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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 NTRS2002

A Data-Driven Solution for Performance Improvement

Marketed as the "Software of the Future," Optimal Engineering Systems P.I. EXPERT(TM) technology offers statistical process control and optimization techniques that are critical to businesses looking to restructure or accelerate operations in order to gain a competitive edge. Kennedy Space Center granted Optimal Engineering Systems the funding and aid necessary to develop a prototype of the process monitoring and improvement software. Completion of this prototype demonstrated that it was possible to integrate traditional statistical quality assurance tools with robust optimization techniques in a user- friendly format that is visually compelling. Using an expert system knowledge base, the software allows the user to determine objectives, capture constraints and out-of-control processes, predict results, and compute optimal process settings.

Source record↗
NASA NTRS2016

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO (Tropospheric Emissions: Monitoring of Pollution) was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (approximately 2.1 kilometers N/S by 4.4 kilometers E/W at 36.5 degrees N, 100 degrees W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

tropospheric measurement suite↗
NASA NTRS2005

Program Plan for 2005: NASA Scientific and Technical Information Program

Throughout 2005 and beyond, NASA will be faced with great challenges and even greater opportunities. Following a period of reevaluation, reinvention, and transformation, we will move rapidly forward to leverage new partnerships, approaches, and technologies that will enhance the way we do business. NASA's Scientific and Technical Information (STI) Program, which functions under the auspices of the Agency's Chief Information Officer (CIO), is an integral part of NASA's future. The program supports the Agency's missions to communicate scientific knowledge and understanding and to help transfer NASA's research and development (R&D) information to the aerospace and academic communities and to the public. The STI Program helps ensure that the Agency will remain at the leading edge of R&D by quickly and efficiently capturing and sharing NASA and worldwide STI to use for problem solving, awareness, and knowledge management and transfer.

NASA programs↗
NASA NTRS2008

An Ontology-Based Archive Information Model for the Planetary Science Community

The Planetary Data System (PDS) information model is a mature but complex model that has been used to capture over 30 years of planetary science data for the PDS archive. As the de-facto information model for the planetary science data archive, it is being adopted by the International Planetary Data Alliance (IPDA) as their archive data standard. However, after seventeen years of evolutionary change the model needs refinement. First a formal specification is needed to explicitly capture the model in a commonly accepted data engineering notation. Second, the core and essential elements of the model need to be identified to help simplify the overall archive process. A team of PDS technical staff members have captured the PDS information model in an ontology modeling tool. Using the resulting knowledge-base, work continues to identify the core elements, identify problems and issues, and then test proposed modifications to the model. The final deliverables of this work will include specifications for the next generation PDS information model and the initial set of IPDA archive data standards. Having the information model captured in an ontology modeling tool also makes the model suitable for use by Semantic Web applications.

Information Model↗
NASA NTRS2020

Modeling Whitecaps on Global Scale

Whitecaps play an important role in the surface-atmosphere interactions across the ocean. They are directly linked to the energy dissipation rate during wave breaking and transfer of heat, momentum, and gas/aerosol exchange from the wind to the sea surface. Although the first models of W were dependent only on wind speeds, a large number of diverse models based on wind and sea state which include wave height, wave age, friction velocity, and stability effects have been proposed since then. However, it is recognized that most of the proposed W models have strong systematic (e.g., zonal bias) and random errors when compared against observations. This is partly due to the differences in environmental conditions, measurement techniques, and geographical locations among these studies. But, some of these biases are linked to the inability of the proposed models to capture the variability in W in certain wind/wave regimes. Despite the knowledge of existing biases, W residual relationships from the models with wind and wave fields remain highly uncertain, with residual trends varying between the published studies. Here, we take advantage of the availability of relatively dense observations of W from WindSat microwave satellite retrievals in combination with the University of Miami wave model which was recently incorporated within the NASA GMAO/GEOS system (GEOS-UMWM). We use Windsat W retrievals to assess and constrain the previously published W models and understand the relationships of residuals from models in different wind/wave regimes. We link these unexplained residual variations to additional factors such as swell index, drag coefficient etc and add information to the existing whitecap models. Since Windsat retrievals cover wide range of environmental conditions, it helps to reduce the uncertainties associated with differences in measurement techniques. Regression of wind-wave fields against all Windsat data points (CTL) results in larger residuals for lower wave age and W is overestimated upto ~4% for wave age < 10 and underestimated by upto ~2% as wave age increases. We attest to this bias by considering two approaches. One is to perform regression separately for different stages of wave development such as developing sea, fully developed, and wind sea regimes thereby understanding the sensitivity of regression coefficients to sea state (EXP1). Another is to derive coefficients of W models in EXP1 as a function of additional wind/wave factors such as swell index, drag coefficient, and mean squared slope, deriving more nonlinear W models (EXP2). EXP2 provides reduction in Root Mean Squared Error (RMSE) by 0.1-0.3%. Sea surface drag has a stronger relationship with regression coefficients compared to swell index.These additional factors provide improved parameterizations in different wind and wave age regimes, with smaller unexplained/residual variations in W that has been a major concern in the W community.

Raman, Aishwarya↗
NASA NTRS2021

Collaborative Pose Estimation of An Unknown Target Using Multiple Spacecraft

A reliable method for pose estimation of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination can be challenging in case of unfavorable illumination, time-varying conditions due to rotational motion and relative orbit, and scale ambiguity resolution. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi- Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS.Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its own images and observes particular target landmarks in parallel and in a distributed fashion. Then, the local network is exploited by the spacecraft to share their consensus proposals and aggregate them to achieve the joint estimate. We validate our algorithm using simulations of relative orbits and observations, captured by each chaser spacecraft. To the best of the authors’ knowledge, this is the first cooperative, vision-based algorithm for estimating the pose and shape of a space object for an arbitrary number of spacecraft.

Chung, Soon-Jo↗
NASA NTRS1993

Agent oriented programming: An overview of the framework and summary of recent research

This is a short overview of the agent-oriented programming (AOP) framework. AOP can be viewed as an specialization of object-oriented programming. The state of an agent consists of components called beliefs, choices, capabilities, commitments, and possibly others; for this reason the state of an agent is called its mental state. The mental state of agents is captured formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for commitment, choice and capability. Agents are controlled by agent programs, which include primitives for communicating with other agents. In the spirit of speech-act theory, each communication primitive is of a certain type: informing, requesting, offering, etc. This document describes these features in more detail and summarizes recent results and ongoing AOP-related work.

Shoham, Yoav↗
NASA NTRS2004

Planning Mars Memory: Learning from the Mer Mission

Knowledge management for space exploration is part of a multi-generational effort at recognizing, preserving and transmitting learning. Each mission should be built on the learning, of both successes and failures, derived from previous missions. Knowledge management begins with learning, and the recognition that this learning has produced knowledge. The Mars Exploration Rover mission provides us with an opportunity to track how learning occurs, how it is recorded, and whether the representations of this learning will be optimally useful for subsequent missions. This paper focuses on the MER science and engineering teams during Rover operations. A NASA team conducted an observational study of the ongoing work and learning of the these teams. Learning occurred in a wide variety of areas: how to run two teams on Mars time for three months; how to use the instruments within the constraints of the martian environment, the deep space network and the mission requirements; how to plan science strategy; how best to use the available software tools. This learning is preserved in many ways. Primarily it resides in peoples memories, to be carried on to the next mission. It is also encoded in stones, in programming sequences, in published reports, and in lessons learned activities, Studying learning and knowledge development as it happens allows us to suggest proactive ways of capturing and using it across multiple missions and generations.

Linde, Charlotte↗
NASA NTRS2008

The Advanced Video Guidance Sensor: Orbital Express and the Next Generation

The Orbital Express (OE) mission performed the first autonomous rendezvous and docking in the history of the United States on May 5-6, 2007 with the Advanced Video Guidance Sensor (AVGS) acting as one of the primary docking sensors. Since that event, the OE spacecraft performed four more rendezvous and docking maneuvers, each time using the AVGS as one of the docking sensors. The Marshall Space Flight Center's (MSFC's) AVGS is a nearfield proximity operations sensor that was integrated into the Autonomous Rendezvous and Capture Sensor System (ARCSS) on OE. The ARCSS provided the relative state knowledge to allow the OE spacecraft to rendezvous and dock. The AVGS is a mature sensor technology designed to support Automated Rendezvous and Docking (AR&D) operations. It is a video-based laser-illuminated sensor that can determine the relative position and attitude between itself and its target. Due to parts obsolescence, the AVGS that was flown on OE can no longer be manufactured. MSFC has been working on the next generation of AVGS for application to future Constellation missions. This paper provides an overview of the performance of the AVGS on Orbital Express and discusses the work on the Next Generation AVGS (NGAVGS).

Howard, Richard T.↗
NASA NTRS2001

GEOSAT Follow-on (GFO) Altimeter Document Series: GFO Altimeter Engineering Assessment Report: From Launch to Acceptance, 10 February 1998 to 29 November 2000 - Volume 1

The US Navy's Geosat Follow-On (GFO) Mission, launched on February 10, 1998, is the latest in a series of altimetric satellites which include Seasat, Geosat, ERS-1, and TOPEX/POSEIDON (T/P). The purpose of this report is to document the GFO altimeter performance determined from the analyses and results performed by the NASA/GSFC/Wallops altimeter calibration team. It is the first of an anticipated series of NASA/GSFC/Wallops' GFO performance documents, each of which will update assessment results. This report covers the performance from launch to instrument acceptance by the Navy on November 29, 2000. Data derived from GFO will lead to improvements in the knowledge of ocean circulation, ice sheet topography, and climate change. In order to capture the maximum amount of information from the GFO data, accurate altimeter calibrations are required for the civilian data set which NOAA will produce. Wallops Flight Facility has provided similar products for the Geosat and T/P missions and is doing the same for GFO.

Hancock, David W., III↗
NASA NTRS2002

GFO Altimeter Engineering Assessment Report

The U.S. Navy's Geosat Follow-On (GFO) Mission, launched on February 20, 1998, is one of a series of altimetric satellites which include Seasat, Geosat, ERS-1, and TOPEX/POSEIDON (T/P). The purpose of this report is to document the GFO altimeter performance determined from the analyses and results performed by NASA's GSFC and Wallops altimeter, calibration team. It is the second of an anticipated series of NASA's GSFC and Wallops GFO performance documents, each of which will update assessment results. This report covers the performance from instrument acceptance by the Navy on November 29, 2000, to the end of Cycle 20 on November 21, 2001. Data derived from GFO will lead to improvements in the knowledge of ocean circulation, ice sheet topography, and climate change. In order to capture the maximum amount of information from the GFO data, accurate altimeter calibrations are required for the civilian data set which NOAA will produce. Wallops Flight Facility has provided similar products for the Geosat and T/P missions and is doing the same for GFO.

Lockwood, Dennis W.↗
NASA NTRS2002

GEOSAT Follow-on (GFO) Altimeter Document Series: GFO Altimeter Engineering Assessment Report, Version 1 - Volume 3

The U.S. Navy's Geosat Follow-On (GFO) Mission, launched on February 20, 1998, is one of a series of altimetric satellites which include Seasat, Geosat, ERS-1, and TOPEX/POSEIDON (T/P). The purpose of this report is to document the GFO altimeter performance determined from the analyses and results performed by NASA's GSFC and Wallops altimeter, calibration team. It is the second of an anticipated series of NASA's GSFC and Wallops GFO performance documents, each of which will update assessment results. This report covers the performance from instrument acceptance by the Navy on November 29, 2000, to the end of Cycle 20 on November 21, 2001. Data derived from GFO will lead to improvements in the knowledge of ocean circulation, ice sheet topography, and climate change. In order to capture the maximum amount of information from the GFO data, accurate altimeter calibrations are required for the civilian data set which NOAA will produce. Wallops Flight Facility has provided similar products for the Geosat and T/P missions and is doing the same for GFO.

Hancock, David W., III↗
NASA NTRS2003

GFO Altimeter Engineering Assessment Report. Update: The First 43 Cycles Since Acceptance, November 29, 2000 to November 30, 2002, Version 1

The U.S. Navy's Geosat Follow-On (GFO) Mission, launched on February 20, 1998, is one of a series of altimetric satellites which include Seasat, Geosat, ERS-1, and TOPEX/POSEIDON (T/P). The purpose of this report is to document the GFO altimeter performance determined from the analyses and results performed by NASA's GSFC and Wallops altimeter, calibration team. It is the third of an anticipated series of NASA's GSFC and Wallops GFO performance documents, each of which will update assessment results. This report covers the performance from instrument acceptance by the Navy on November 29, 2000, to the end of Cycle 42 on November 30, 2002. Data derived from GFO will lead to improvements in the knowledge of ocean circulation, ice sheet topography, and climate change. In order to capture the maximum amount of information from the GFO data, accurate altimeter calibrations are required for the civilian data set which NOAA will produce. Wallops Flight Facility has provided similar products for the Geosat and T/P missions and is doing the same for GFO.

Hancock, D. W., III↗
NASA NTRS2004

GFO Altimeter Engineering Assessment Report. Update: The First 65 Cycles Since Acceptance, November 29, 2000 to December 9, 2003, Version 1

The U.S. Navy's Geosat Follow-On (GFO) Mission, launched February 10, 1998, is one of a series of altimetric satellites which include Seasat, Geosat, ERS-1, and TOPEX/POSEIDON (T/P). The purpose of this report is to document the GFO altimeter performance determined from the analyses and results performed by NASA's GSFC and Wallops altimeter calibration team. It is the fourth of an anticipated series of NASA's GSFC and Wallops GFO performance documents, each of which will update assessment results. This report covers the performance from instrument acceptance by the Navy on November 29, 2000, to the end of Cycle 65 on December 9, 2003. Data derived from GFO will lead to improvements in the knowledge of ocean circulation, ice sheet topography, and climate change. In order to capture the maximum amount of information from the GFO data, accurate altimeter calibrations are required for the civilian data set which NOAA will produce. Wallops Flight Facility has provided similar products for the Geosat and T/P missions and is doing the same for GFO.

Hancock, D. W., III↗
NASA NTRS2010

How NASA Expanded its Innovation Framework to Find New Solutions to Old Problems

A radio frequency engineer from rural New Hampshire contributed the best solution to a public challenge issued by NASA's Space Life Sciences Directorate. This is a clear example of what Aneesh Chopra, the US Federal Chief Technology Officer, describes as the notion that in our society, knowledge is widely dispersed. And if it s widely dispersed, how do we capture the insights from the American people?" Chopra later said, to a live audience at the 2010 Rethinking Government event: "A semi-retired radio frequency engineer was able to share his idea about how to solve this problem, and it so blew away other ideas that NASA said it exceeded their requirements! No complicated RFP, the need for lobbyists, some convoluted processes, etc. Just a smart person who was paid a modest fee for his insight."

Davis, Jeffrey R.↗
NASA NTRS2017

The Use of Sun Elevation Angle for Stereogrammetric Boreal Forest Height in Open Canopies

Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; less than 5 m spatial resolution) yields fine-scaled digital surface models (DSMs) of elevation. These DSMs may represent elevations that range from the ground to the vegetation canopy surface, are produced from stereoscopic image pairs (stereo pairs) that have a variety of acquisition characteristics, and have been coupled with lidar data of forest structure and ground surface elevation to examine forest height. This work explores surface elevations from HRSI DSMs derived from two types of acquisitions in open canopy forests. We (1) apply an automated mass-production stereogrammetry workflow to along-track HRSI stereo pairs, (2) identify multiple spatially coincident DSMs whose stereo pairs were acquired under different solar geometry, (3) vertically co-register these DSMs using coincident spaceborne lidar footprints (from ICESat-GLAS) as reference, and(4) examine differences in surface elevations between the reference lidar and the co-registered HRSI DSMs associated with two general types of acquisitions (DSM types) from different sun elevation angles. We find that these DSM types, distinguished by sun elevation angle at the time of stereo pair acquisition, are associated with different surface elevations estimated from automated stereogrammetry in open canopy forests. For DSM values with corresponding reference ground surface elevation from spaceborne lidar footprints in open canopy northern Siberian Larix forests with slopes less than10, our results show that HRSI DSM acquired with sun elevation angles greater than 35deg and less than 25deg (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors less than 0.68 m relative to lidar. The latter, particularly those with angles less than 10deg, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (6.0 m, sigma = 1.4 m) was consistent with the distribution of plot-level mean tree heights (6.5m, sigma = 1.2 m). We conclude that the variation in sun elevation angle at time of stereo pair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register rand difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.

Paul M Montesano↗
NASA NTRS1991

Explanations in knowledge systems - Design for Explainable Expert Systems

The Explainable Expert Systems framework (EES), in which the focus is on capturing those design aspects that are important for producing good explanations, including justifications of the system's actions, explications of general problem-solving strategies, and descriptions of the system's terminology, is discussed. EES was developed as part of the Strategic Computing Initiative of DARPA. Both the general principles from which the system was derived and how the system was derived from those principles can be represented in EES. The Program Enhancement Advisor, which is the main prototype on which the explanation work has been developed and tested, is presented. PEA is an advice system that helps users improve their Common Lisp programs by recommending transformations that enhance the user's code. How EES produces better explanations is shown.

Swartout, William↗
NASA NTRS2006

Trade Studies of Space Launch Architectures using Modular Probabilistic Risk Analysis

A top-down risk assessment in the early phases of space exploration architecture development can provide understanding and intuition of the potential risks associated with new designs and technologies. In this approach, risk analysts draw from their past experience and the heritage of similar existing systems as a source for reliability data. This top-down approach captures the complex interactions of the risk driving parts of the integrated system without requiring detailed knowledge of the parts themselves, which is often unavailable in the early design stages. Traditional probabilistic risk analysis (PRA) technologies, however, suffer several drawbacks that limit their timely application to complex technology development programs. The most restrictive of these is a dependence on static planning scenarios, expressed through fault and event trees. Fault trees incorporating comprehensive mission scenarios are routinely constructed for complex space systems, and several commercial software products are available for evaluating fault statistics. These static representations cannot capture the dynamic behavior of system failures without substantial modification of the initial tree. Consequently, the development of dynamic models using fault tree analysis has been an active area of research in recent years. This paper discusses the implementation and demonstration of dynamic, modular scenario modeling for integration of subsystem fault evaluation modules using the Space Architecture Failure Evaluation (SAFE) tool. SAFE is a C++ code that was originally developed to support NASA s Space Launch Initiative. It provides a flexible framework for system architecture definition and trade studies. SAFE supports extensible modeling of dynamic, time-dependent risk drivers of the system and functions at the level of fidelity for which design and failure data exists. The approach is scalable, allowing inclusion of additional information as detailed data becomes available. The tool performs a Monte Carlo analysis to provide statistical estimates. Example results of an architecture system reliability study are summarized for an exploration system concept using heritage data from liquid-fueled expendable Saturn V/Apollo launch vehicles.

Mathias, Donovan L.↗