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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 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats.

Complex Networks↗

DOE OSTI2025

Key Performance Indicators for Vehicle Grid Integration

Electric vehicle (EV) sales account for a rapidly growing portion of the light-duty vehicle market and a portion of medium and heavy-duty fleet vehicles. However, in many locations, charging stations will require costly utility grid upgrades with long lead times. Today, there are a few methods of smart charge management (SCM) which can reduce the costs and wait times for electric vehicle supply equipment (EVSE) interconnection approvals, as well as reduce impacts of the charging stations on the grid and on EV driver transportation costs. It is crucial for the EV charging industry to understand the vehicle grid integration (VGI) requirements for EVSE to prevent adverse impacts from EVSE interconnections and assure that charging loads and interconnections are affordable.

33 - ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2013

Estimation of Airline Benefits from Avionics Upgrade under Preferential Merge Re-sequence Scheduling

Modernization of the airline fleet avionics is essential to fully enable future technologies and procedures for increasing national airspace system capacity. However in the current national airspace system, system-wide benefits gained by avionics upgrade are not fully directed to aircraft/airlines that upgrade, resulting in slow fleet modernization rate. Preferential merge re-sequence scheduling is a best-equipped-best-served concept designed to incentivize avionics upgrade among airlines by allowing aircraft with new avionics (high-equipped) to be re-sequenced ahead of aircraft without the upgrades (low-equipped) at enroute merge waypoints. The goal of this study is to investigate the potential benefits gained or lost by airlines under a high or low-equipped fleet scenario if preferential merge resequence scheduling is implemented.

air traffic management↗

DOE OSTI2024

Sustainable Port Operations: Powered by NREL

Seaports are vital economic hubs that allow the United States to compete on a global scale. But the heavy vehicles and cargo equipment that enable their operations also emit harmful air pollutants and greenhouse gas emissions. For nearly two decades, National Renewable Energy Laboratory (NREL) researchers have worked toward comprehensive seaport decarbonization. They fuse world-class analysis with deep vehicle and transportation systems knowledge to guide strategic deployment of low- and zero-emissions vehicles, charging and refueling infrastructure, and grid improvements. Together, these capabilities can enable sustainable port operations. This fact sheet outlines major seaport and airport decarbonization capabilities across the laboratory, including: fleet research, energy data, and insights for decarbonization; comprehensive hydrogen infrastructure deployment; optimized charging through grid integration; strategic blueprinting for clean, optimized technology deployment; and integrating diversity, equity, inclusion, and accessibility considerations into decarbonization efforts.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

DOE OSTI2023

Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet

The current light water reactor fleet uses time-based or failure-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging technologies in terms of artificial intelligence (AI) and cloud computing to enable a cost-effective, predictive maintenance strategy. This report examines the feasibility of cloud computing for the nuclear industry’s needs in terms of the cloud’s computing capabilities, feasibility, and regulatory concerns. The technical viability of cloud computing was analyzed using one year worth of data from a boiling water reactor’s safety relief valve. Models were hosted on a local desktop, Idaho National Laboratory’s high-performance computer, and Microsoft Azure. Data was loaded, processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was used to determined that cloud computing has adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, there is a benefit as the plant no longer needs manage their own servers, software, cybersecurity, and IT support staff. Many of these features can be offloaded on to the cloud provider. A comprehensive analysis was completed that showed the current annual cost of operating is more expensive than using cloud computing resources. Lastly, the regulatory framework does not explicitly address AI or autonomous control. Currently, the NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than new regulations to address the use of AI and ML. But since many of the AI applications are focused on non-safety related applications, such as balance-of-plant components, they will likely have little or no regulatory restrictions or necessary approvals. Demonstrating how AI can improve maintenance and operation of these non-safety related systems seems like the likely path forward for implementing AI and cloud computing resources inside nuclear power plants (NPPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗

DOE OSTI2022

Smart Charging for Electric Ride-Hailing Vehicles using Renewables: A San Francisco Case Study

Charging large fleets of electric ride-hailing vehicles (ERVs) is a complex matter that could serve different objectives: lower carbon dioxide emissions, lower monetary expenditures, or maximize solar photovoltaics (PV) energy consumption. Currently, it is unclear how each of those objectives could impact the business and performance of a ride-hailing fleet. In order to fill this gap, this article employs a dynamic transportation model: a smart charging simulation that combines agent-based, discrete-event, and system dynamic modelling by comparing the above-mentioned objectives in separate scenarios. The results show that each scenario successfully manages to shift between 34% and 87% of all load to hours of the day when the objectives of those scenarios are met. Therefore, in comparison to the baseline, smart charging can save between 5% and 26% of monthly emissions and between 4% and 57% of monthly expenditures. The solar PV scenario, however, results in the highest savings, while ensuring profitable economics via net metering in the short- as well as long term. Finally, the sensitivity analysis points to important trade-offs between several fleet performance metrics. The article concludes by giving business and policy recommendations for maximising the economic, energy and environmental efficiency of large ERV fleets.

ADVANCED PROPULSION SYSTEMS,SOLAR ENERGY↗

NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗

NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors Mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗

NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors mounted on Trash-Trucks: Methods Development and Lessons Learned

Drive-by sensing↗

NASA NTRS2011

Preparing for the High Frontier: The Role and Training of NASA Astronauts in the Post- Space Shuttle Era

In May 2010, the National Research Council (NRC) was asked by NASA to address several questions related to the Astronaut Corps. The NRC s Committee on Human Spaceflight Crew Operations was tasked to answer several questions: 1. How should the role and size of the activities managed by the Johnson Space Center Flight Crew Operations Directorate change after space shuttle retirement and completion of the assembly of the International Space Station (ISS)? 2. What are the requirements for crew-related ground-based facilities after the Space Shuttle program ends? 3. Is the fleet of aircraft used for training the Astronaut Corps a cost-effective means of preparing astronauts to meet the requirements of NASA s human spaceflight program? Are there more cost-effective means of meeting these training requirements? Although the future of NASA s human spaceflight program has garnered considerable discussion in recent years and there is considerable uncertainty about what the program will involve in the coming years, the committee was not tasked to address whether human spaceflight should continue or what form it should take. The committee s task restricted it to studying activities managed by the Flight Crew Operations Directorate or those closely related to its activities, such as crew-related ground-based facilities and the training aircraft.

Source record↗

NASA NTRS2010

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed

Simon, Donald L.↗

DOE OSTI2025

Freight Electrification, Moving Goods While Managing Grid Impacts

Analyzing freight data from the Salt Lake City inland port demonstrates significant potential forintegrating battery-electric freight vehicles (BEVs) into key transportation corridors. Our partnership with freight operators like RSD and MWCS, and other companies moving freight around the port, provided valuable fleet telemetry and operationalinsights, highlighting critical corridors on and around I-15 and I-80.

Source record↗

NASA NTRS2011

The Role and Training of NASA Astronauts in the Post-Shuttle Era

In May 2010 the National Research Council (NRC) was asked by NASA to address several questions related to the Astronaut Corps. The NRC's Committee on Human Spaceflight Crew Operations was tasked to: 1. How should the role and size of the activities managed by the Johnson Space Center Flight Crew Operations Directorate change following space shuttle retirement and completion of the assembly of the International Space Station (ISS)? 2. What are the requirements for crew-related ground-based facilities after the Space Shuttle program ends? 3. Is the fleet of aircraft used for training the Astronaut Corps a cost-effective means of preparing astronauts to meet the requirements of NASA's human spaceflight program? Are there more cost-effective means of meeting these training requirements? Although the future of NASA's human spaceflight program has garnered considerable discussion in recent years, and there is considerable uncertainty about what that program will involve in the coming years, the committee was not tasked to address whether or not human spaceflight should continue, or what form it should take. The committee's task restricted it to studying those activities managed by the Flight Crew Operations Directorate, or those closely related to its activities, such as crew-related ground-based facilities and the training aircraft.

Source record↗

DOE OSTI2021

INTERFUEL: FAST - Updates for FY 2021 Reporting

This presentation presents an overview of coming changes to how federal agencies with motor vehicles will submit required information about the motor vehicles, fueling centers, and electric vehicle supply equipment (EVSE) inventory through the Federal Automotive Statistical Tool (FAST). The presentation provides information on changes throughout the FAST system as well as changes planned for implementation later this fall. This presentation is intended for delivery via WebEx/GoToMeeting at the September 15, 2021 meeting of the DOE-sponsored INTERFUEL working group. FAST is a web-based information management tool developed by INL and funded by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

INTERFUEL: FAST - Fueling Center / EVSE Inventory Reporting Update [Slides]

This presentation presents an overview of coming changes to how federal agencies with motor vehicles will submit required information about fueling centers and electric vehicle supply equipment (EVSE) inventory through the Federal Automotive Statistical Tool (FAST). The presentation provides background on the process, a re-cap of how the information is currently submitted, and describes the new approach for organizations to submit the required information this year. This presentation is intended for delivery via WebEx/GoToMeeting at the August 11, 2021 meeting of the DOE-sponsored INTERFUEL working group. FAST is a web-based information management tool developed by INL and funded by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Pillars of Innovation Across Southeast Idaho & the Interstate-15 Corridor

Since 1949, Idaho National Laboratory (INL) has been home to developing civilian and defense nuclear reactor technologies and managing spent nuclear fuel. Today, INL is the nation’s nuclear energy research laboratory, sustaining the safe and efficient operation of existing reactors, powering science in space, and breaking ground on the future fleet of advanced nuclear reactors. INL is only one of many rising technology resources in the region, however. Along the Interstate 15 (I-15) corridor, technology and cybersecurity industries and intellectual assets are rapidly expanding. Creating an innovation hub in this region would unite capabilities to solve current and future challenges in nuclear reactor sustainment and expanded deployment, integrated fuel cycle solutions, integrated energy systems, advanced materials and manufacturing for extreme environments, and secure and resilient cyber-physical systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2017

Virtualized Multi-Mission Operations Center (vMMOC) and its Cloud Services

His presentation will cover, the current and future, technical and organizational opportunities and challenges with virtualizing a multi-mission operations center. The full deployment of Goddard Space Flight Centers (GSFC) Virtualized Multi-Mission Operations Center (vMMOC) is nearly complete. The Space Science Mission Operations (SSMO) organizations spacecraft ACE, Fermi, LRO, MMS(4), OSIRIS-REx, SDO, SOHO, Swift, and Wind are in the process of being fully migrated to the vMMOC. The benefits of the vMMOC will be the normalization and the standardization of IT services, mission operations, maintenance, and development as well as ancillary services and policies such as collaboration tools, change management systems, and IT Security. The vMMOC will also provide operational efficiencies regarding hardware, IT domain expertise, training, maintenance and support.The presentation will also cover SSMO's secure Situational Awareness Dashboard in an integrated, fleet centric, cloud based web services fashion. Additionally the SSMO Telemetry as a Service (TaaS) will be covered, which allows authorized users and processes to access telemetry for the entire SSMO fleet, and for the entirety of each spacecrafts history. Both services leverage cloud services in a secure FISMA High and FedRamp environment, and also leverage distributed object stores in order to house and provide the telemetry. The services are also in the process of leveraging the cloud computing services elasticity and horizontal scalability. In the design phase is the Navigation as a Service (NaaS) which will provide a standardized, efficient, and normalized service for the fleet's space flight dynamics operations. Additional future services that may be considered are Ground Segment as a Service (GSaaS), Telemetry and Command as a Service (TCaaS), Flight Software Simulation as a Service, etc.

Ido, Haisam Kassim↗