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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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NASA NTRS2016

Trade-Space Analysis Tool for Constellations (TAT-C)

Traditionally, space missions have relied on relatively large and monolithic satellites, but in the past few years, under a changing technological and economic environment, including instrument and spacecraft miniaturization, scalable launchers, secondary launches as well as hosted payloads, there is growing interest in implementing future NASA missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: How many spacecraft should be included in the constellation? Which design has the best costrisk value? The main goals of TAT-C are to: Handle multiple spacecraft sharing a mission objective, from SmallSats up through flagships, Explore the variables trade space for pre-defined science, cost and risk goals, and pre-defined metrics Optimize cost and performance across multiple instruments and platforms vs. one at a time.This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI, then formulating Trade-space Search Requests for the Trade-space Search Iterator first with inputs from the Knowledge Base, then, in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules, generating multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, streamlining the computations by modeling orbits in a way that balances accuracy and performance.TAT-C current version includes uniform Walker constellations as well as Ad-Hoc constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The Knowledge Base supports both analysis and exploration, and the current GUI prototype automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost.

Science Data Processing↗
NASA NTRSDate not supplied

Visualizing Geospatial Data through ESRI Story Maps for Earth Science Education: Lessons Learned from My NASA Data

For 20 years My NASA Data (MND) has curated NASA Earth science data and provided the data to educators in engaging learner-centered resources. MND has recently featured story maps as an innovative way to engage students in NASA Earth data. A story map is a cloud-based lesson that engages the learner in interactive geospatial maps using NASA data, and other multimedia content, text, and tasks that can be seamlessly incorporated in classroom instruction. This immersive technology eliminates the need for the user to move among tricky interfaces to access and visualize Earth science data, and no special software is required to be downloaded. Each story map integrates data from different NASA satellite missions, retrieved from Distributed Active Archive Centers (DAACs). Story maps also employ data analysis tools, such as time series options and swipe tools that allow learners to view and analyze relationships between scientific variables. MND has produced 25 story map lesson plans on the topics of air quality, the urban heat island effect, Earth’s energy budget, phytoplankton distribution, hurricane formation, solar eclipses, ocean circulation patterns, sea ice extent, and volcanic eruptions. Nine of them are extended story maps and written in the 5E format, which is internationally recognized as best practice based on how children learn science. Each story map resource is developed by the MND team featuring a GIS programming specialist, a lead scientist, and educational specialist/s to ensure the context, content, and methods are scientifically and educationally sound. The MND story maps are written for middle and high school science teachers and students as they connect with the Earth Systems Science phenomena featured in the Next Generation Science Standards. Each story map includes supporting resources for smooth integration in the classroom. During Fiscal Year 2023, The My NASA Data website received over 100,000 story map engagements during. These metrics highlight the interest in story maps as an Earth Science educational resource.

Desiray Wilson↗
NASA NTRS2017

Data Production on Past and Future NASA Missions

Data return is a metric that is commonly publicized for all space science missions. In the early days of the Space Program, this figure was small, and could be described in bits or maybe even megabits. But now, missions are capable of returning data volumes two or three orders of magnitude larger. For example, Voyager 1 and 2 combined produced a little over 5 Terabits of data in 39 years of operation. In contrast, the Cassini mission, launched two decades after Voyager, produced about one and a half times those data volumes in half the time. NISAR, an Earth Science Mission currently in implementation, plans to produce over 28 Petabits of raw data in just 3 years. This means that NISAR will produce about as many data in 30 days as the combined data production of nearly all planetary missions to date. These increases in capability are a result of technology enhancements in two main areas: telecommunications architecture (both space and ground segments) and data storage technology. This paper describes the progression of these two technologies over the course of more than three decades of space missions and provides additional insight into the design of the end-to-end NISAR Data System Architecture. Trends in the data are briefly explored and compared to Moore’s Law which provides only a qualitative model for memory growth but not for data production. In summary, early missions are found to be driven by unrefined processes while later missions, having utilized earlier lessons learned, focus more on improvements to flight and ground capabilities. Data return seems to fall into three categories. First, deep space missions are driven by the large distances that limit data return to the Earth. Next, the orbiter infrastructure around Mars helps these missions generate more data than other deep space spacecraft. Finally, near-Earth missions have the greatest capabilities for the studied metrics due to their close proximity to Earth and the ground network availability.

Xaypraseuth, Peter↗
NASA NTRS1994

The Evolution of Technology in the Deep Space Network: A History of the Advanced Systems Program

The Deep Space Network (DSN) of 1995 might be described as the evolutionary result of 45 years of deep space communication and navigation, together with the synergistic activities of radio science and radar and radio astronomy. But the evolution of the DSN did not just happen - it was carefully planned and created. The evolution of the DSN has been an ongoing engineering activity, and engineering is a process of problem solving under constraints, one of which is technology. In turn, technology is the knowledge base providing the capability and experience for practical application of various areas of science, when needed. The best engineering solutions result from optimization under the fewest constraints, and if technology needs are well anticipated (ready when needed), then the most effective engineering solution is possible. Throughout the history of the DSN it has been the goal and function of DSN advanced technology development (designated the DSN Advanced Systems Program from 1963 through 1994) to supply the technology needs of the DSN when needed, and thus to minimize this constraint on DSN engineering. Technology often takes considerable time to develop, and when that happens, it is important to have anticipated engineering needs; at times, this anticipation has been by as much as 15 years. Also, on a number of occasions, mission malfunctions or emergencies have resulted in unplanned needs for technology that has, in fact, been available from the reservoir of advanced technology provided by the DSN Advanced Systems Program. Sometimes, even DSN engineering personnel fail to realize that the organization of JPL permits an overlap of DSN advanced technology activities with subsequent engineering activities. This can result in the flow of advanced technology into DSN engineering in a natural and sometimes almost unnoticed way. In the following pages, we will explore some of the many contributions of the DSN Advanced Systems Program that were provided to DSN Engineering and Implementation. These contributions are, for the most part, unique capabilities that have met the requirements of flight projects for 45 years. These unique capabilities include not only the world's best deep-space communications system, but also outstanding competency in the fields of radio metric measurement, radar and radio astronomy, and radio science.

Layland, J. W.↗
NASA NTRS2019

Integrated Analysis of Multiple User Metrics - A “Sequel”; and Introducing the Google Analytic

For decades, the Goddard Earth Sciences Data and Information Services Center (GES DISC) has archived and distributed enormous volumes of NASA Earth science data (accompanied with many developed tools and services) to various research/applications communities and the general public. Being “immersed” in the Big Data era, we have inevitably faced the challenges of our continually increasing archived data in both volume and variety, as well as enhanced user needs and demands. In recent years, we have actively analyzed different types of user metrics, such as operational distribution metrics (recording numbers of distinct users and downloaded data files, size of distributed data volume): user publication metrics (mining info from our Giovanni users’ publications): and Bugzilla metrics (collecting info from user questions or feedback from user assistance tickets). Such metrics have helped us achieve a better understanding of user needs, demands, characteristics, and behaviors, which has then helped us improve our user services. Now we will present a “Sequel” of integrated analysis of multiple metrics at the GES DISC by introducing and adding one new kind of metrics acquired via utilizing our recently implemented Google Analytic 360 suite. Several “newer” reports, e.g., “What web site features and links are the most popular (and least)?” and “What are the top 25 dataset Keyword searches?” retrieved from this new metrics set will be presented, along with the aforementioned “traditional” metrics results.

Shie, Chung-Lin↗
NASA NTRS2011

Combined EDL-Mobility Planning for Planetary Missions

This paper presents an analysis framework for planetary missions that have coupled mobility and EDL (Entry-Descent-Landing) systems. Traditional systems engineering approaches to mobility missions such as MERs (Mars Exploration Rovers) and MSL (Mars Science Laboratory) independently study the EDL system and the mobility system, and does not perform explicit trade-off between them or risk minimization of the overall system. A major challenge is that EDL operation is inherently uncertain and its analysis results such as landing footprint are described using PDF (Probability Density Function). The proposed approach first builds a mobility cost-to-go map that encodes the driving cost of any point on the map to a science target location. The cost could include variety of metrics such as traverse distance, time, wheel rotation on soft soil, and closeness to hazards. It then convolves the mobility cost-to-go map with the landing PDF given by the EDL system, which provides a histogram of driving cost, which can be used to evaluate the overall risk of the mission. By capturing the coupling between EDL and mobility explicitly, this analysis framework enables quantitative tradeoff between EDL and mobility system performance, as well as the characterization of risks in a statistical way. The simulation results are presented with a realistic Mars terrain data

mission analysis↗
NASA NTRS1993

The first lunar outpost: The design reference mission and a new era in lunar science

The content of the First Lunar Outpost (FLO) Design Reference Mission has been formulated and a 'strawman' science program has been established. The mission consists of two independent launches using heavy lift vehicles that land directly on the lunar surface. A habitat module and support systems are flown to the Moon first. After confirmation of a successful deployment of the habitat systems, the crewed lunar lander is launched and piloted to within easy walking distance (2 km) of the habitat. By eliminating the Apollo style lunar orbit rendezvous, landing sites at very high latitudes can be considered. A surface rover and the science experiments will accompany the crew. The planned stay time is 45 days, two lunar days and one night. A payload of 3.3 metric tons will support a series of geophysics, geology, astronomy, space physics, resource utilization, and life science experiments. Sample return is 150 to 200 kg. The rover is unpressurized and can carry four astronauts or two astronauts and 500 kg of payload. The rover can also operate in robotic mode with the addition of a robotics package. The science and engineering experiment strategy is built around a representative set of place holder experiments.

Lofgren, Gary E.↗
NASA NTRSDate not supplied

CLDP/ISS Glovebox Technical Interchange Meeting

This presentation is designed to provide a high-level overview of the Microgravity Science Glovebox (MSG) and the Life Sciences Glovebox (LSG) facilities onboard the International Space Station. In addition, it provides metrics and lessons-learned information intended for the Commercial Low-Earth Orbit Development Program (CLDP) Partners.

glovebox↗
NASA NTRS2018

NASA's Earth Observing Data and Information System - Near-Term Challenges

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, digital elevation, atmosphere dynamics and composition, and inter-disciplinary research, and many others. One of the key components of EOSDIS is a set of twelve discipline-based Distributed Active Archive Centers (DAACs) distributed across the United States. Managed by NASA's Earth Science Data and Information System (ESDIS) Project at Goddard Space Flight Center, these DAACs serve over 3 million users globally. The ESDIS Project provides the infrastructure support for EOSDIS, which includes other components such as the Science Investigator-led Processing systems (SIPS), common metadata and metrics management systems, specialized network systems, standards management, and centralized support for use of commercial cloud capabilities. Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, EOSDIS has evolved continually over the past three decades. However, many challenges remain. Challenges addressed in this paper include: growing volume and variety, achieving consistency across a diverse set of data producers, managing information about a large number of datasets, migration to a cloud computing environment, optimizing data discovery and access, incorporating user feedback from a diverse community, keeping metadata updated as data collections grow and age, and ensuring that all the content needed for understanding datasets by future users is identified and preserved.

Remote Sensing↗
NASA NTRS2015

A Framework for Orbital Performance Evaluation in Distributed Space Missions for Earth Observation

Distributed Space Missions (DSMs) are gaining momentum in their application to earth science missions owing to their unique ability to increase observation sampling in spatial, spectral and temporal dimensions simultaneously. DSM architectures have a large number of design variables and since they are expected to increase mission flexibility, scalability, evolvability and robustness, their design is a complex problem with many variables and objectives affecting performance. There are very few open-access tools available to explore the tradespace of variables which allow performance assessment and are easy to plug into science goals, and therefore select the most optimal design. This paper presents a software tool developed on the MATLAB engine interfacing with STK, for DSM orbit design and selection. It is capable of generating thousands of homogeneous constellation or formation flight architectures based on pre-defined design variable ranges and sizing those architectures in terms of predefined performance metrics. The metrics can be input into observing system simulation experiments, as available from the science teams, allowing dynamic coupling of science and engineering designs. Design variables include but are not restricted to constellation type, formation flight type, FOV of instrument, altitude and inclination of chief orbits, differential orbital elements, leader satellites, latitudes or regions of interest, planes and satellite numbers. Intermediate performance metrics include angular coverage, number of accesses, revisit coverage, access deterioration over time at every point of the Earth's grid. The orbit design process can be streamlined and variables more bounded along the way, owing to the availability of low fidelity and low complexity models such as corrected HCW equations up to high precision STK models with J2 and drag. The tool can thus help any scientist or program manager select pre-Phase A, Pareto optimal DSM designs for a variety of science goals without having to delve into the details of the engineering design process.

Earth Science↗
NASA NTRS1996

Breakthrough Propulsion Physics Research Program

In 1996, a team of government, university and industry researchers proposed a program to seek the ultimate breakthroughs in space transportation: propulsion that requires no propellant mass, propulsion that can approach and, if possible, circumvent light speed, and breakthrough methods of energy production to power such devices. This Breakthrough Propulsion Physics program, managed by Lewis Research Center, is one part of a comprehensive, long range Advanced Space Transportation Plan managed by Marshall Space Flight Center. Because the breakthrough goals are beyond existing science, a main emphasis of this program is to establish metrics and ground rules to produce near-term credible progress toward these incredible possibilities. An introduction to the emerging scientific possibilities from which such solutions can be sought is also presented.

Millis, Marc G.↗
NASA NTRS2020

Barefoot Rover: a Sensor-Infused Rover Wheel Demonstrating In-Situ Engineering and Science Extractions using Machine Learning

In this work, we demonstrate an instrumented wheel concept which utilizes a 2D pressure grid, an electrochemical impedance spectroscopy (EIS) sensor and machine learning (ML) to extract meaningful metrics from the interaction between the wheel and surface terrain. These include continuous slip/skid estimation, balance, and sharpness for engineering applications. Estimates of surface hydration, texture, terrain patterns, and regolith physical properties such as cohesion and angle of internal friction are additionally calculated for science applications. Traditional systems rely on post-processing of visual images and vehicle telemetry to estimate these metrics. Through in-situ sensing, these metrics can be calculated in near real time and made available to onboard science and engineering autonomy applications. This work aims to provide a deployable system for future planetary exploration missions to increase science and engineering capabilities through increased knowledge of the terrain.

Marchetti, Yuliya↗
NASA NTRS1992

Interferometer testbed: Overview and hardware summary

Viewgraphs on an overview and hardware summary for an interferometer testbed are presented. Topics covered include: reference mission and science requirements; science requirements derivation; reference spacecraft design summary; testbed requirements and performance metric; disturbance source and signal; and testbed architecture.

Nisbet, Andrew↗
NASA NTRS2002

Remote Sensing Information Science Research

This document is the final report summarizing research conducted by the Remote Sensing Research Unit, Department of Geography, University of California, Santa Barbara under National Aeronautics and Space Administration Research Grant NAG5-10457. This document describes work performed during the period of 1 March 2001 thorough 30 September 2002. This report includes a survey of research proposed and performed within RSRU and the UCSB Geography Department during the past 25 years. A broad suite of RSRU research conducted under NAG5-10457 is also described under themes of Applied Research Activities and Information Science Research. This research includes: 1. NASA ESA Research Grant Performance Metrics Reporting. 2. Global Data Set Thematic Accuracy Analysis. 3. ISCGM/Global Map Project Support. 4. Cooperative International Activities. 5. User Model Study of Global Environmental Data Sets. 6. Global Spatial Data Infrastructure. 7. CIESIN Collaboration. 8. On the Value of Coordinating Landsat Operations. 10. The California Marine Protected Areas Database: Compilation and Accuracy Issues. 11. Assessing Landslide Hazard Over a 130-Year Period for La Conchita, California Remote Sensing and Spatial Metrics for Applied Urban Area Analysis, including: (1) IKONOS Data Processing for Urban Analysis. (2) Image Segmentation and Object Oriented Classification. (3) Spectral Properties of Urban Materials. (4) Spatial Scale in Urban Mapping. (5) Variable Scale Spatial and Temporal Urban Growth Signatures. (6) Interpretation and Verification of SLEUTH Modeling Results. (7) Spatial Land Cover Pattern Analysis for Representing Urban Land Use and Socioeconomic Structures. 12. Colorado River Flood Plain Remote Sensing Study Support. 13. African Rainfall Modeling and Assessment. 14. Remote Sensing and GIS Integration.

Clarke, Keith C.↗
NASA NTRS2003

Formation Tetrahedron Design for Phase I of the Magnetospheric Multiscale Mission

The Magnetospheric Multiscale Mission (MMS) is a NASA mission intended to make fundamental advancements in our understanding of the Earth's Magnetosphere. There are three processes that MMS will study including magnetic reconnection, charged particle acceleration, and turbulence. There are four phases in the nominal mission and this work addresses some of the outstanding issues in phase I. The nominal phase I orbit is 1.2 x 12 R(sub e) highly elliptic orbit with four spacecraft nominally forming a regular tetrahedron. In this paper we investigate the relative dynamics of the four MMS spacecraft about an assumed reference orbit. There are several tetrahedron dimensions required in Phase I of the mission and in this work we design optimal tetrahedrons for the 10 km baseline. The performance metric used in the optimization process is directly related to the science return, and is based on an extension of previous work performed by Glassmeier. The optimizer we use is a commercially available Sequential Quadratic Programming (SQP) routine. Multiple optimal solutions are found, and we characterize how the performance of the formation varies between different regions of the reference orbit.

Hughes, Steven P.↗
NASA NTRSDate not supplied

Exploring Blockchain to Support Open Science Practices

Open science aims to foster transparent sharing of scientific processes including open access, incentivization, provenance, open source code and tools, metrics, and resource sharing. However, effective management of these processes remains a challenge. This paper explores the application of blockchain technology to address these key aspects of open science. Blockchain offers a decentralized and secureplatform for information exchange and verification. By leveraging blockchain, open science can enhance transparency and reproducibility. In this paper, we present an implementation of blockchain for Earth science data synchronization across organizations, enabling tracking of data copying, citation, anddownload. The findings highlight the potential of blockchain in supporting open science objectives.

Iksha Gurung↗
NASA NTRS2017

Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.

Gentry, Diana↗
NASA NTRS2020

NASA’s Space Launch System: Launch Capability for Lunar Exploration and Transformative Science

Excitement is building for the first launch of NASA’s Space Launch System (SLS), a unique exploration asset for the agency’s Artemis lunar program as well as for a new generation of science missions. SLS is designed for an array of missions beyond Earth’s orbit. The flexible system, which can be configured for Orion, cargo or Orion with co-manifested payload missions, offers high escape velocities to send more mass to deep space destinations. When configured with an 8.4 m-diameter fairing, SLS offers unmatched payload volume for human exploration and science missions. The initial Block 1 variant will insert at least 26 metric tons (t) to trans-lunar injection (TLI) and the more powerful Block 1B vehicle will launch 34-37 t to TLI using a new-development upper stage. Much of the initial SLS Block 1 vehicle is complete, including the upper stage and payload section, the core stage, engines and the solid rocket boosters. The first mission, Artemis I, launching from modernized and upgraded facilities at Kennedy Space Center (KSC), will be an uncrewed test flight of SLS, Orion and ground processing, with a primary objective of testing Orion’s heat shield at lunar re-entry velocity. Artemis I will have accommodations for 13 6U CubeSat payloads. These CubeSat missions will be deployed along the upper stage disposal trajectory after Orion separates from the vehicle. A rare opportunity for CubeSats to be deployed beyond low Earth orbit (LEO), Artemis I CubeSat missions range from searching for hydrogen and other volatiles on the lunar South Pole to studying the acceleration mechanisms of solar and interplanetary particles from a heliocentric trajectory. With manufacturing of the initial vehicle complete, fabrication and procurement is progressing for the second flight of SLS and Orion, Artemis II. Also an SLS Block 1 and Orion flight launching from KSC, Artemis II will mark the return of American astronauts to deep space with a lunar flyby-free return trajectory mission. With the Artemis III flight, NASA has the goal to land the first woman and the next man on the Moon. Infrastructure beyond SLS will be required for this effort, including elements of the lunar Gateway as well as lunar rovers, landers and additional commercially supplied launch services. SLS, as the only vehicle with the capability to lift 26 t of mass to TLI in its initial Block 1 variant, will remain a key component of this new-era exploration program. Future variants – Block 1B and Block 2 –will lift 34-45 t to TLI. This paper will discuss the status of testing and integration for the Artemis I vehicle, manufacturing progress for the second vehicle and the manifest outlook for primary, co-manifested and secondary payloads in the current deep space exploration environment.

Creech, Stephen D.↗