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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Proceeding With Caution

E-ViEWS was developed with assistance from the Technology Affiliates Program at NASA's Jet Propulsion Laboratory (JPL). The system incorporates JPL expertise in the areas of systems engineering, transportation systems, antennas, controls, optical displays, and mechanical systems. It consists of three modules that streamline traffic flow in the presence of emergency vehicles, and act as a guardrail to protect today's motorists from distractions that could result in serious accidents. Although emergency vehicles use sirens and flashing lights to warn others as they rapidly pass through intersections, some drivers may be oblivious to the emergency situation at hand, due to factors such as car radios, cellular phones, air conditioning, rolled-up windows, vehicle sound proofing, and hearing impairment. The company has also the company has launched testing efforts for Intellirail, a highly intelligent locomotive warning system that is based on the JPL/E-ViEWS preemption emergency vehicle platform.

Source record↗
NASA NTRS2004

Preliminary Assessment of Mars Exploration Rover Landing Site Predictions

Selection of the Mars Exploration Rover (MER) landing sites took place over a three year period in which engineering constraints were identified, 155 possible sites were downselected to the final two, surface environments and safety considerations were developed, and the potential science return at the sites was considered. Landing sites in Gusev crater and Meridiani Planum were selected because they appeared acceptably safe for MER landing and roving and had strong morphologic and mineralogical indicators of liquid water in their past and thus appeared capable of addressing the science objectives of the MER missions, which are to determine the aqueous, climatic, and geologic history of sites on Mars where conditions may have been favorable to the preservation of evidence of possible pre-biotic or biotic processes. Engineering constraints important to the selection included: latitude (10 N-15 S) for maximum solar power; elevation (<-1.3 km) for sufficient atmosphere to slow the lander; low horizontal winds, shear and turbulence in the last few kilometers to minimize horizontal velocity; low 10-m scale slopes to reduce airbag spinup and bounce; moderate rock abundance to reduce abrasion or stroke-out of the airbags; and a radar-reflective, load-bearing and trafficable surface safe for landing and roving that is not dominated by fine-grained dust. In selecting the MER landing sites these engineering constraints were addressed via comprehensive evaluation of surface and atmospheric characteristics from existing remote sensing data and models as well as targeted orbital information acquired from Mars Global Surveyor and Mars Odyssey. This evaluation resulted in a number of predictions of the surface characteristics of the sites, which are tested in this abstract. Relating remote sensing signatures to surface characteristics at landing sites allows these sites to be used as ground truth for the orbital data, is essential for selecting and validating landing sites for future missions, and is required for correctly interpreting the surfaces and materials globally present on Mars.

Golombek, M.↗
NASA NTRSDate not supplied

Aircraft Engine Particle Emissions during the 2023 NASA-Boeing ecoDemonstrator Field Measurements

Aerosol aircraft emissions are responsible for the formation of contrail cirrus clouds which can alter Earth’s radiation and water budgets exerting substantial effects on climate. However, at present, the impact of aviation on climate forcing remaining uncertain. Recently, NASA has collaborated with Boeing ecoDemonstrator program to improve the understanding of air traffic emissions and the impact of different jet fuel compositions on contrail microphysics. As part of the program, in October 2023 the NASA DC-8 aircraft was used to sample engine exhausts emitted by a Boeing ecoDemonstrator Explorer 737-10 burning sustainable aviation fuel (SAF) and conventional jet fuel over the north western US. In this study, we present unique observations of aerosols properties relevant for contrail-cirrus cloud formation collected during the 2023 ecoDemonstrator research flights and we will discuss the variations of key microphysical contrail parameters in function of different jet fuel compositions and engine conditions.

Francesca Gallo↗
NASA NTRS2000

1999 NASA Seal/Secondary Air System Workshop

NASA Glenn hosted the Seals/Secondary Air System Workshop on October 2829, 1999. Each year NASA and our industry and university partners share their respective seal technology development. We use these workshops as a technical forum to exchange recent advancements and "lessons-learned" in advancing seal technology and solving problems of common interest. As in the past we are publishing two volumes. Volume 1 will be publicly available and will be made available on-line through the web page address listed at the end of this chapter. Volume 2 will be restricted under International Traffic and Arms Regulations (I.T.A.R.) In this conference participants gained an appreciation of NASA's new Ultra Efficient Engine Technology (UEET) program and how this program will be partnering with ongoing DOE -industrial power production and DOD- military aircraft engine programs. In addition to gaining a deeper understanding into sealing advancements and challenges that lie ahead, participants gained new working and personal relationships with the attendees. When the seals and secondary fluid management program was initiated, the emphasis was on rocket engines with spinoffs to gas turbines. Today, the opposite is true and we are, again building our involvement in the rocket engine and space vehicle demonstration programs.

Steinetz, Bruce M.↗
NASA NTRS2017

Evaluation of Early Ground Control Station Configurations for Interacting with a UAS Traffic Management (UTM) System

The purpose of this paper is to report on a human factors evaluation of ground control station design concepts for interacting with an unmanned traffic management system. The data collected for this paper comes from recent field tests for NASA's Unmanned Traffic Management (UTM) project, and covers the following topics; workload, situation awareness, as well as flight crew communication, coordination, and procedures. The goal of this evaluation was to determine if the various software implementations for interacting with the UTM system can be described and classified into design concepts to provide guidance for the development of future UTM interfaces. We begin with a brief description of NASA's UTM project, followed by a description of the test range configuration related to a second development phase. We identified (post hoc) two classes in which the ground control stations could be grouped. This grouping was based on level of display integration. The analysis was exploratory and informal. It was conducted to compare ground stations across those two classes and against the aforementioned topics. Herein, we discuss the results.

unmanned traffic management↗
NASA NTRS1972

Systems simulation for an airport trailing vortex warning system

The approach, development, and limited system studies associated with a system simulation for an Airport Trailing Vortex Warning System are documented. The usefulness is shown of a systems engineering approach to the problem of developing a system, as dictated by aircraft vortices, which will increase air-traffic flow in the takeoff/landing corridors of busy airports while maintaining the required safety factor for each operation. The simulation program has been developed in a modular form which permits new, more sophisticated component models, when they become available and are required, to be incorporated into the program with a minimum of program modifications. This report documents a limited system study that has been performed using this Total System Simulation Model. The resulting preliminary system requirements, conclusions, and recommendations are given.

Jeffreys, H. B.↗
NASA NTRS1975

National law enforcement telecommunications network

Alternative approaches are analyzed to a National Law Enforcement Telecommunications Network (NALECOM) designed to service all state-to-state and state-to-national criminal justice communications traffic needs in the United States. Network topology options were analyzed, and equipment and personnel requirements for each option were defined in accordance with NALECOM functional specifications and design guidelines. Evaluation criteria were developed and applied to each of the options leading to specific conclusions. Detailed treatments of methods for determining traffic requirements, communication line costs, switcher configurations and costs, microwave costs, satellite system configurations and costs, facilities, operations and engineering costs, network delay analysis and network availability analysis are presented. It is concluded that a single regional switcher configuration is the optimum choice based on cost and technical factors. A two-region configuration is competitive. Multiple-region configurations are less competitive due to increasing costs without attending benefits.

Reilly, N. B.↗
NASA NTRS2003

Determination and Applications of Environmental Costs at Different Sized Airports: Aircraft Noise and Engine Emissions

With the increasing trend of charging for externalities and the aim of encouraging the sustainable development of the air transport industry, there is a need to evaluate the social costs of these undesirable side effects, mainly aircraft noise and engine emissions, for different airports. The aircraft noise and engine emissions social costs are calculated in monetary terms for five different airports, ranging from hub airports to small regional airports. The number of residences within different levels of airport noise contours and the aircraft noise classifications are the main determinants for accessing aircraft noise social costs. Whist, based on the damages of different engine pollutants on the human health, vegetation, materials, aquatic ecosystem and climate, the aircraft engine emissions social costs vary from engine types to aircraft categories. The results indicate that the relationship appears to be curvilinear between environmental costs and the traffic volume of an airport. The results and methodology of environmental cost calculation could input for to the proposed European wide harmonized noise charges as well as the social cost benefit analysis of airports.

Lu, Cherie↗
NASA NTRS1992

Lab-scale Lidar Sensing of Diesel Engines Exhausts

Combustion technology and its environmental concerns are being considered with increasing attention, not only for global-scale effects, but also for toxicological implications, particularly in the lift conditions of traffic-congested areas and industrial sites. Majority combustion by-products (CO, NO(sub x)) and unburned hydrocarbons (HC), are already subject to increasingly severe regulations; however other, non-regulated minority species, mainly soot and heavy aromatic molecules, involve higher health risks, as they are suspected to be agents of serious pathologies and even mutagenic effects. This is but one of the reasons why much research work is being carried out worldwide on the physical properties of these substances. Correspondingly, the need arises to detect their presence in urban environments, with as high a sensitivity as is required by their low concentrations, proper time- and space-resolutions, and 'real-time' capabilities. Lidar techniques are excellent candidates to this purpose, although severe constraints limit their applicability, eye-safety problems and aerosol Mie scattering uncertainties above all. At CNR's Istituto Motori in Napels, a Lidar-like diagnostic system is being developed, aimed primarily at monitoring the dynamic behavior of internal combustion engines, particularly diesel exhausts, and at exploring the feasibility of a so-called 'Downtown Lidar'.

Borghese, A.↗
NASA NTRS1975

Software engineering of a navigation and guidance system for commercial aircraft

The avionics experimental configuration of the considered system is briefly reviewed, taking into account the concept of an advanced air traffic management system, flight critical and noncritical functions, and display system characteristics. Cockpit displays and the navigation computer are examined. Attention is given to the functions performed in the navigation computer, major programs in the navigation computer, and questions of software development.

Lachmann, S. G.↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT - Poster

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗
NASA NTRS2024

The Knowledge-based Digital Platform Concept for Advanced Air Mobility Research and Development

National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers (SE) together across organizational boundaries. The overarching vision for the KbDP Concept for AAM R&D is a substantial undertaking. The initial concept and implementation will focus on UAM operations to tractably learn and adjust the concept with a manageable database. Lessons learned and best practices with a smaller scope will enable successful scalability to AAM R&D or even to the entire modes of transportation and logistics. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. Initial implementation of three KbDP concepts of use shows promising benefits to NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject. It is envisioned that the KbDP will manage an information database defined by mathematical, data science, and system engineering principles. AIML algorithms play a vital role in this KbDP concept by extracting meaningful knowledge from the information database, which the human user leverages to improve the efficiency and effectiveness of their research greatly.

ATM↗
NASA NTRS2010

NASA USRP Internship Final Report

The purpose of this report is to describe the body of work I have produced as a NASA USRP intern in the spring 2010. My mentor during this time was Richard Birr and I assisted him with many tasks in the advanced systems group in the engineering design lab at NASA's Kennedy space center. The main priority was and scenario modeling for the FAA's next generation air traffic control system and also developing next generation range systems for implementation at Kennedy space center. Also of importance was the development of wiring diagrams for the portable communications terminal for the desert rats program.

Black, Jesse A.↗
NASA NTRS2003

Distributed and Centralized Conflict Management Under Traffic Flow Management Constraints

Current air transportation in the United States relies on a system born half a century ago. While demand for air travel has kept increasing over the years, technologies at the heart of the National Airspace System (NAS) have not been able to follow an adequate evolution. For instance, computers used to centralize flight data in airspace sectors run a software developed in 1972. Safety, as well as certification and portability issues arise as major obstacles for the improvement of the system. The NAS is a structure that has never been designed, but has rather evolved over time. This has many drawbacks, mainly due to a lack of integration and engineering leading to many inefficiencies and losses of performance. To improve the operations, understanding of this complex needs to be built up to a certain level. This work presents research done on Air Traffic Management (ATM) at the level of the en-route sector.

Feron, Eric↗
NASA NTRS1986

Simulation investigation of the effect of the NASA Ames 80-by 120-foot wind tunnel exhaust flow on light aircraft operating in the Moffett field trafffic pattern

A preliminary study of the exhaust flow from the Ames Research Center 80 by 120 Foot Wind Tunnel indicated that the flow might pose a hazard to low-flying light aircraft operating in the Moffett Field traffic pattern. A more extensive evaluation of the potential hazard was undertaken using a fixed-base, piloted simulation of a light, twin-engine, general-aviation aircraft. The simulated aircraft was flown through a model of the wind tunnel exhaust by pilots of varying experience levels to develop a data base of aircraft and pilot reactions. It is shown that a light aircraft would be subjected to a severe disturbance which, depending upon entry condition and pilot reaction, could result in a low-altitude stall or cause damage to the aircraft tail structure.

Streeter, Barry G.↗