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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 NTRS2012

Fast, Affordable, Science and Technology Satellite (FASTSAT) Huntsville-01 (HSV-01) Spacecraft Lessons Learned Report

The Fast Affordable Science and Technology Satellite (FASTSAT) project is a path finding effort to produce reliable satellite busses for different applications at an unprecedented speed and low cost. The project is designed to be a generational project and the first satellite produced is the Huntsville -01 (HSV-01) spacecraft. The subject of this report is the lessons learned gained during the development, testing, and up to the delivery of the FASTSAT HSV -01 spacecraft. The purpose of this report is to capture the major findings that will greatly benefit the future FASTSAT satellites and perhaps other projects interested in pushing the boundaries for cost and schedule. The FASTSAT HSV -01 primary objectives, success criteria, and team partners are summarized to give a frame of reference to the lessons learned.

Smith, Timothy A.↗
NASA NTRS2015

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

machine learning↗
NASA NTRS1999

Space 2000 Symposium

The purpose of the Space 2000 Symposium is to present the creativity and achievements of key figures of the 20th century. It offers a retrospective discussion on space exploration. It considers the future of the enterprise, and the legacy that will be left for future generations. The symposium includes panel discussions, smaller session meetings with some panelists, exhibits, and displays. The first session entitled "From Science Fiction to Science Facts" commences after a brief overview of the symposium. The panel discussions include talks on space exploration over many decades, and the missions of the millennium to search for life on Mars. The second session, "Risks and Rewards of Human Space Exploration," focuses on the training and health risks that astronauts face on their exploratory mission to space. Session three, "Messages and Messengers Informing and Inspire Space Exploration and the Public," focuses on the use of TV medium by educators and actors to inform and inspire a wide variety of audiences with adventures of space exploration. Session four, "The Legacy of Carl Sagan," discusses the influences made by Sagan to scientific research and the general public. In session five, "Space Exploration for a new Generation," two student speakers and the NASA Administrator Daniel S. Goldin address the group. Session six, "Destiny or Delusion? -- Humankind's Place in the Cosmos," ends the symposium with issues of space exploration and some thought provoking questions. Some of these issues and questions are: what will be the societal implications if we discover the origin of the universe, stars, or life; what will be the impact if scientists find clear evidence of life outside the domains of the Earth; should there be limits to what humans can or should learn; and what visionary steps should space-faring people take now for future generations.

Source record↗
NASA NTRS2002

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Rossi, F.↗
NASA NTRS2019

Machine Learning, Markov Chain Monte Carlo, and Optimal Algorithms to Characterize the AdvACT Kilopixel Transition-Edge Sensor Arrays

Next-generation focal planes comprising dozens of kilopixel transition-edge sensor (TES) arrays require new methods to rapidly screen candidate arrays, evaluate array non-idealities in the field, identify outlier devices for removal, and optimize the array performance in the field. We demonstrate robust methods to estimate TES parameters (critical temperatures and thermal conductivity parameters) and their uncertainties using a custom Markov Chain Monte Carlo (MCMC) algorithm. We also constrain systematic effects in estimating the TES parameters from non-isothermal current-voltage curves (IVs) at approximately a ~3% level. Additionally, for the first time, we have applied Machine Learning (ML) algorithms to tune detector arrays and optimize their performance.

Maria Salatino↗
NASA NTRS2004

Spacecraft Fire Suppression: Testing and Evaluation

The objective of this project is the testing and evaluation of the effectiveness of a variety of fire suppressants and fire-response techniques that will be used in the next generation of spacecraft (Crew Exploration Vehicle, CEV) and planetary habitats. From the many lessons learned in the last 40 years of space travel, there is common agreement in the spacecraft fire safety community that a new fire suppression system will be needed for the various types of fire threats anticipated in new space vehicles and habitats. To date, there is no single fire extinguishing system that can address all possible fire situations in a spacecraft in an effective, reliable, clean, and safe way. The testing conducted under this investigation will not only validate the various numerical models that are currently being developed, but it will provide new design standards on fire suppression that can then be applied to the next generation of spacecraft extinguishment systems. The test program will provide validation of scaling methods by conducting small, medium, and large scale fires. A variety of suppression methods will be tested, such as water mist, carbon dioxide, and nitrogen with single and multiple injection points and direct or distributed agent deployment. These injection methods cover the current ISS fire suppression method of a portable hand-held fire extinguisher spraying through a port in a rack and also next generation spacecraft units that may have a multi-point suppression delivery system built into the design. Consideration will be given to the need of a crew to clean-up the agent and recharge the extinguishers in flight in a long-duration mission. The fire suppression methods mentioned above will be used to extinguish several fire scenarios that have been identified as the most relevant to spaceflight, such as overheated wires, cable bundles, and circuit boards, as well as burning cloth and paper. Further testing will be conducted in which obstructions and ventilation will be added to represent actual spacecraft conditions (e.g., a series of cards in a card rack).

Abbud-Madrid, Angel↗
NASA NTRS1999

Detection in fixed and random noise in foveal and parafoveal vision explained by template learning

Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.

NASA Center ARC↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
NASA NTRS2002

Successful Development of an Automated Rendezvous and Capture System

During the 1990's, the Marshall Space Flight Center (MSFC) conducted pioneering research in the development of an automated rendezvous and capture/docking system for U.S. space vehicles. Development and demonstration of a rendezvous sensor was identified early in the AR&C Program as the critical enabling technology that allows automated proximity operations and docking. A first generation rendezvous sensor, the Video Guidance Sensor (VGS) was developed and successfully flown on STS 87 and again on STS 95, proving the concept of a video-based sensor. Advances in both video and signal processing technologies and the lessons learned from the two successful flight experiments provided a baseline for the development, by the MSFC, of a new generation of video based rendezvous sensor. The Advanced Video Guidance Sensor (AVGS) has greatly increased performance and additional capability for longer-range operation with a new Target designed as a direct replacement for existing ISS hemispherical reflectors. A ground demonstration of the entire system and software was successfully tested.

Roe, Fred D.↗
NASA NTRS1992

Cognitive competencies - Products of genes, experience, and technology

The paper examines methods used in studying cognitive competency in primates. Citing experiments on teaching language skills to chimpanzees, it is shown that some methods used for inquiry might lead to the cultivation and generation of new competencies, and specifically to the development of observational and relational learning skills. It is noted that methods can also limit the generality of conclusions; erroneous conclusions may be made based on certain generally accepted methods, whereby the research might be treatments that profoundly determine the assessment of dependent variables. Particular attention is given to the role of age in learning, showing that young primates can be taught the meaning of lexigrams and many specific tasks in much shorter time than adults; on the basis of these experiments, it was concluded that cultural gains did evolve primarily as a consequence of context within which infants were growing.

Rumbaugh, Duane M.↗
NASA NTRS2004

Short Time Scale Evolution of Microbiolites in Rapidly Receding Altiplanic Lakes: Learning How to Recognize Changing Signatures of Life

As part of the exploration of high altitude lakes as analogs to Martian paleolakes environment, we are investigating a remarkably large and diverse field of lacustrine stromatolites located at 4,365m in the Bolivian Altiplano (22 deg 47 00 min S and 67 deg 47.00 min W).The field is composed of both early Holocene fossil structures located on paleoshorelines and present-day active cyanobacterial communities on the shore and at the bottom of the current Laguna Blanca and Verde. Its physical environment, broad diversity of morphologies, and their associated spatial heterogeneity, origin, and scale offer a unique opportunity to explore microbiolites in conditions reminiscent of early Earth and Mars. At this altitude and latitude, UV radiation levels are enhanced (40% higher than sea level) and harmful to microorganisms living in shallow waters which provide only minimal protection from UV. Similar conditions prevailed on early Earth when the ozone layer had yet to be formed in the atmosphere. Compared to those studied at sea levels, these stromatolites could yield new insights about the earliest terrestrial forms of life. Moreover, the combination of physical and geological environment of this site is exceptionally analogous to conditions believed to be prevalent on Mars at the end of the Noachian (3.5 Ga ago), allowing to test the potential for forming stromatolites in martian paleolakes and learn how to identify their fossil record remotely. Our overarching goal is to generate new astrobiological information on high-altitude stromatolites as clues to early biospheres with implications for Earth and Mars. Our two central objectives are: (1) characterize the biological, geological, and mineralogical features and significance of this field, and to identify geo-signatures such as morphology, geology, chronostratigraphy, mineralogy and biosignatures, and (2) to facilitate remote-sensing and ground robotic detection capabilities for future astrobiological missions to Mars.

Cabrol, N. A.↗
NASA NTRS2006

Three Years of Hyperspectral Data from AIRS: What Have We Learned

This viewgraph presentation reviews the Atmospheric Infrared Sounder (AIRS) that is on the Aqua satellite. The expectations that were had about AIRS have been exceeded. The lessons learned from AIRS are reviewed. These lessons are used to enhance the next generation hyperspectral sounder, being planned.

accuracy↗
NASA NTRS2008

Tool Support for Parametric Analysis of Large Software Simulation Systems

The analysis of large and complex parameterized software systems, e.g., systems simulation in aerospace, is very complicated and time-consuming due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such an analysis with a tool to support envelope assessment: we utilize a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. Additional test-cases, automatically generated from models (e.g., UML, Simulink, Stateflow) improve the coverage. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. The tool has been developed around the Trick simulation environment, which is widely used within NASA. We will present this tool with a GN&C (Guidance, Navigation and Control) simulation of a small satellite system.

Schumann, Johann↗
NASA NTRS2012

Kosmo's Farewell Advice

Joe Kosmo shared some final words and advice for his teammates in the Spacesuit and Crew Survival Systems Branch (EC5) and the Crew and Thermal Systems Division (CTSD (EC)) upon his retirement. He knew nothing about spacesuits when he started working for NASA in 1961, but neither did anyone else. He summed up the best lessons learned during his 50 years of developing U.S. spacesuits and encouraged the next generation s space industry workers to challenge what they hear and decide what is right. Topics include and oral history of early NASA manned flights by Richard S. Johnston, U.S. human spaceflight chronology, a history of advanced EVA project funding, and a discussion of NASA's innovative spirit.

Kosmo, Joe↗
NASA NTRS2008

Preparing Cassini Uplink Operations for Extended Mission

The Cassini-Huygens Mission to Saturn and Titan, a joint venture between the National Aeronautics and Space Administration, the European Space Agency, and the Italian Space Agency, is conducting a four-year, prime mission exploring the Saturnian system, including its atmosphere, rings, magnetosphere, moons and icy satellites. Launched in 1997, Cassini began its prime mission in 2004. Cassini is now preparing for a new era, a two-year extended mission to revisit many of the highlights and new discoveries made during the prime mission. Because of the light time delay from Earth to Saturn, and the time needed to coordinate the complicated science and engineering activities that take place on the spacecraft, commanding on Cassini is done in approximately 40-day intervals known as sequences. The Cassini Uplink Operations team is responsible for the final development and validation of the pointing profile and instrument and spacecraft commands that are contained in a sequence. During this final analysis prior to uplink to the spacecraft, thorough and exact evaluation is necessary to ensure there are no mistakes during commanding. In order to perform this evaluation, complete and refined processes and procedures are fundamental. The Uplink Operations team is also responsible for anomaly response during sequence execution, a process in which critical decisions often are made in real-time. Recent anomalies on other spacecraft missions have highlighted two major risks in the operations process: (1) personnel turnover and the retirement of critical knowledge and (2) aging, outdated operations procedures. If other missions are a good barometer, the Cassini extended mission will be presented with a high personnel turnover of the Cassini flight team, which could lead to a loss of expertise that has been essential to the success of the prime mission. In order to prepare the Cassini Uplink Operations Team for this possibility and to continue to develop and operate safe science and engineering sequences, a review and major update of the current documentation and operations procedures was needed. This paper will address the changes made to extended mission sequence generation processes primarily due to new restrictions in spacecraft operating capability and lessons learned from prime mission. In addition, it will address the state of the prime mission operations procedures, the philosophy changes and updates that were made to those procedures in response to process improvement, and the validation of those new procedures through the training of current and new personnel. And lastly, it will address the lessons learned throughout prime mission and how the Uplink Operations team chose to incorporate those lessons into the working documentation and team knowledge. This incorporation was necessary to facilitate the success of the extended mission with potentially all new personnel at some point prior to the end of the mission.

Maxwell, Jennifer L.↗
NASA NTRS2010

Observations on the State of NASA's GN&C Engineering Discipline: Results of an Independent Non-Advocate Study

The NASA Technical Fellows periodically conduct State-of-the-Discipline assessments. The GN&C Technical Fellow contracted Harlan Brown & Company in 2007 and 2009 to conduct independent, third party studies to gain unbiased insight and understanding into the attitudes and beliefs of NASA's GN&C Community of Practice (CoP). The paper first outlines the background, objectives and methodology of the studies. The paper then summarizes key study results of the 2007 baseline study, as well as the 2009 update. The update was then used to track and monitor perceptions, identify performance trends, identify areas where further improvement needs to be made in NASA's GN&C discipline. It also generated feedback on the recently developed GN&C CoP online knowledge capture and learning site.

Pawlikowski, Gerald J.↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRSDate not supplied

Online Small-Sat Knowledge Repositories and Modeling Tools for Risk Reduction and Enhanced Mission Success

Developers of small satellites face the challenges of a short development schedule, incomplete information about parts from vendors, and limited budgets. The situation is complicated by the need to use commercial electronic parts rather than radiation hardened parts to meet performance and budget constraints. Fortunately, a number of organizations are standing up new modeling and data resources that are available for free online. Motivation for sharing models, part data, and lessons learned include shorter development times, increased mission success likelihood, and training the next generation of space professionals. The purpose of this paper is to make these resources and their complementary capabilities known to the small satellite community.

Small Satellites↗