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NASA NTRS2019

Landsat 9 Micrometeoroid and Orbital Debris (MMOD) Mission Success Approach

Landsat 9* (L9) is the successor mission to Landsat 8 (L8) previously known as Landsat Data Continuity Mission (LDCM). Both missions are large unmanned remote sensing satellites operating in sunsynchronous polar orbits. As opposed to L8/LDCM, systems engineers for L9 incorporated Micrometeoroid/Orbital Debris (MMOD) protection for small object collisions as part of the L9’s mission success criteria. In other words, the NASA Process for Limiting Orbital Debris (NASA-STD-8719.14A) only calls for analyses of the protection of disposal-critical hardware, but L9 opted to also assess and provide small particle penetration protections for all observatory components including instruments that are not part of the spacecraft components needed for controlled reentry. Systems engineers at Goddard developed a design process to protect against MMOD during the life of Low Earth Orbit (LEO) observatories, and in particular the Landsat 9 Mission. Simply stated, this design process enhanced the effectiveness of existing Multi-Layer Insulation (MLI) to provide the needed protection. The end goal of the design process was to establish a necessary blanket areal density for a given electronics box or instrument wall thickness and a separation between the outer MLI blanket and the structure underneath. The trade space was presented as a set of design curves for different combinations of blanket density, box wall thickness, and separation distance between MLI and structure. An advantage of this process was that it is largely independent of MMOD flux data on a surface-by surface basis. Ultimately, cost savings should result from incorporating small object penetration protection early in the design cycle, rather than adding spot shielding blankets later as needed to meet an overall penetration risk standard (the more traditional approach). The approach and implementation to the L9 Observatory design will be addressed in this paper. *L9 is a joint mission being formulated, implemented, and operated by the National Aeronautics and Space Administration (NASA) and the Department of the Interior’s (DOI) United States Geological Survey (USGS).

Approach↗
NASA NTRS2000

The Transition from Spacecraft Development Ot Flight Operation: Human Factor Considerations

In the field of aeronautics and astronautics, a paradigm shift has been witnessed by those in academia, research and development, and private industry. Long development life cycles and the budgets to support such programs and projects has given way to aggressive task schedules and leaner resources to draw from all the while challenging assigned individuals to create and produce improved products of processes. however, this "faster, better, cheaper" concept cannot merely be applied to the design, development, and test of complex systems such as earth-orbiting of interplanetary robotic spacecraft. Full advantage is not possible without due consideration and application to mission operations planning and flight operations, Equally as important as the flight system, the mission operations system consisting of qualified personnel, ground hardware and software tools, and verified and validated operational processes, should also be regarded as a complex system requiring personnel to draw upon formal education, training, related experiences, and heuristic reasoning in engineering an effective and efficient system. Unquestionably, qualified personnel are the most important elements of a mission operations system. This paper examines the experiences of the Deep Space I Project, the first in a series of new technology in-flight validation missions sponsored by the United States National Aeronautics and Space Administration (NASA), specifically, in developing a subsystems analysis and technology validation team comprised of former spacecraft development personnel. Human factor considerations are investigated from initial concept/vision formulation; through operational process development; personnel test and training; to initial uplink product development and test support. Emphasis has been placed on challenges and applied or recommended solutions, so as to provide opportunities for future programs and projects to address and disposition potential issues and concerns as early as possible to reap the benefits associated with learning from other's past experiences.

Basilio, Ralph R.↗
NASA NTRS2019

Using Virtual Reality and Motion Capture as tools for Human Factors Engineering at NASA Marshall Space Flight Center

NASA Marshall Space Flight Center (MSFC) Human Factors Engineering (HFE) Team is implementing virtual reality (VR) and motion capture (MoCap) into HFE analyses of various projects through its Virtual Environments Lab (VEL). VR allows for multiple analyses early in the design process and more opportunities to give design feedback. This tool can be used by engineers in most disciplines to compare design alternatives and is particularly valuable to HFE to give early input during these evaluations. These techniques are being implemented for concept development of Deep Space Habitats (DSH), and work is being done to implement VR for design aspects of the Space Launch System (SLS). VR utilization in the VEL will push the design to be better formulated before mockups are constructed, saving budget and time. The MSFC VEL will continue forward leaning implementation with VR technologies in these and other projects for better models earlier in the design process.

Andrews, Tanya↗
NASA NTRS2020

Optimal Low Thrust Orbit Transfers for Space Telescope Refueling at SEL2

The James Webb Space Telescope (JWST), a ten billion-dollar infrared telescope with a 6.5m primary mirror to be launched in 2021, is designed to operate in a Halo orbit around the second Sun-Earth Lagrange point (SEL2) for five to ten years. At that point fuel for station keeping and attitude maneuvers will run out. Refueling missions to JWST, as well as to similar space telescope missions proposed for SEL2, could greatly enhance the “science-per-dollar” value and promote a more sustainable use of space assets. In this paper, we present a novel approach to designing fuel optimal trajectories that will allow the refueling spacecraft to arrive at the SEL2 Halo orbit with maximum final mass (i.e. fuel payload). The low thrust optimal control problem is formulated using an indirect optimization method, leading to a two-point boundary value problem with a bang-bang control structure. We make use of a hyperbolic tangent smoothing technique for performing continuation on the thrust magnitude to reduce the sharpness of the control switches in early iterations and, thus, promote convergence. The problem is posed and solved in the circular restricted three-body problem. In this dynamical system, invariant manifolds exist that can be utilized to reduce fuel consumption. The here presented methodology to this challenging and important problem in astrodynamics demonstrates a significant potential for low-cost refueling mission design.

Woollands, Robyn↗
NASA NTRS2013

Covariance Analysis Tool (G-CAT) for Computing Ascent, Descent, and Landing Errors

G-CAT is a covariance analysis tool that enables fast and accurate computation of error ellipses for descent, landing, ascent, and rendezvous scenarios, and quantifies knowledge error contributions needed for error budgeting purposes. Because GCAT supports hardware/system trade studies in spacecraft and mission design, it is useful in both early and late mission/ proposal phases where Monte Carlo simulation capability is not mature, Monte Carlo simulation takes too long to run, and/or there is a need to perform multiple parametric system design trades that would require an unwieldy number of Monte Carlo runs. G-CAT is formulated as a variable-order square-root linearized Kalman filter (LKF), typically using over 120 filter states. An important property of G-CAT is that it is based on a 6-DOF (degrees of freedom) formulation that completely captures the combined effects of both attitude and translation errors on the propagated trajectories. This ensures its accuracy for guidance, navigation, and control (GN&C) analysis. G-CAT provides the desired fast turnaround analysis needed for error budgeting in support of mission concept formulations, design trade studies, and proposal development efforts. The main usefulness of a covariance analysis tool such as G-CAT is its ability to calculate the performance envelope directly from a single run. This is in sharp contrast to running thousands of simulations to obtain similar information using Monte Carlo methods. It does this by propagating the "statistics" of the overall design, rather than simulating individual trajectories. G-CAT supports applications to lunar, planetary, and small body missions. It characterizes onboard knowledge propagation errors associated with inertial measurement unit (IMU) errors (gyro and accelerometer), gravity errors/dispersions (spherical harmonics, masscons), and radar errors (multiple altimeter beams, multiple Doppler velocimeter beams). G-CAT is a standalone MATLAB- based tool intended to run on any engineer's desktop computer.

Boussalis, Dhemetrios↗
NASA NTRSDate not supplied

NASA’s Earth System Observatory Formulation Progress

The 2017 Earth Science Decadal Survey by the National Academies of Science, Engineering, and Medicine identified five foundational observations, or Designated Observables (DO), to be implemented as missions by NASA. To address these five DO areas – Surface Biology and Geology; Mass Change; Aerosols; Cloud, Convection, and Precipitation; and Surface Deformation and Change – NASA is developing the Earth System Observatory (ESO) as an array of Earth-focused, interconnected satellite missions. The ESO will build on the strong history of international partnerships in NASA Earth science, with initial participation and collaboration on these missions across space agency partners including the Japanese Aerospace Exploration Agency (JAXA), Centre National D'Etudes Spatiales (CNES), Canadian Space Agency (CSA), Deutsches Zentrum für Luft- und Raumfahrt (DLR), and Agenzia Spaziale Italiana (ASI). Targeting launch dates in the late 2020s and early 2030s, the ESO will work as a single observatory, with each satellite in the ESO delivering its own valuable information, but taken together, the data and imagery will provide the global community with a multidimensional, holistic view of the Earth. In addition to furthering the world’s scientific knowledge, data gathered by ESO missions will provide actionable science to inform decisions related to climate change, disaster mitigation, and wildfires, improve real-time agricultural processes, and many other applications. This paper will provide an overview of the ESO development activities, as well as an update on the individual ESO missions currently in formulation – Atmosphere Observing System, Gravity Recovery and Climate Experiment-Continuity, and Surface Biology and Geology – including current mission architectures and concepts, international partner collaboration, science community engagement, and applications efforts, as well as how ESO data will be freely open and accessible to users all over the world.

NASA Earth Science↗
NASA NTRS2004

The Surface Density Distribution in the Solar Nebula

The commonly used minimum mass power law representation of the pre-solar nebula is reanalyzed using a new cumulative-mass-model. This model predicts a smoother surface density approximation compared with methods based on direct computation of surface density. The density is quantified using two independent analytical formulations. First, a best-fit transcendental function is applied directly to the basic planetary data. Next a solution to the time-dependent disk evolution equation is parametrically adapted to the solar nebula data. The latter model is shown to be a good approximation to the finite-size early Solar Nebula, and by extension to other extra solar protoplanetary disks.

Davis, Sanford S.↗
NASA NTRS2019

Simulaneous optimization of spacecraft and trajectory design for interplanetary missions utilizing solar electric propulsion

A major challenge in formulating interplanetary mission concepts utilizing electric propulsion is the large number of trajectory variables that must be considered (thrust profile, flyby options, launch vehicle delivery), all of which are affected by spacecraft design variables (power, mass, thruster, payload, staging). This is significantly more complex than traditional ballistic/chemical mission design and early concepts are often suboptimal as a result, potentially missing valuable options. This paper presents a novel tool (MORT) for simultaneously optimizing the spacecraft design alongside the trajectory given mission constraints and objectives, including example results relevant to the exploration of Mars.

Lock, Rob↗
NASA NTRS2014

Evaluation of Geopolymer Concrete for Rocket Test Facility Flame Deflectors

The current paper presents results from a combined research effort by Louisiana Tech University (LTU) and NASA Stennis Space Center (SSC) to develop a new alumina-silicate based cementitious binder capable of acting as a high performance refractory material with low heat ablation rate and high early mechanical strength. Such a binder would represent a significant contribution to NASA's efforts to develop a new generation of refractory 'hot face' liners for liquid or solid rocket plume environments. This project was developed as a continuation of on-going collaborations between LTU and SSC, where test sections of a formulation of high temperature geopolymer binder were cast in the floor and walls of Test Stand E-1 Cell 3, an active rocket engine test stand flame trench. Additionally, geopolymer concrete panels were tested using the NASA-SSC Diagnostic Test Facility (DTF) thruster, where supersonic plume environments were generated on a 1ft wide x 2ft long x 6 inch deep refractory panel. The DTF operates on LOX/GH2 propellants producing a nominal thrust of 1,200 lbf and the combustion chamber conditions are Pc=625psig, O/F=6.0. Data collected included high speed video of plume/panel area and surface profiles (depth) of the test panels measured on a 1-inch by 1-inch giving localized erosion rates during the test. Louisiana Tech conducted a microstructure analysis of the geopolymer binder after the testing program to identify phase changes in the material.

Allgood, Daniel C.↗
NASA NTRS1985

Questions about Mercury's role in comparative planetary geophysics

Problems which have arisen in formulating a mutually consistent picture of Mercury's evolution are outlined. It appears that one or more of the following widely adopted assumptions are wrong about Mercury: (1) its original composition at least approximately resulted from equilibrium condensation; (2) its magnetic field arises from a still-active dynamo; (3) its thermal evolution should have yielded early core formation followed by cooling and a global contraction approaching 20 km in the planet's radius; (4) Mercury's surface is basaltic and the intercrater plains are of volcanic origin. It is suggested that Mercury's role in comparative planetology be reevaluated in the context of an alternative timescale based on the possibility that Mercury was subjected to a continuing source of cratering projectiles over recent aeons, which have not impacted the other terrestrial planets. Although such vulcanoids have not yet been discovered, the evolution of Mercury's orbit due to secular perturbations could well have led to a prolonged period of sweeping out any intra-Mercurian planetesimals that were originally present. Mercury's surface could be younger than previously believed, which explains why Mercury's core is still molten.

Chapman, C. R.↗
NASA NTRS2003

Monte Carlo Methodology Serves Up a Software Success

Widely used for the modeling of gas flows through the computation of the motion and collisions of representative molecules, the Direct Simulation Monte Carlo method has become the gold standard for producing research and engineering predictions in the field of rarefied gas dynamics. Direct Simulation Monte Carlo was first introduced in the early 1960s by Dr. Graeme Bird, a professor at the University of Sydney, Australia. It has since proved to be a valuable tool to the aerospace and defense industries in providing design and operational support data, as well as flight data analysis. In 2002, NASA brought to the forefront a software product that maintains the same basic physics formulation of Dr. Bird's method, but provides effective modeling of complex, three-dimensional, real vehicle simulations and parallel processing capabilities to handle additional computational requirements, especially in areas where computational fluid dynamics (CFD) is not applicable. NASA's Direct Simulation Monte Carlo Analysis Code (DAC) software package is now considered the Agency s premier high-fidelity simulation tool for predicting vehicle aerodynamics and aerothermodynamic environments in rarified, or low-density, gas flows.

Source record↗
NASA NTRS1998

Surface-Wind Anomalies in North-Atlantic and North Pacific from SSM/I Observations: Influence on Temperature of Adjoining Land Regions

Surface winds over the oceans are derived from Special Sensor Microwave Imager (SSM/I) measurements, assigning direction by Variational Analysis Method (VAM). Validations by comparison with other measurements indicate highly-satisfactory data quality. Providing global coverage from 1988, the dataset is a convenient source for surface-wind climatology. In this study, the interannual variability of zonal winds is analyzed concentrating on the westerlies in North Atlantic and North Pacific, above 30 N. Interannual differences in the westerlies exceeding 10 m sec (exp -1) are observed over large regions, often accompanied by changes of the same magnitude in the easterlies below 30 N. We concentrate on February/March, since elevated temperatures, by advancing snow-melt, can produce early spring. The extremely strong westerlies in 1997 observed in these months over North Atlantic (and also North Pacific) apparently contributed to large surface-temperature anomalies in western Europe, on the order of +3 C above the climatic monthly average for England and France. At these latitudes strong positive anomalies extended in a ring around the globe. We formulated an Index of South westerlies for the North Atlantic, which can serve as an indicator for day-by-day advection effects into Europe. In comparing 1997 and 1998 with the previous years, we establish significant correlations with the temperature anomalies (one to five days later, depending on the region, and on the season). This variability of the ocean-surface winds and of the temperature anomalies on land may be related to the El Nino/La Nina oscillations. Such large temperature fluctuations over large areas, whatever the cause, can be regarded as noise in attempts to assess long-term trends in global temperature.

Otterman, Joseph↗
NASA NTRS2008

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models is expected to be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 ground processing algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area)

Goodman, Steven J.↗
NASA NTRS2008

Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch readiness in December 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models will be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) forecast offices in Southern and Eastern Region. This effort is designed to help improve our understanding of the application of these data in operational settings.

Goodman, Steven J.↗
NASA NTRS2008

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornado activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications.

Goodman, Steven↗
NASA NTRS2008

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational.The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) sate]lite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications. Abstract for the 3 rd Conference on Meteorological

Goodman, Steven↗
NASA NTRS1990

Computation and parallel implementation for early vision

The problem of early vision is to transform one or more retinal illuminance images-pixel arrays-to image representations built out of such primitive visual features such as edges, regions, disparities, and clusters. These transformed representations form the input to later vision stages that perform higher level vision tasks including matching and recognition. Researchers developed algorithms for: (1) edge finding in the scale space formulation; (2) correlation methods for computing matches between pairs of images; and (3) clustering of data by neural networks. These algorithms are formulated for parallel implementation of SIMD machines, such as the Massively Parallel Processor, a 128 x 128 array processor with 1024 bits of local memory per processor. For some cases, researchers can show speedups of three orders of magnitude over serial implementations.

Gualtieri, J. Anthony↗
NASA NTRS2002

Optimization of Systems with Uncertainty: Initial Developments for Performance, Robustness and Reliability Based Designs

This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.

Crespo, Luis G.↗