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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 NTRS1973

Lunar composition from Apollo orbital measurements

An X-ray spectrometer carried in the Service Module of the Apollo 15 and 16 spacecraft were employed for compositional mapping of the lunar surface. The surface measurements involved observations of the intensity and characteristic energy distribution of the fluorescent X-rays produced by the interaction of the solar X-rays with the lunar surface as well as naturally occurring gamma-rays and cosmic ray produced gamma-rays. A large scale compositional map of approximately twenty percent of the lunar surface was obtained for the first time. It was possible to demonstrate differences between the highlands and the mare and to learn something about the composition of the moon's hidden side. Results obtained from the X-ray experiment and the gamma-ray experiment are consistent with those obtained from lunar sample analysis.

Adler, I.↗

NASA NTRS1993

Using solar flare track densities to determine the origin of interplanetary dust particles

Sandford theoretically explored the use of solar flare track densities in interplanetary dust particles (IDP's) to distinguish whether they were of cometary or asteroidal origin. He determined that there were differences in the characteristic distributions of track densities that would occur from these two possible sources. Flynn examined the heating of IDP's on atmospheric entry and concluded that IDP's must be predominantly from asteroidal sources because these asteroidal particles would have sufficiently low velocities to survive atmospheric heating with little or no modification whereas cometary particles would ordinarily have velocities that are much higher. This paper looks at what we can learn by combining the approaches of Sandford and Flynn.

Blanford, George E.↗

NASA NTRS2003

Neural Flight Control System

The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

Gundy-Burlet, Karen↗

NASA NTRS2008

Re-Engineering the Tropical Rainfall Measuring Mission (TRMM) Satellite Utilizing Goddard Space Flight Center (GSFC) Mission Services Center (GMSEC) Middleware Based Technology to Enable Lights Out Operations and Autonomous Re-Dump of Lost Telemetry Data

The Tropical Rainfall Measuring Mission (TRMM) spacecraft was launched in November of 1996 in order to obtain unique three dimensional radar cross sectional observations of cloud structures with particular interest in hurricanes. The TRMM mission life was recently extended with current estimates that operations will continue through the 2012-2013 timeframe. Faced with this extended mission profile, the project has embarked on a technology refresh and re-engineering effort. TRMM has recently implemented a re-engineering effort to expand a middleware based messaging architecture to enable fully redundant lights-out of flight operations activities. The middleware approach is based on the Goddard Mission Services Evolution Center (GMSEC) architecture, tools and associated open-source Applications Programming Interface (API). Middleware based messaging systems are useful in spacecraft operations and automation systems because private node based knowledge (such as that within a telemetry and command system) can be broadcast on the middleware messaging bus and hence enable collaborative decisions to be made by multiple subsystems. In this fashion, private data is made public and distributed within the local area network and multiple nodes can remain synchronized with other nodes. This concept is useful in a fully redundant architecture whereby one node is monitoring the processing of the 'prime' node so that in the event of a failure the backup node can assume operations of the prime, without loss of state knowledge. This paper will review and present the experiences, architecture, approach and lessons learned of the TRMM re-engineering effort centered on the GMSEC middleware architecture and tool suite. Relevant information will be presented that relates to the dual redundant parallel nature of the Telemetry and Command (T and C) and Front-End systems and how these systems can interact over a middleware bus to achieve autonomous operations including autonomous commanding to recover missing science data during the same spacecraft contact.

Marius, Julio L.↗

NASA NTRSDate not supplied

Real World Applications of AI/ML in Optimizing Airspace Operations

As National Airspace System (NAS) is going through the Digital Transformation journey, data science and analytics methods can significantly contribute to improving the traditional physics-based decision-making tools. The adoption of AI/ML methods will not only help accelerate Federal Aviation Administration (FAA)’s vision of Info-centric NAS but also contribute to the overall objective of sustainable aviation. AI/ML can improve the ground and airspace operations by enhancing the accuracy of current decision-making tools used by the airlines and the FAA to manage traffic on the ground and in the air. Huge amount of data that gets collected during a flight. AI/ML methods can extract information from this data and provide valuable insights to make better operational decisions. NASA has partnered with the FAA and commercial airlines such as American and Southwest Airlines on this effort and has successfully demonstrated the benefits of using ML in real world environment by reducing delays and optimizing ground operations at the US airports. In 2022 itself, NASA demonstrated real-world benefits (over 24K lbs. of fuel savings, over 76.6K lbs. CO2 emission savings, and several hours of delay savings) by deploying ML based prediction models to optimize ground operations at Dallas/Fort Worth International and Dallas Love Field Airports in Texas. These tools are being deployed on the cloud for broader deployment, adaptability, and scalability. NASA is developed a reference implementation of the cloud-based platform to significantly lower the bar to development and distribution of these digital services for aviation. In this talk, I will share information about the Digital Information Platform project, the novel AI/ML based approaches used for optimizing ground operations and the opportunities to partner with NASA on these demonstrations.

air traffic management↗

NASA NTRS2011

Lessons Learned from OMI Observations of Point Source SO2 Pollution

The Ozone Monitoring Instrument (OMI) on NASA Aura satellite makes global daily measurements of the total column of sulfur dioxide (SO2), a short-lived trace gas produced by fossil fuel combustion, smelting, and volcanoes. Although anthropogenic SO2 signals may not be detectable in a single OMI pixel, it is possible to see the source and determine its exact location by averaging a large number of individual measurements. We describe new techniques for spatial and temporal averaging that have been applied to the OMI SO2 data to determine the spatial distributions or "fingerprints" of SO2 burdens from top 100 pollution sources in North America. The technique requires averaging of several years of OMI daily measurements to observe SO2 pollution from typical anthropogenic sources. We found that the largest point sources of SO2 in the U.S. produce elevated SO2 values over a relatively small area - within 20-30 km radius. Therefore, one needs higher than OMI spatial resolution to monitor typical SO2 sources. TROPOMI instrument on the ESA Sentinel 5 precursor mission will have improved ground resolution (approximately 7 km at nadir), but is limited to once a day measurement. A pointable geostationary UVB spectrometer with variable spatial resolution and flexible sampling frequency could potentially achieve the goal of daily monitoring of SO2 point sources and resolve downwind plumes. This concept of taking the measurements at high frequency to enhance weak signals needs to be demonstrated with a GEOCAPE precursor mission before 2020, which will help formulating GEOCAPE measurement requirements.

Krotkov, N.↗

NASA NTRS2005

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes data management of NASA remote sensing data for Northern Eurasia Earth Science Partnership Initiative (NEESPI). Many types of ground and integrative (e.g., satellite, GIs) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of Northern Eurasia cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential. The proposed project targets integration of remote sensing data from AVHRR, MODIS, and other NASA instruments on board US- satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEPNCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

NASA NTRS2006

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes the recently awarded ACCESS project to provide data management of NASA remote sensing data for the Northern Eurasia Earth Science Partnership Initiative (NEESPI). The project targets integration of remote sensing data from MODIS, and other NASA instruments on board US-satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEP/NCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

NASA NTRS2013

Investigating Actuation Force Fight with Asynchronous and Synchronous Redundancy Management Techniques

Within distributed fault-tolerant systems the term force-fight is colloquially used to describe the level of command disagreement present at redundant actuation interfaces. This report details an investigation of force-fight using three distributed system case-study architectures. Each case study architecture is abstracted and formally modeled using the Symbolic Analysis Laboratory (SAL) tool chain from the Stanford Research Institute (SRI). We use the formal SAL models to produce k-induction based proofs of a bounded actuation agreement property. We also present a mathematically derived bound of redundant actuation agreement for sine-wave stimulus. The report documents our experiences and lessons learned developing the formal models and the associated proofs.

Hall, Brendan↗

NASA NTRS2019

Cognitive Communications for NASA Space Systems

The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. NASA has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of spacecraft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗

NASA NTRSDate not supplied

Eleven Countries, an Integrated Spacecraft: the Story of International Collaboration that Built the Orion Spacecraft and Powered the Success of the Artemis I Mission

The quest to return humans to the Moon in the next step towards humanity's exploration of space is more alive than ever. After a great deal of achievements, failures, and lessons learned, the Artemis I mission set o to the Moon on November 16, 2022, with the goal of testing a new rocket, the Space Launch System, and a new spacecraft, Orion: designed, assembled, and tested across two continents, and 11 countries. Behind this mission, decades of experience with the International Space Station, Autonomous Transfer Vehicle operations, and many other program collaborations built the know-how on how to succeed together in the toughest environment | deep space. The Artemis I mission proved to be an incredible success, meeting 161 total mission objectives, including 21 developed during the flight based on outperforming spacecraft. It was also a case-study in international collaboration, given that ESA, NASA, and industry partners Airbus and Lockheed Martin for the first time had to design, build, test, and fly a fully integrated human-rated spacecraft, with most critical functions dependent and interconnected across U.S. and European systems. The U.S.-built Orion Crew Module and Crew Module Adapter and European-built European Service Module (ESM) shared critical interfaces and commodities, from propulsion, avionics, active/passive thermal, electrical power generation, storage and distribution to the software that managed it all. In this paper, we will describe relevant aspects of the integrated spacecraft design, providing context for the challenges that the team faced in all phases required to get Orion ready to fly, and provide a direct account of how the joint team formed, trained, and supported the operations of the successful Artemis I mission. We will also explore the evolution of the partnerships, given that these allow a multi-national e ort to sustain the program production, share costs, leverage a broader base of engineering expertise, and build more diverse capabilities over the long haul to support the Artemis goals and objectives. Lastly, we will cover critical lessons learned and how the Orion Program has implemented these in preparation of the next Artemis missions to repeat the success of Artemis I. The purpose of this paper is to document knowledge we gained and lessons we learned through the development of an integrated Orion spacecraft, since it is imperative we build on this now, at the dawn of the Artemis Program, an international endeavor to push human space exploration.

Deep Space Exploration↗

NASA NTRS1995

Link Winds: A visual data analysis system and its application to the atmospheric ozone depletion problem

The Linked Windows Interactive Data System (LinkWinds) is a prototype visual data exploration system resulting from a NASA Jet Propulsion Laboratory (JPL) program of research into the application of graphical methods for rapidly accessing, displaying, and analyzing large multi variate multidisciplinary data sets. Running under UNIX it is an integrated multi-application executing environment using a data-linking paradigm to dynamically interconnect and control multiple windows containing a variety of displays and manipulators. This paradigm, resulting in a system similar to a graphical spreadsheet, is not only a powerful method for organizing large amounts of data for analysis, but leads to a highly intuitive, easy-to-learn user interface. It provides great flexibility in rapidly interacting with large masses of complex data to detect trends, correlations, and anomalies. The system, containing an expanding suite of non-domain-specific applications, provides for the ingestion of a variety of data base formats and hard -copy output of all displays. Remote networked workstations running LinkWinds may be interconnected, providing a multiuser science environment (MUSE) for collaborative data exploration by a distributed science team. The system is being developed in close collaboration with investigators in a variety of science disciplines using both archived and real-time data. It is currently being used to support the Microwave Limb Sounder (MLS) in orbit aboard the Upper Atmosphere Research Satellite (UARS). This paper describes the application of LinkWinds to this data to rapidly detect features, such as the ozone hole configuration, and to analyze correlations between chemical constituents of the atmosphere.

Jacobson, Allan S.↗

NASA NTRS1998

Reynolds Number Effects at High Angles of Attack

Lessons learned from comparisons between ground-based tests and flight measurements for the high-angle-of-attack programs on the F-18 High Alpha Research Vehicle (HARV), the X-29 forward-swept wing aircraft, and the X-31 enhanced fighter maneuverability aircraft are presented. On all three vehicles, Reynolds number effects were evident on the forebodies at high angles of attack. The correlation between flight and wind tunnel forebody pressure distributions for the F-18 HARV were improved by using twin longitudinal grit strips on the forebody of the wind-tunnel model. Pressure distributions obtained on the X-29 wind-tunnel model at flight Reynolds numbers showed excellent correlation with the flight data up to alpha = 50 deg. Above (alpha = 50 deg. the pressure distributions for both flight and wind tunnel became asymmetric and showed poorer agreement, possibly because of the different surface finish of the model and aircraft. The detrimental effect of a very sharp nose apex was demonstrated on the X-31 aircraft. Grit strips on the forebody of the X-31 reduced the randomness but increased the magnitude of the asymmetry. Nose strakes were required to reduce the forebody yawing moment asymmetries and the grit strips on the flight test noseboom improved the aircraft handling qualities.

Fisher, David F.↗

NASA NTRS2010

SSV Launch Monitoring Strategies: HGDS Design Implementation Through System Maturity

With over 500,000 gallons of liquid hydrogen and liquid oxygen, it is of vital importance to monitor the space shuttle vehicle (SSV) from external tank (ET) load through launch. The Hazardous Gas Detection System (HGDS) was installed as the primary system responsible for monitoring fuel leaks within the orbiter and ET. The HGDS was designed to obtain the lowest possible detection limits with the best resolution while monitoring the SSV for any hydrogen, helium, oxygen, or argon as the main requirement. The HGDS is a redundant mass spectrometer used for real-time monitoring during Power Reactant Storage and Distribution (PRSD) load and ET load through launch or scrub. This system also performs SSV processing leak checks of the Tail Service Mast (TSM) umbilical quick disconnects (QD's), Ground Umbilical Carrier Plate (GUCP) QD's and supports auxiliary power unit (APU) system tests. From design to initial implementation and operations, the HGDS has evolved into a mature and reliable launch support system. This paper will discuss the operational challenges and lessons learned from facing design deficiencies, validation and maintenance efforts, life cycle issues, and evolving requirements

Shoemaker, Marc D.↗

NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗

NASA NTRS1999

Advanced Communications Technology Satellite (ACTS): Four-Year System Performance

The Advanced Communications Technology Satellite (ACTS) was conceived at the National Aeronautics and Space Administration (NASA) in the late 1970's as a follow-on program to ATS and CTS to continue NASA's long history of satellite communications projects. The ACTS project set the stage for the C-band satellites that started the industry, and later the ACTS project established the use of Ku-band for video distribution and direct-to-home broadcasting. ACTS, launched in September 1993 from the space shuttle, created a revolution in satellite system architecture by using digital communications techniques employing key technologies such as a fast hopping multibeam antenna, an on-board baseband processor, a wide-band microwave switch matrix, adaptive rain fade compensation, and the use of 900 MHz transponders operating at Ka-band frequencies. This paper describes the lessons learned in each of the key ACTS technology areas, as well as in the propagation investigations.

Acosta, Roberto J.↗

NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗

NASA NTRS1986

At least some errors are randomly generated (Freud was wrong)

An experiment was carried out to expose something about human error generating mechanisms. In the context of the experiment, an error was made when a subject pressed the wrong key on a computer keyboard or pressed no key at all in the time allotted. These might be considered, respectively, errors of substitution and errors of omission. Each of seven subjects saw a sequence of three digital numbers, made an easily learned binary judgement about each, and was to press the appropriate one of two keys. Each session consisted of 1,000 presentations of randomly permuted, fixed numbers broken into 10 blocks of 100. One of two keys should have been pressed within one second of the onset of each stimulus. These data were subjected to statistical analyses in order to probe the nature of the error generating mechanisms. Goodness of fit tests for a Poisson distribution for the number of errors per 50 trial interval and for an exponential distribution of the length of the intervals between errors were carried out. There is evidence for an endogenous mechanism that may best be described as a random error generator. Furthermore, an item analysis of the number of errors produced per stimulus suggests the existence of a second mechanism operating on task driven factors producing exogenous errors. Some errors, at least, are the result of constant probability generating mechanisms with error rate idiosyncratically determined for each subject.

Sellen, A. J.↗