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

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Research and Technology Challenges for Human Data Analysts in Future Safety Management Systems

Enabling new and novel concepts of operations for Advanced Air Mobility poses an important need to evolve current safety management systems (SMS) and is posited to be realized through advances in Machine Learning (ML) Data Sciences and Artificial Intelligence. The “In-time Aviation Safety Management System” (IASMS) concept of operations supports the need to evolve today’s SMS to become more tailorable, scalable, and interoperable in response to forecasted changes expected for the future airspace system. Key to IASMS is integration of proactive and predictive ML algorithms trained to provide “in time” detection and mitigation of hazards and emergent risks through new methods and novel data types. IASMS research and technology development includes human factors design considerations for these systems to include human-system teaming, innovations in human interfaces and management of complex digital data information, human-system interaction/model-based system engineering, and verification and validation for data assurance and trust.

Chad L Stephens↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

An “EPIIC” Vision to Evolve Project Integration, Innovation, and Collaboration with Broad Impact for How NASA Executes Complex Projects

Evolving Project Integration, Innovation, and Collaboration (EPIIC) is a vision defined to transform the way projects manage information to support real-time decisions, capture best practices and lessons learned, perform assessments, and manage risk across a portfolio of projects. The foundational project management needs for data and information will be revolutionized through innovations on how we manage and access that data, implement configuration control, and certify compliance. The embedded intelligence of new interactive data interfaces integrate technical and programmatic data such that near real time analytics can be accomplished to more efficiently and accurately complete systems engineering and project management tasks. The system-wide data analytics that are integrated into customized data interfaces allows the growing team of engineers and managers required to develop and implement major NASA missions the ability to access authoritative source(s) of system information while greatly reducing the labor required to complete system assessments. This would allow, for example, much of what is accomplished in a scheduled design review to take place as needed, between any team members, at any time. An intelligent data interface that rigorously integrates systems engineering and project management information in near real time can provide substantially greater insight for systems engineers, project managers, and the large diverse teams required to complete a complex project. System engineers, programmatic personnel (those who focus on cost, schedule, and risk), the technical engineering disciplines, and project management can realize immediate benefit from the shared vision described herein. Implementation of the vision also enables significant improvements in the performance of the engineered system being developed.

Moreland, Robert J.↗
NASA NTRS1983

Information-Systems Data-Flow Diagram

Single form presents clear picture of entire system. Form giving relational review of data flow well suited to information system planning, analysis, engineering, and management. Used to review data flow for developing system or one already in use.

Blosiu, J. O.↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Advanced Data Acquisition Systems with Self-Healing Circuitry

Kennedy Space Center's Spaceport Engineering & Technology Directorate has developed a data acquisition system that will help drive down the cost of ground launch operations. This system automates both the physical measurement set-up function as well as configuration management documentation. The key element of the system is a self-configuring, self-calibrating, signal-conditioning amplifier that automatically adapts to any sensor to which it is connected. This paper will describe the core technology behind this device and the automated data system in which it has been integrated. The paper will also describe the revolutionary enhancements that are planned for this innovative measurement technology. All measurement electronics devices contain circuitry that, if it fails or degrades, requires the unit to be replaced, adding to the cost of operations. Kennedy Space Center is now developing analog circuits that will be able to detect their own failure and dynamically reconfigure their circuitry to restore themselves to normal operation. This technology will have wide ranging application in all electronic devices used in space and ground systems.

Larson, William E.↗
NASA NTRS2002

Engine System Loads Analysis Compared to Hot-Fire Data

Early implementation of structural dynamics finite element analyses for calculation of design loads is considered common design practice for high volume manufacturing industries such as automotive and aeronautical industries. However with the rarity of rocket engine development programs starts, these tools are relatively new to the design of rocket engines. In the NASA MC-1 engine program, the focus was to reduce the cost-to-weight ratio. The techniques for structural dynamics analysis practices, were tailored in this program to meet both production and structural design goals. Perturbation of rocket engine design parameters resulted in a number of MC-1 load cycles necessary to characterize the impact due to mass and stiffness changes. Evolution of loads and load extraction methodologies, parametric considerations and a discussion of load path sensitivities are important during the design and integration of a new engine system. During the final stages of development, it is important to verify the results of an engine system model to determine the validity of the results. During the final stages of the MC-1 program, hot-fire test results were obtained and compared to the structural design loads calculated by the engine system model. These comparisons are presented in this paper.

Frady, Gregory P.↗
NASA NTRS1978

Collection, processing and dissemination of data for the national solar demonstration program

A national solar data system developed for the DOE by IBM provides for automatic gathering, conversion, transfer, and analysis of demonstration site data. NASA requirements for this system include providing solar site hardware, engineering, data collection, and analysis. The specific tasks include: (1) solar energy system design/integration; (2) developing a site data acquisition subsystem; (3) developing a central data processing system; (4) operating the test facility at Marshall Space Flight Center; (5) collecting and analyzing data. The systematic analysis and evaluation of the data from the National Solar Data System is reflected in a monthly performance report and a solar energy system performance evaluation report.

Day, R. E.↗
NASA NTRS1988

Spacecraft mass estimation, relationships and engine data: Task 1.1 of the lunar base systems study

A collection of scaling equations, weight statements, scaling factors, etc., useful for doing conceptual designs of spacecraft are given. Rules of thumb and methods of calculating quantities of interest are provided. Basic relationships for conventional, and several non-conventional, propulsion systems (nuclear, solar electric and solar thermal) are included. The equations and other data were taken from a number of sources and are not at all consistent with each other in level of detail or method, but provide useful references for early estimation purposes.

Source record↗
NASA NTRS1991

Requirements analysis notebook for the flight data systems definition in the Real-Time Systems Engineering Laboratory (RSEL)

A hybrid requirements analysis methodology was developed, based on the practices actually used in developing a Space Generic Open Avionics Architecture. During the development of this avionics architecture, a method of analysis able to effectively define the requirements for this space avionics architecture was developed. In this methodology, external interfaces and relationships are defined, a static analysis resulting in a static avionics model was developed, operating concepts for simulating the requirements were put together, and a dynamic analysis of the execution needs for the dynamic model operation was planned. The systems engineering approach was used to perform a top down modified structured analysis of a generic space avionics system and to convert actual program results into generic requirements. CASE tools were used to model the analyzed system and automatically generate specifications describing the model's requirements. Lessons learned in the use of CASE tools, the architecture, and the design of the Space Generic Avionics model were established, and a methodology notebook was prepared for NASA. The weaknesses of standard real-time methodologies for practicing systems engineering, such as Structured Analysis and Object Oriented Analysis, were identified.

Wray, Richard B.↗
NASA NTRS2012

Engineering a Multimission Approach to Navigation Ground Data System Operations

The Mission Design and Navigation (MDNAV) Section at the Jet Propulsion Laboratory (JPL) supports many deep space and earth orbiting missions from formulation to end of mission operations. The requirements of these missions are met with a multimission approach to MDNAV ground data system (GDS) infrastructure capable of being shared and allocated in a seamless and consistent manner across missions. The MDNAV computing infrastructure consists of compute clusters, network attached storage, mission support area facilities, and desktop hardware. The multimission architecture allows these assets, and even personnel, to be leveraged effectively across the project lifecycle and across multiple missions simultaneously. It provides a more robust and capable infrastructure to each mission than might be possible if each constructed its own. It also enables a consistent interface and environment within which teams can conduct all mission analysis and navigation functions including: trajectory design; ephemeris generation; orbit determination; maneuver design; and entry, descent, and landing analysis. The savings of these efficiencies more than offset the costs of increased complexity and other challenges that had to be addressed: configuration management, scheduling conflicts, and competition for resources. This paper examines the benefits of the multimission MDNAV ground data system infrastructure, focusing on the hardware and software architecture. The result is an efficient, robust, scalable MDNAV ground data system capable of supporting more than a dozen active missions at once.

Mission Design and Navigation (MDNAV)↗
NASA NTRS1974

ERTS program of the US Army Corps of Engineers

The Army Corps of Engineers research and development efforts associated with the ERTS Program are confined to applications of investigation, design, construction, operation, and maintenance of water resource projects. Problems investigated covered: (1) resource inventory; (2) environmental impact; (3) pollution monitoring; (4) water circulation; (5) sediment transport; (6) data collection systems; (7) engineering; and (8) model verification. These problem areas were investigated in relation to bays, reservoirs, lakes, rivers, coasts, and regions. ERTS-1 imagery has been extremely valuable in developing techniques and is now being used in everyday applications.

Jarman, J. W.↗
NASA NTRS1974

The DLG processor: A data management executive for the engineering design integration (EDIN) system. Volume 1: Engineering description and utilization manual

The DLG processor is a Univac 1100 series computer program designed to read, modify, manipulate, and replace symbolic images. DLG is controlled by a set of user supplied directives and operates from a data base of stratified information which can be merged with the symbolic images. Data bases can be constructed and maintained in the mass storage media using the DLG directive language.

Glatt, C. R.↗
NASA NTRS2012

Lessons Learned from Ares I Upper Stage Structures and Thermal Design

The Ares 1 Upper Stage was part of the vehicle intended to succeed the Space Shuttle as the United States manned spaceflight vehicle. Although the Upper Stage project was cancelled, there were many lessons learned that are applicable to future vehicle design. Lessons learned that are briefly detailed in this Technical Memorandum are for specific technical areas such as tank design, common bulkhead design, thrust oscillation, control of flight and slosh loads, purge and hazardous gas system. In addition, lessons learned from a systems engineering and vehicle integration perspective are also included, such as computer aided design and engineering, scheduling, and data management. The need for detailed systems engineering in the early stages of a project is emphasized throughout this report. The intent is that future projects will be able to apply these lessons learned to keep costs down, schedules brief, and deliver products that perform to the expectations of their customers.

Ahmed, Rafiq↗
NASA NTRS1990

Methods for the development of a bioregenerative life support system

Presented here is a rudimentary approach to designing a life support system based on the utilization of plants and animals. The biggest stumbling block in the initial phases of developing a bioregenerative life support system is encountered in collecting and consolidating the data. If a database existed for the systems engineer so that he or she may have accurate data and a better understanding of biological systems in engineering terms, then the design process would be simplified. Also addressed is a means of evaluating the subsystems chosen. These subsystems are unified into a common metric, kilograms of mass, and normalized in relation to the throughput of a few basic elements. The initial integration of these subsystems is based on input/output masses and eventually balanced to a point of operation within the inherent performance ranges of the organisms chosen. At this point, it becomes necessary to go beyond the simplifying assumptions of simple mass relationships and further define for each organism the processes used to manipulate the throughput matter. Mainly considered here is the fact that these organisms perform input/output functions on differing timescales, thus establishing the need for buffer volumes or appropriate subsystem phasing. At each point in a systematic design it is necessary to disturb the system and discern its sensitivity to the disturbance. This can be done either through the introduction of a catastrophic failure or by applying a small perturbation to the system. One example is increasing the crew size. Here the wide range of performance characteristics once again shows that biological systems have an inherent advantage in responding to systemic perturbations. Since the design of any space-based system depends on mass, power, and volume requirements, each subsystem must be evaluated in these terms.

Goldman, Michelle↗
NASA NTRS1986

Implementation of a data management software system for SSME test history data

The implementation of a software system for managing Space Shuttle Main Engine (SSME) test/flight historical data is presented. The software system uses the database management system RIM7 for primary data storage and routine data management, but includes several FORTRAN programs, described here, which provide customized access to the RIM7 database. The consolidation, modification, and transfer of data from the database THIST, to the RIM7 database THISRM is discussed. The RIM7 utility modules for generating some standard reports from THISRM and performing some routine updating and maintenance are briefly described. The FORTRAN accessing programs described include programs for initial loading of large data sets into the database, capturing data from files for database inclusion, and producing specialized statistical reports which cannot be provided by the RIM7 report generator utility. An expert system tutorial, constructed using the expert system shell product INSIGHT2, is described. Finally, a potential expert system, which would analyze data in the database, is outlined. This system could use INSIGHT2 as well and would take advantage of RIM7's compatibility with the microcomputer database system RBase 5000.

Abernethy, Kenneth↗
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↗