it will support the automation of predictive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2014

Evaluation Metrics Applied to Accident Tolerant Fuels

The safe, reliable, and economic operation of the nation’s nuclear power reactor fleet has always been a top priority for the United States’ nuclear industry. Continual improvement of technology, including advanced materials and nuclear fuels, remains central to the industry’s success. Decades of research combined with continual operation have produced steady advancements in technology and have yielded an extensive base of data, experience, and knowledge on light water reactor (LWR) fuel performance under both normal and accident conditions. One of the current missions of the U.S. Department of Energy’s (DOE) Office of Nuclear Energy (NE) is to develop nuclear fuels and claddings with enhanced accident tolerance for use in the current fleet of commercial LWRs or in reactor concepts with design certifications (GEN-III+). Accident tolerance became a focus within advanced LWR research upon direction from Congress following the 2011 Great East Japan Earthquake, resulting tsunami, and subsequent damage to the Fukushima Daiichi nuclear power plant complex. The overall goal of ATF development is to identify alternative fuel system technologies to further enhance the safety, competitiveness and economics of commercial nuclear power. Enhanced accident tolerant fuels would endure loss of active cooling in the reactor core for a considerably longer period of time than the current fuel system while maintaining or improving performance during normal operations. The U.S. DOE is supporting multiple teams to investigate a number of technologies that may improve fuel system response and behavior in accident conditions, with team leadership provided by DOE national laboratories, universities, and the nuclear industry. Concepts under consideration offer both evolutionary and revolutionary changes to the current nuclear fuel system. Mature concepts will be tested in the Advanced Test Reactor at Idaho National Laboratory beginning in Summer 2014 with additional concepts being readied for insertion in fiscal year 2015. This paper provides a brief summary of the proposed evaluation process that would be used to evaluate and prioritize the candidate accident tolerant fuel concepts currently under development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗