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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗
NASA NTRS2024

Deeper Dive into Interoperability and Its Implications for LunaNet Communications and Navigation Services

The Artemis program being developed by United States’ (US) National Aeronautics and Space Administration (NASA) is developing the capabilities to return humans to the Moon and establish an initial base camp and associated infrastructure with extensive contributions from international and commercial partners. In planning for cislunar exploration and science missions, space agencies are collaborating to enable communications, networking, and Positioning, Navigation, and Timing (PNT) systems—called LunaNet—to exchange information and provide services to cislunar spacecraft and space systems, thus helping each other to achieve their shared goals. To achieve commonality and lower cost for mutual benefit, the strategy of interoperability is being adopted to help fit all the pieces together and function smoothly. Facilitating interoperability should benefit lunar missions by providing the ability to operate in a collaborative environment similar to the terrestrial Internet. Interoperability allows them to share information, navigate safely despite increasing radio frequency congestion, and follow common processes and procedures for effective joint operations. Unlike prior government-dominated efforts, this ecosystem is expected to include and benefit for-profit (commercial) businesses, non-profit organizations, and academic institutions as active stakeholders. Ultimately, the goal is to enable a cislunar ecosystem of service providers and users to contribute to and/or utilize infrastructure and capabilities to achieve mission objectives that span the full range of human endeavors while supporting a variety of business models. This approach enables a Systems of Systems (SoS), such as a Network-of-Networks, to be sustainable in the context of the LunaNet ecosystem as systems evolve over time in technologies, standards, components, and user applications. This paper reports on the results of an effort to help frame the development of the international LunaNet architecture by providing a canonical definition of interoperability broad enough to meet these needs, examining architectural and operational implications of the definition, and exploring interoperability strategies and tactics to deploy and evolve the services proposed for cislunar exploration and science missions.

interoperability↗
NASA NTRSDate not supplied

A Natural Language Understanding Approach for Digitizing Aircraft Ground Taxi Instructions

Advancements in natural language processing (NLP) technologies offer a unique opportunity to furnish aircraft crews, primarily pilots, with digital instructions for taxiing operations. Digital taxi instructions, delivered either as text or graphics, can streamline taxiing procedures, thereby reducing radio congestion, minimizing communication errors, and enhancing aircraft monitoring. Techniques used for natural language understanding (NLU), a subset of NLP focused on machine comprehension of natural language, can extract taxi instructions directly from verbal radio communications. This capability paves the way for implementing a digital taxi communication framework with minimal adjustments to the existing air traffic controller operations. This paper delves into a novel application of NLU: the automated generation of digital taxi instructions from air traffic controller speech. We detail the development of an annotation scheme to represent aircraft ground traffic communications within the US National Airspace System (NAS), employing intent classification (IC) and slot filling (SF) to extract taxi instructions using NLU models. Several neural network models were trained on a dataset annotated with our scheme, achieving notable accuracy and F1 scores. Our research demonstrates the feasibility of using NLU to automatically generate digital taxi instructions, showcasing its potential to streamline the implementation of digital taxi communications.

LSTM↗
NASA NTRSDate not supplied

Development of Candidate Airspace Procedures for Urban Air Mobility in the Dallas Area

As the concept of on-demand electric air taxis gains popularity and begins to take shape, many research efforts are underway to address the challenges of integrating this new class of passenger-carrying air vehicles into the already congested U.S. National Airspace System. Through collaborations between the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration, and industry, this concept, commonly referred to as Urban Air Mobility (UAM), envisions a safe, reliable, and efficient mode of transportation traversing metropolitan and urban areas. Among the many challenges being tackled, airspace procedures and information requirements are critical areas of research that need to be addressed. In a joint effort between NASA and Joby Aviation, a human-in-the-loop study was conducted at the NASA Ames Research Center to evaluate initial and midterm operations with air traffic controllers and on-board UAM pilots in the Dallas area. This area was chosen due to its complex Class Bravo Airspace that extends to the surface over a relatively large area. To achieve the goals and objectives of the study, multiple preceding efforts were conducted to develop the candidate procedures and information requirements. This paper provides an overview of the operational concept used for the study, the process that was followed, and the findings from the two tabletop meetings and a shakedown activity. The combination of these led to a set of airspace procedures, letters of agreement, and information requirements that were evaluated in the human-inthe-loop study.

Urban Air Mobility↗
NASA NTRSDate not supplied

Usability of an Updated Version of the Supplemental Data Service Provider-Consolidated Dashboard for Supporting Uncrewed Aircraft System Traffic Management

The Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD) is a preflight planning user interface (UI) that serves to aid operators when drafting routes for small uncrewed aircraft systems (sUASs). The primary function of the SDSP-CD is to identify hazards that an sUAS may encounter along a proposed flight path and assess the severity of these risks. A usability study was conducted on an updated version of the SDSP-CD to determine if the most recent iterations made to the system improved objective performance and subjective user experience. There are two main components of the SDSP-CD interface: (1) the dashboard and (2) the interactive map. The dashboard provides users with hazard and vehicle limitations for each sUAS in their fleet while the map contains a graphical representation of each vehicle’s route, hazard details, and geographic information. A series of preflight risk-assessment questions and tasks were developed to examine how participants interact with the updated version of the SDSP-CD. Additionally, a new service that measures vertiport congestion was developed and included as one of the services that was tested. In the present study, participants were trained to use the SDSP-CD and then completed two simulated scenarios during which they performed a variety of tasks, responded to questions, and completed surveys. The two scenarios developed for the present study were the Package Delivery and Hurricane Preparation scenarios. The Package Delivery scenario involved a fleet of four sUASs delivering low-stakes items (e.g., lunches and snacks) to people in a fictitious city. The Hurricane Preparation scenario involved a fleet of 11 sUASs delivering a range of supplies (from medicine to boardgames) to employees stranded at an office park due to road closures caused by an impending hurricane. Participants assumed the role of a fleet manager during both scenarios and were responsible for managing the sUASs in their fleet. Questions included those with objectively correct responses, open-ended strategy responses, and subjective user experience feedback. It was found that participants were largely successful at using the SDSP-CD interface to answer questions with objectively correct responses. Additionally, participants were able to use reasoning and logic based on the information available in the SDSP-CD to determine the cause of various risks and what actions they would consider taking. Finally, although participants reported that there were elements of the UI that could be improved, overall feedback pertaining to user experience suggested that the SDSP-CD concept is viable.

usability testing↗
NASA NTRSDate not supplied

Development of a Safety Hazards Risk Assessment Tool for Uncrewed Aircraft System Traffic Management during Preflight Planning

Tremendous growth in the uncrewed and remotely piloted vehicle market is expected in low-altitude, uncontrolled airspace, resulting in potential decreases in safety without systems that support monitoring, assessing, and mitigating risk. At NASA, the System-Wide Safety (SWS) project has been developing a suite of data-driven tools to predict hazards so that the potential risks that these hazards pose can be mitigated. Services to predict various hazards have been developed, including battery capacity, proximity to static obstacles, population risks, global positioning system signal strength, radio frequency spectrum interference risk, and vertiport congestion. These services can monitor hazards along a flight path and if any risks posed by these hazards exceed a threshold, the uncrewed aircraft system (UAS) fleet manager can be alerted to mitigate the risk by modifying the flight path, changing the scheduled departure or arrival times, and/or diverting the vehicle to an alternate vertiport. These services were originally developed to monitor and assess risks during flight, but they have been adapted to assess hazard risks prior to departure so that a fleet manager can evaluate the potential risks for a fleet of UAS along their planned flight paths. These services have been integrated into a prototype tool called the Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD), developed at NASA Ames Research Center. The tool consists of a dashboard which provides a comprehensive overview for a number of risks and a map display that shows the details of the hazards along each flight’s path. Based on the findings from three previous studies, the SDSP-CD has been updated with new design elements and functions. In this paper, we describe lessons learned from the previous studies, changes made to the interface, and the feedback received during a follow-up usability study. Overall, participants reported that there is a substantial benefit of having a fleet manager use a consolidated dashboard to assess hazards for the vehicles in their fleet and to provide situational awareness to potential risks so that they can be mitigated prior to flight. Once the SDSP-CD matures, it will need to be integrated into flight and mission planning tools. Some initial thoughts on how this integration should be accomplished are shared in this paper. Finally, the functional differences between preflight vs. in-flight risk assessment and the differences in fleet manager vs. UAS pilot roles that may require different information and user interactions are discussed.

preflight↗
NASA NTRSDate not supplied

A Natural Language Understanding Approach for Digitizing Aircraft Ground Taxi Instructions

Advancements in natural language processing (NLP) technologies offer a unique opportunity to furnish aircraft crews, primarily pilots, with digital instructions for taxiing operations. Digital taxi instructions, delivered either as text or graphics, can streamline taxiing procedures, thereby reducing radio congestion, minimizing communication errors, and enhancing aircraft monitoring. Techniques used for natural language understanding (NLU), a subset of NLP focused on machine comprehension of natural language, can extract taxi instructions directly from verbal radio communications. This capability paves the way for implementing a digital taxi communication framework with minimal adjustments to the existing air traffic controller operations. This paper delves into a novel application of NLU: the automated generation of digital taxi instructions from air traffic controller speech. We detail the development of an annotation scheme to represent aircraft ground traffic communications within the US National Airspace System (NAS), employing intent classification (IC) and slot filling (SF) to extract taxi instructions using NLU models. Several neural network models were trained on a dataset annotated with our scheme, achieving notable accuracy and 𝐹1 scores. Our research demonstrates the feasibility of using NLU to automatically generate digital taxi instructions, showcasing its potential to streamline the implementation of digital taxi communications.

ATC↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
DOE OSTI2011

Simulation of Large-Scale HPC Architectures

The Extreme-scale Simulator (xSim) is a recently developed performance investigation toolkit that permits running high-performance computing (HPC) applications in a controlled environment with millions of concurrent execution threads. It allows observing parallel application performance properties in a simulated extreme-scale HPC system to further assist in HPC hardware and application software co-design on the road toward multi-petascale and exascale computing. This paper presents a newly implemented network model for the xSim performance investigation toolkit that is capable of providing simulation support for a variety of HPC network architectures with the appropriate trade-off between simulation scalability and accuracy. The taken approach focuses on a scalable distributed solution with latency and bandwidth restrictions for the simulated network. Different network architectures, such as star, ring, mesh, torus, twisted torus and tree, as well as hierarchical combinations, such as to simulate network-on-chip and network-on-node, are supported. Network traffic congestion modeling is omitted to gain simulation scalability by reducing simulation accuracy.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Contingency Analysis of Cascading Line Outage Events

As the US power systems continue to increase in size and complexity, including the growth of smart grids, larger blackouts due to cascading outages become more likely. Grid congestion is often associated with a cascading collapse leading to a major blackout. Such a collapse is characterized by a self-sustaining sequence of line outages followed by a topology breakup of the network. This paper addresses the implementation and testing of a process for N-k contingency analysis and sequential cascading outage simulation in order to identify potential cascading modes. A modeling approach described in this paper offers a unique capability to identify initiating events that may lead to cascading outages. It predicts the development of cascading events by identifying and visualizing potential cascading tiers. The proposed approach was implemented using a 328-bus simplified SERC power system network. The results of the study indicate that initiating events and possible cascading chains may be identified, ranked and visualized. This approach may be used to improve the reliability of a transmission grid and reduce its vulnerability to cascading outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Enhancing I/O Throughput via Efficient Routing and Placement for Large-scale Parallel File Systems

As storage systems get larger to meet the demands of petascale systems, careful planning must be applied to avoid congestion points and extract the maximum performance. In addition, the large data sets generated by such systems makes it desirable for all compute resources to have common access to this data without needing to copy it to each machine. This paper describes a method of placing I/O close to the storage nodes to minimize contention on Cray s SeaStar2+ network, and extends it to a routed Lustre configuration to gain the same benefits when running against a center-wide file system. Our experiments using half of the resources of Spider the center-wide file system at the Oak Ridge Leadership Computing Facility show that I/O write bandwidth can be improved by up to 45% (from 71.9 to 104 GB/s) for a direct-attached configuration and by 137% (47.6 GB/s to 115 GB/s) for a routed configuration. We demonstrated up to 20.7% reduction in run-time for production scientific applications. With the full Spider system, we demonstrated over 240 GB/s of aggregate bandwidth using our techniques.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact

The variation of household attributes such as income, travel distance, age, household member, and education for different residential areas may generate different market penetration rates for plug-in hybrid electric vehicle (PHEV). Residential areas with higher PHEV ownership could increase peak electric demand locally and require utilities to upgrade the electric distribution infrastructure even though the capacity of the regional power grid is under-utilized. Estimating the future PHEV ownership distribution at the residential household level can help us understand the impact of PHEV fleet on power line congestion, transformer overload and other unforeseen problems at the local residential distribution network level. It can also help utilities manage the timing of recharging demand to maximize load factors and utilization of existing distribution resources. This paper presents a multi agent-based simulation framework for 1) modeling spatial distribution of PHEV ownership at local residential household level, 2) discovering “PHEV hot zones” where PHEV ownership may quickly increase in the near future, and 3) estimating the impacts of the increasing PHEV ownership on the local electric distribution network with different charging strategies. In this paper, we use Knox County, TN as a case study to show the simulation results of the agent-based model (ABM) framework. However, the framework can be easily applied to other local areas in the US.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Steric Effect for Proton, Hydrogen-Atom, andHydride Transfer Reactions with Geometric Isomers of NADH-Model Ruthenium Complexes

Two isomers, [Ru(1)]{sup 2+} (Ru = Ru(bpy){sub 2}, bpy = 2,2{prime}-bipyridine, 1 = 2-(pyrid-2{prime}-yl)-1-azaacridine) and [Ru(2)]{sup 2+} (2 = 3-(pyrid-2{prime}-yl)-4-azaacridine), are bio-inspired model compounds containing the nicotinamide functionality and can serve as precursors for the photogeneration of C-H hydrides for studying reactions pertinent to the photochemical reduction of metal-C{sub 1} complexes and/or carbon dioxide. While it has been shown that the structural differences between the azaacridine ligands of [Ru(1)]{sup 2+} and [Ru(2)]{sup 2+} have a significant effect on the mechanism of formation of the hydride donors, [Ru(1HH)]{sup 2+} and [Ru(2HH)]{sup 2+}, in aqueous solution, we describe the steric implications for proton, net-hydrogen-atom and net-hydride transfer reactions in this work. Protonation of [Ru(2{sup {sm_bullet}-})]{sup +} in aprotic and even protic media is slow compared to that of [Ru(1{sup {sm_bullet}-})]{sup +}. The net hydrogen-atom transfer between *[Ru(1)]{sup 2+} and hydroquinone (H{sub 2}Q) proceeds by one-step EPT, rather than stepwise electron-proton transfer. Such a reaction was not observed for *[Ru(2)]{sup 2+} because the non-coordinated N atom is not easily available for an interaction with H{sub 2}Q. Finally, the rate of the net hydride ion transfer from [Ru(1HH)]{sup 2+} to [Ph{sub 3}C]{sup +} is significantly slower than that of [Ru(2HH)]{sup 2+} owing to steric congestion at the donor site.

14 SOLAR ENERGY↗
DOE OSTI2012

Decision-Support Software for Grid Operators: Transmission Topology Control for Infrastructure Resilience to the Integration of Renewable Generation

GENI Project: The CRA team is developing control technology to help grid operators more actively manage power flows and integrate renewables by optimally turning on and off entire power lines in coordination with traditional control of generation and load resources. The control technology being developed would provide grid operators with tools to help manage transmission congestion by identifying the facilities whose on/off status must change to lower generation costs, increase utilization of renewable resources and improve system reliability. The technology is based on fast optimization algorithms for the near to real-time change in the on/off status of transmission facilities and their software implementation.

ARPA-E↗
DOE OSTI2012

Distributed Power Flow Control: Distributed Power Flow Control using Smart Wires for Energy Routing

GENI Project: Smart Wire Grid is developing a solution for controlling power flow within the electric grid to better manage unused and overall transmission capacity. The 300,000 miles of high-voltage transmission line in the U.S. today are congested and inefficient, with only around 50% of all transmission capacity utilized at any given time. Increased consumer demand should be met in part with more efficient and an economical power flow. Smart Wire Grid’s devices clamp onto existing transmission lines and control the flow of power within—much like how internet routers help allocate bandwidth throughout the web. Smart wires could support greater use of renewable energy by providing more consistent control over how that energy is routed within the grid on a real-time basis. This would lessen the concerns surrounding the grid’s inability to effectively store intermittent energy from renewables for later use.

ARPA-E↗
DOE OSTI2012

Power Flow Controller for Renewables: Transformer-less Unified Power Flow Controller for Wind and Solar Power Transmission

GENI Project: MSU is developing a power flow controller to improve the routing of electricity from renewable sources through existing power lines. The fast, innovative, and lightweight circuitry that MSU is incorporating into its controller will eliminate the need for a separate heavy and expensive transformer, as well as the construction of new transmission lines. MSU’s controller is better suited to control power flows from distributed and intermittent wind and solar power systems than traditional transformer-based controllers are, so it will help to integrate more renewable energy into the grid. MSU‘s power flow controller can be installed anywhere in the existing grid to optimize energy transmission and help reduce transmission congestion.

ARPA-E↗
DOE OSTI2013

Characterizing Walk Trips in communities by Using Data from 2009 National Household Travel Survey, American Community Survey, and Other Sources

Non-motorized travel (i.e. walking and bicycling) are of increasing interest to the transportation profession, especially in context with energy consumption, reducing vehicular congestion, urban development patterns, and promotion of healthier life styles. This research project aimed to identify factors impacting the amount of travel for both walk and bike trips at the Census block group or tract level, using several public and private data sources. The key survey of travel behavior is the 2009 National Household Travel Survey (NHTS) which had over 87,000 walk trips for persons 16 and over, and over 6000 bike trips for persons 16 and over. The NHTS, in conjunction with the Census Bureau s American Community Survey, street density measures using Census Bureau TIGER, WalkScore , Nielsen Claritas employment estimates, and several other sources were used for this study. Stepwise Logistic Regression modeling techniques as well as Discriminant Analysis were applied using the integrated data set. While the models performed reasonably well for walk trips, travel by bike was abandoned due to sparseness of data. This paper discusses data sources utilized and modeling processes conducted under this study. It also presents a summary of findings and addresses data challenges and lesson-learned from this research effort.

NHTS↗