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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Flux Improvement based on Machine Learning for the CERES FluxByCldTyp Data Product

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRS1976

Helios mission support

The successful flight of the Helios-1 spacecraft continues as it approaches its third aphelion. The Helios-2 spacecraft has entered its first superior conjunction and continues in excellent health. Much valuable scientific information about the solar corona and its effects on electromagnetic waves is expected to be learned from data gathered during this superior conjunction.

Goodwin, P. S.↗
NASA NTRS1991

Radar signal categorization using a neural network

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

Anderson, James A.↗
NASA NTRS1999

Digitized Educational Technology: A Learning Tool Using Remotely Sensed Data

Digitized Educational software for different levels of instruction were developed and placed on the web (geocities). Students attending the Pre-Engineering Summer 1998 Camp at Dillard University explored the use of the software which included presentations, applications, and special exercises. Student comments were received and considered for adjustments. The second outreach program included students from Colton Junior High School and Natural Science Majors at Dillard University. The Natural Majors completed a second survey concerning reasons why students selected majors in the Sciences and Mathematics. Two student research assistants (DU) and faculty members/parents of Colton Junior High assisted.

Love, Gloria Carter↗
NASA NTRS1994

A Guide to the Literature on Learning Graphical Models

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Buntine, Wray L.↗
NASA NTRS2001

Training Data Optimized and Conditioned to Learn Characteristic Patterns of Vibrating Blisks and Fan Blades

At the NASA Glenn Research Center, we have been training artificial neural networks to interpret the characteristic patterns (see the leftmost image) generated from electronic holograms of vibrating structures. These patterns not only visualize the vibration properties of structures, but small changes in the patterns can indicate structural changes, cracking, or damage. Neural networks detect these small changes well. Our objective has been to adapt the neural-network, electronic-holography combination for inspecting components in Glenn's Spin Rig.

Decker, Arthur J.↗
NASA NTRS2000

Probing Below the Surface of Mars

In this activity, students will record and graph temperature data to learn about NASA's Mars Microprobe Mission, Deep Space 2, and how the properties of a material affect the transfer of heat.

Deep Space 2 Mars Microprobes classroom experiment↗
NASA NTRS2010

Photogrammetric Measurements of an EH-60L Brownout Cloud

There is a critical lack of quantitative data regarding the mechanism of brownout cloud formation. Recognizing this, tests were conducted during the Air Force Research Lab 3D-LZ Brownout Test at the US Army Yuma Proving Ground. Photogrammetry was utilized during two rounds of flight tests with an instrumented EH-60L Black Hawk to determine if this technique could quantitatively measure the formation and evolution of a brownout cloud. Specific areas of interest include the location, size, and average convective velocity of the cloud, along with the characteristics of any defined structures within it. Following the first flight test, photogrammetric data were validated through comparison with onboard vehicle data. Lessons learned from this test were applied to the development of an improved photogrammetry system. A second flight test, utilizing the improved system, demonstrated that obtaining quantitative measurements of the brownout cloud are possible. Results from these measurements are presented in the paper. Flow visualization with chalk dust seeding was also tested. It was observed that pickup forces of the brownout cloud appear to be very low. Overall, these tests demonstrate the viability of photogrammetry as a means for quantifying brownout cloud formation and evolution.

Wong, Oliver D.↗
NASA NTRS2015

Technology Alignment and Portfolio Prioritization (TAPP): Advanced Methods in Strategic Analysis, Technology Forecasting and Long Term Planning for Human Exploration and Operations, Advanced Exploration Systems and Advanced Concepts

The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision­-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi­‐criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.

Funaro, Gregory V.↗
NASA NTRS2016

Use and Protection of GPS Sidelobe Signals for Enhanced Navigation Performance in High Earth Orbit

GPS (Global Positioning System) Space Service Volume (SSV) signal environment is from 3,000-36,000 kilometers altitude. Current SSV specifications only capture performance provided by signals transmitted within 23.5(L1) or 26(L2-L5) off-nadir angle. Recent on-orbit data lessons learned show significant PNT (Positioning, Navigation and Timing) performance improvements when the full aggregate signal is used. Numerous military civil operational missions in High Geosynchronous Earth Orbit (HEOGEO) utilize the full signal to enhance vehicle PNT performance

Navigation Satellites↗
NASA NTRS2017

The Pathway to a Safe and Effective Medication Formulary for Exploration Spaceflight

PURPOSE: Exploration space missions pose several challenges to providing a comprehensive medication formulary designed to accommodate the size and space limitations of the spacecraft; while addressing the individual medications needs and preferences of the Crew; the negative outcome of a degrading inventory over time, the inability to resupply before expiration dates; and the need to properly forecast the best possible medication candidates to treat conditions that will occur in the future. METHODS: The Pharmacotherapeutics Discipline has partnered with the Exploration Medical Capabilities (ExMC) Element to develop and propose a research pathway that is comprehensively focused on evidence-based models and theories, as well as on new diagnostic tools and treatments or preventive measures aimed at closure of the Med02 “Pharmacy” Gap; defined in the Human Research Program’s (HRP) risk-based research strategy. The Med02 Gap promotes the challenge to identify a strategy to ensure that medications used to treat medical conditions during exploration space missions are available, safe, and effective. It is abundantly clear that pharmaceutical intervention is an essential component of risk management planning for astronaut healthcare during exploration space. However, the quandary still remains of how to assemble a formulary that is comprehensive enough to prevent or treat anticipated medical events; and is also chemically stable, safe, and robust enough to have sufficient potency to last for the duration of an exploration space mission. In cases where that is not possible, addressing this Gap requires exploration of novel drug development techniques, dosage forms, and dosage delivery platforms that enhance chemical stability as well as therapeutic effectiveness. RESULTS: The proposed research pathway outlines the steps, processes, procedures, and a research portfolio aimed at identifying a capability that will provide a safe and effective pharmacy for any specific exploration Design Reference Mission (DRM). The proposed approach to building this research portfolio is to seek research projects that concentrate on four major focus areas; (1) Formulary selection, (2) Formulary potency and shelf life, (3) Formulary safety and toxicity, and (4) Novel technology and innovation such as portable real-time chemical analysis innovative drug therapies and dosage and delivery platforms. CONCLUSION: The research pathway has been completed and presented to the HRP. In spring 2017, it is scheduled to be reviewed by a panel of pharmaceutical and clinical experts that will evaluate the scientific merit and operational feasibility of the research pathway, as well as make suggestions for any warranted additions or improvements. Once finalized, the ExMC Element will proceed with the execution of this research pathway with the goal of gathering as much data, and learning as much as possible, to provide a safe and effective pharmaceutical formulary for use during exploration missions.

Daniels, V. R.↗
NASA NTRS2018

Human in the Loop Tests: Experiences in Space Suit Testing

What is a space suit? Space suit testing; How and why we test them; hardware design vs. user functionality; Data collection lessons learned; Challenges of objective and subjective data; Personal experience from 2 perspectives: test director and test subject

Ross, Amy J.↗
NASA NTRS2020

Space Flight LiDARs, Navigation & Science Instrument Implementations: Lasers, Optoelectronics, Integrated Photonics, Fiber Optic Subsystems and Components

For the past 25 years, the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center's Photonics Group in the Engineering Directorate has been substantially contributing to the flight design, development, production, testing and integration of many science and navigational instruments. The Moon to Mars initiative will rely heavily upon utilizing commercial technologies for instrumentation with aggressive schedule deadlines. The group has an extensive background in screening, qualifying, development and integration of commercial components for spaceflight applications. By remaining adaptable and employing a rigorous approach to component and instrument development, they have forged and fostered relationships with industry partners. They have been willing to communicate lessons learned in packaging, part construction, materials selection, testing, and other facets of the design and production process critical to implementation for high-reliability systems. As a result, this successful collaboration with industry vendors and component suppliers has enabled a history of mission success from the Moon to Mars (and beyond) while balancing cost, schedule, and risk postures. In cases where no commercial components exist, the group works closely with other teams at Goddard Space Flight Center and other NASA field centers to fabricate and produce flight hardware for science, remote sensing, and navigation applications. Summarized here is the last ten years of instrumentation development lessons learned and data collected from the subsystems down to the optoelectronic component level.

Detectors↗
NASA NTRSDate not supplied

Photonics for Space Flight Instruments

For the past 25 years, the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center’s Photonics Group in the Engineering Directorate has been substantially contributing to the flight design, development, production, testing and integration of many science and navigational instruments. The group has an extensive background in screening, qualifying, development and integration of commercial components for spaceflight applications. They have been willing to communicate lessons learned in packaging, part construction, materials selection, testing, and other facets of the design and production process critical to implementation for high-reliability systems. Summarized here is the last ten years of instrumentation development lessons learned and data collected from the subsystems down to the optoelectronic component level.

Spaceflight↗