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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

SPoRT: Transitioning NASA and NOAA Experimental Data to the Operational Weather Community

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS, Suomi NPP, and planned JPSS and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volumes will likely increase by a factor of 5-10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

Jedlovec, Gary J.↗
NASA NTRSDate not supplied

Lunar Instrument Data Integration Into the Virtual Reality Mission Simulation System for Decision Communication and Situational Awareness

In situ resource utilization (ISRU) technologies are a key advancement required to make human habitation on the Moon and Mars viable. The upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission will provide crucial correlations between volatiles and lunar geology to understand the water content available for ISRU on the moon. The mission will require the coordination of multi-disciplinary teams across the country making real time decisions based on rover instrument data. The virtual reality Mission Simulation System (vMSS) is a virtual reality platform designed at MIT by the Resource Exploration and Science of our Cosmic Environment (RESOURCE) team to provide teams with a collaboration interface for planetary missions like VIPER. Herein we determine the integration pathway for analog based datasets that are examples of VIPER's two main instruments, the near-infrared volatile spectrometer subsystem (NIRVSS) and the neutron spectrometer subsystem (NSS), into vMSS to provide the most valuable visualization tools. Focusing on improving situational awareness, decision making, reducing task load and incorporating comments from scientists working previous analogs and on the current VIPER mission, we recommend critical elements to implement into vMSS and the best approaches for data visualization. We present a review of relevant analogs and state of the art mission software. We have developed a design concept and path to flight of analysed instrument data integrated with data maps that allow for virtual manipulation and annotation between non-co-located team members. We focus on pre-mission mapping of a priori data for improved situational awareness, layering of analysed instrument data, correlative mapping and interactive capabilities for in-mission decision making, as well as archiving and annotation tools for post-mission analysis. Finally, we lay out the roadmap for the future development of immersive sample site visualization capabilities and the use of integrated instrument data in vMSS with automated temporal and geospatial planning.

Cody Alison Paige↗
NASA NTRS2009

CEOS Contributions to Informing Energy Management and Policy Decision Making Using Space-Based Earth Observations

Earth observations are playing an increasingly significant role in informing decision making in the energy sector. In renewable energy applications, space-based observations now routinely augment sparse ground-based observations used as input for renewable energy resource assessment applications. As one of the nine Group on Earth Observations (GEO) societal benefit areas, the enhancement of management and policy decision making in the energy sector is receiving attention in activities conducted by the Committee on Earth Observation Satellites (CEOS). CEOS has become the "space arm" for the implementation of the Global Earth Observation System of Systems (GEOSS) vision. It is directly supporting the space-based, near-term tasks articulated in the GEO three-year work plan. This paper describes a coordinated program of demonstration projects conducted by CEOS member agencies and partners to utilize Earth observations to enhance energy management end-user decision support systems. I discuss the importance of engagement with stakeholders and understanding their decision support needs in successfully increasing the uptake of Earth observation products for societal benefit. Several case studies are presented, demonstrating the importance of providing data sets in formats and units familiar and immediately usable by decision makers. These projects show the utility of Earth observations to enhance renewable energy resource assessment in the developing world, forecast space-weather impacts on the power grid, and improve energy efficiency in the built environment.

Eckman, Richard S.↗
NASA NTRS1981

Space tracking and data systems; Proceedings of the Symposium, Arlington, VA, June 16-18, 1981

The AIAA/NASA Symposium on Space Tracking and Data Systems, held in Pentagon City, Virginia, on June 16-18, 1981, had the purpose of reviewing international activities in space tracking and data systems for civil use in the 1980-2000 time frame. Participants included 225 representatives from industrial and government organizations in eight nations. The nations represented include the United States, France, Germany, India, Japan, Norway, Spain, and Sweden. The major functions of the systems described at the Symposium are related to the initial downlink of telemetry and spacecraft status data, attendant tracking activities, and uplink of spacecraft commands; communication between the associated acquisition sites and central processing and control stations; formulation and implementation of commands that control the spacecraft and its payload; and processing of spacecraft data needed to make command decisions. Attention is given to an overview of current activities and plans, and supporting developments, taking into account the time from 1980 to 1990. New developments are also considered.

Grey, J.↗
NASA NTRS2003

The Virtual Test Bed Project

This is a report of my activities as a NASA Fellow during the summer of 2003 at the NASA Kennedy Space Center (KSC). The core of these activities is the assigned project: the Virtual Test Bed (VTB) from the Spaceport Engineering and Technology Directorate. The VTB Project has its foundations in the NASA Ames Research Center (ARC) Intelligent Launch & Range Operations program (ILRO). The objective of the VTB project is to develop a unique collaborative computing environment where simulation models can be hosted and integrated in a seamless fashion. This collaborative computing environment will have as emphasis operational models. This report will focus on the decisions about the different simulation modeling environments considered, simulation platform development, technology and operational models assessment, and computing infrastructure implementation.

Rabelo, Luis↗
NASA NTRS2019

Fuser and Fuser in the Cloud

The ˜Fuser' is the name of the SWIM data integration service that has been built, utilized and evolved on ATD-2 to synchronize trajectory predictions from multiple decision support tools. NASA is researching ways to make this data stream available for broader consumption as an example implementation that would help reduce the burden for consumers while enabling access to data that would help industry prepare and innovate for TFDM (e.g. integrates TTP feed).

Fuser↗
NASA NTRS2018

Towards Design Principles for Visual Analytics in Operations Contexts

Operations engineering teams interact with complex data systems to make technical decisions that ensure the operational efficacy of their missions. To support these decision-making tasks, which may require elastic prioritization of goals dependent on changing conditions, custom analytics tools are often developed. We were asked to develop such a tool by a team at the NASA Jet Propulsion Laboratory, where rover telecom operators make decisions based on models predicting how much data rovers can transfer from the surface of Mars. Through research, design, implementation, and informal evaluation of our new tool, we developed principles to inform the design of visual analytics systems in operations contexts. We offer these principles as a step towards understanding the complex task of designing these systems. The principles we present are applicable to designers and developers tasked with building analytics systems in domains that face complex operations challenges such as scheduling, routing, and logistics.

Lombeyda, Santiago↗
NASA NTRS2020

Operability on the Europa Clipper Mission: Challenges and Opportunities

Flight and ground system operability has been a focus area on the Europa Clipper Project since early in its formulation phase. This has given the operations team the opportunity to influence the design, with a goal of increasing overall system operability. This paper presents example operability challenges, opportunities, and solutions arising from the pre-Critical Design Review (CDR) system design. The integrated wing assembly design directly couples a scientific instrument (the REASON sounding radar) to the spacecraft’s power source (solar array wing panels). Impacts to mission operations of this design include: increased slew durations; solar array pointing constraints during inner cruise, Europa flybys, and orbit trim maneuvers; and stray light intrusions into the stellar reference units’ keep out zones. The use of CCSDS File Delivery Protocol (CFDP) Class-2 for reliable downlink of the large volume of Europa Clipper science data is described, along with nominal and off-nominal use cases. The effort to improve post-launch spacecraft visibility by adding a third low-gain antenna to the spacecraft is detailed. The design of the bulk data store has necessitated the implementation of accountable data products (ADPs), accountability identifiers (AIDs), and metadata packets to provide end-to-end science data accountability. To streamline and automate the flight rules generation and checking process, a first order and temporal logic-based solution of expressing flight rules without ambiguity, and whose programmatic implementation can be automated, is proposed. The focus on operability has had a positive influence on Europa Clipper design decisions, although cost, schedule, budget, heritage, and other technical concerns have many times outweighed operability concerns. However, experience to date demonstrates that this approach to operability results in more thorough, balanced consideration of the effect of early design trades and decisions on the operations phase of a mission than seen in many previous missions, and provides operations development insight into prioritizing work to go.

Signorelli, Joel↗
NASA NTRS2021

Operability on the Europa Clipper Mission: Challenges and Opportunities

Flight and ground system operability has been a focus area on the Europa Clipper Project since early in its formulation phase. This has given the operations team the opportunity to influence the design, with a goal of increasing overall system operability. This paper presents example operability challenges, opportunities, and solutions arising from the Critical Design Review (CDR) system design. The integrated wing assembly design directly couples a scientific instrument (the REASON sounding radar) to the spacecraft’s power source (solar array wing panels). Impacts to mission operations of this design include: increased slew durations; solar array pointing constraints during inner cruise, Europa flybys, and orbit trim maneuvers; and stray light intrusions into the stellar reference units’ keep out zones. The use of CCSDS File Delivery Protocol (CFDP) Class-2 for reliable downlink of the large volume of Europa Clipper science data is described, along with nominal and off-nominal use cases. The effort to improve post-launch spacecraft visibility by adding a third low-gain antenna to the spacecraft is detailed. The design of the bulk data store has necessitated the implementation of accountable data products (ADPs), accountability identifiers (AIDs), and metadata packets to provide end-to-end science data accountability. To streamline and automate the flight rules generation and checking process, a first order and temporal logic-based solution of expressing flight rules without ambiguity, and whose programmatic implementation can be automated, is proposed. The focus on operability has had a positive influence on Europa Clipper design decisions, although cost, schedule, budget, heritage, and other technical concerns have many times outweighed operability concerns. However, experience to date demonstrates that this approach to operability results in more thorough, balanced consideration of the effect of early design trades and decisions on the operations phase of a mission than seen in many previous missions, and provides operations development insight into prioritizing work to go.

Kumar, Meghana↗
NASA NTRS2006

NASA Langley Research Center Systems Analysis & Concepts Directorate Participation in the Exploration Systems Architecture Study

The NASA Langley Research Center (LaRC) Systems Analysis & Concepts Directorate (SACD) began studying human exploration missions beyond low Earth orbit (LEO) in the year 1999. This included participation in NASA s Decadal Planning Team (DPT), the NASA Exploration Team (NExT), Space Architect studies and Revolutionary Aerospace Systems Concepts (RASC) architecture studies that were used in formulating the new Vision for Space Exploration. In May of 2005, NASA initiated the Exploration Systems Architecture Study (ESAS). The primary outputs of the ESAS activity were concepts and functional requirements for the Crewed Exploration Vehicle (CEV), its supporting launch vehicle infrastructure and identification of supporting technology requirements and investments. An exploration systems analysis capability has evolved to support these functions in the past and continues to evolve to support anticipated future needs. SACD had significant roles in supporting the ESAS study team. SACD personnel performed the liaison function between the ESAS team and the Shuttle/Station Configuration Options Team (S/SCOT), an agency-wide team charged with using the Space Shuttle to complete the International Space Station (ISS) by the end of Fiscal Year (FY) 2010. The most significant of the identified issues involved the ability of the Space Shuttle system to achieve the desired number of flights in the proposed time frame. SACD with support from the Kennedy Space Center performed analysis showing that, without significant investments in improving the shuttle processing flow, that there was almost no possibility of completing the 28-flight sequence by the end of 2010. SACD performed numerous Lunar Surface Access Module (LSAM) trades to define top level element requirements and establish architecture propellant needs. Configuration trades were conducted to determine the impact of varying degrees of segmentation of the living capabilities of the combined descent stage, ascent stage, and other elements. The technology assessment process was developed and implemented by SACD as the ESAS architecture was refined. SACD implemented a rigorous and objective process which included (a) establishing architectural functional needs, (b) collection, synthesis and mapping of technology data, and (c) performing an objective decision analysis resulting in technology development investment recommendations. The investment recommendation provided budget, schedule, and center/program allocations to develop required technologies for the exploration architecture, as well as the identification of other investment opportunities to maximize performance and flexibility while minimizing cost and risk. A summary of the trades performed and methods utilized by SACD for the Exploration Systems Mission Directorate (ESAS) activity is presented along with how SACD is currently supporting the implementation of the Vision for Space Exploration.

Keyes, Jennifer↗
NASA NTRS2012

Onboard Sensor Data Qualification in Human-Rated Launch Vehicles

The avionics system software for human-rated launch vehicles requires an implementation approach that is robust to failures, especially the failure of sensors used to monitor vehicle conditions that might result in an abort determination. Sensor measurements provide the basis for operational decisions on human-rated launch vehicles. This data is often used to assess the health of system or subsystem components, to identify failures, and to take corrective action. An incorrect conclusion and/or response may result if the sensor itself provides faulty data, or if the data provided by the sensor has been corrupted. Operational decisions based on faulty sensor data have the potential to be catastrophic, resulting in loss of mission or loss of crew. To prevent these later situations from occurring, a Modular Architecture and Generalized Methodology for Sensor Data Qualification in Human-rated Launch Vehicles has been developed. Sensor Data Qualification (SDQ) is a set of algorithms that can be implemented in onboard flight software, and can be used to qualify data obtained from flight-critical sensors prior to the data being used by other flight software algorithms. Qualified data has been analyzed by SDQ and is determined to be a true representation of the sensed system state; that is, the sensor data is determined not to be corrupted by sensor faults or signal transmission faults. Sensor data can become corrupted by faults at any point in the signal path between the sensor and the flight computer. Qualifying the sensor data has the benefit of ensuring that erroneous data is identified and flagged before otherwise being used for operational decisions, thus increasing confidence in the response of the other flight software processes using the qualified data, and decreasing the probability of false alarms or missed detections.

Wong, Edmond↗
NASA NTRS2019

Connecting Space to Village: Tethys Adoption Within SERVIR

The SERVIR program, a joint development initiative of National Aeronautics and Space Administration (NASA) and United States Agency for International Development (USAID), works in partnership with leading regional organizations world-wide to help developing countries use information provided by Earth observing satellites and geospatial technologies for managing climate risks and land use. SERVIR empowers decision-makers with tools, products, and services to act locally on critical issues related to four key areas: Food Security and Agriculture, Water and Water-related Disasters, Land Use/Land Cover and Ecosystems, Weather and Climate. SERVIR is improving awareness, increasing access to information, and supporting analysis to help people in West Africa, Eastern and Southern Africa, Hindu Kush-Himalaya (HKH), the Lower Mekong, South America and Mesoamerica. NASA funds an Applied Sciences Team (AST), composed of experts from universities and research institutions around the United States. Through AST, SERVIR adopted the Tethys platform introduced by Brigham Young University (BYU) for the implementation of tools to deliver scientific data to stakeholders in the Hindu Kush-Himalaya region. Several Tethys applications developed within SERVIR have raised interest amongst decision makers in the countries served by our regional hubs. They see these new apps as key vehicles to disseminate complex information related to weather models, hydrological models, and air quality monitoring, and as such the scope of our Tethys efforts continue to expand to other regions. As of July 2019, each one of the SERVIR hubs (with the exception of Amazonia, which has only recently started), have stood up Tethys portals and have at least one individual trained in the implementation and maintenance of a Tethys portal and in the creation of basic applications. HKH, with BYU's valuable support, has implemented very sophisticated applications that showcase the possibilities and the benefits that the Tethys platform brings.

Tethys↗
NASA NTRS2001

Market Assessment of Forward-Looking Turbulence Sensing Systems

In recognition of the importance of turbulence mitigation as a tool to improve aviation safety, NASA's Aviation Safety Program developed a Turbulence Detection and Mitigation Sub-element. The objective of this effort is to develop highly reliable turbulence detection technologies for commercial transport aircraft to sense dangerous turbulence with sufficient time warning so that defensive measures can be implemented and prevent passenger and crew injuries. Current research involves three forward sensing products to improve the cockpit awareness of possible turbulence hazards. X-band radar enhancements will improve the capabilities of current weather radar to detect turbulence associated with convective activity. LIDAR (Light Detection and Ranging) is a laser-based technology that is capable of detecting turbulence in clear air. Finally, a possible Radar-LIDAR hybrid sensor is envisioned to detect the full range of convective and clear air turbulence. To support decisions relating to the development of these three forward-looking turbulence sensor technologies, the objective of this study was defined as examination of cost and implementation metrics. Tasks performed included the identification of cost factors and certification issues, the development and application of an implementation model, and the development of cost budget/targets for installing the turbulence sensor and associated software devices into the commercial transport fleet.

Kauffmann, Paul↗
NASA NTRS1999

Continuous Risk Management at NASA

NPG 7120.5A, "NASA Program and Project Management Processes and Requirements" enacted in April, 1998, requires that "The program or project manager shall apply risk management principles..." The Software Assurance Technology Center (SATC) at NASA GSFC has been tasked with the responsibility for developing and teaching a systems level course for risk management that provides information on how to comply with this edict. The course was developed in conjunction with the Software Engineering Institute at Carnegie Mellon University, then tailored to the NASA systems community. This presentation will briefly discuss the six functions for risk management: (1) Identify the risks in a specific format; (2) Analyze the risk probability, impact/severity, and timeframe; (3) Plan the approach; (4) Track the risk through data compilation and analysis; (5) Control and monitor the risk; (6) Communicate and document the process and decisions. This risk management structure of functions has been taught to projects at all NASA Centers and is being successfully implemented on many projects. This presentation will give project managers the information they need to understand if risk management is to be effectively implemented on their projects at a cost they can afford.

Hammer, Theodore F.↗
NASA NTRS2004

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Hoover, Richard B.↗
NASA NTRS2005

New Technologies for Weather Accident Prevention

Weather is a causal factor in thirty percent of all aviation accidents. Many of these accidents are due to a lack of weather situation awareness by pilots in flight. Improving the strategic and tactical weather information available and its presentation to pilots in flight can enhance weather situation awareness and enable avoidance of adverse conditions. This paper presents technologies for airborne detection, dissemination and display of weather information developed by the National Aeronautics and Space Administration (NASA) in partnership with the Federal Aviation Administration (FAA), National Oceanic and Atmospheric Administration (NOAA), industry and the research community. These technologies, currently in the initial stages of implementation by industry, will provide more precise and timely knowledge of the weather and enable pilots in flight to make decisions that result in safer and more efficient operations.

Stough, H. Paul, III↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗