SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Graphical data necessary for the analysis of planetary exploration missions to Jupiter are presented. Positional and geometric information spanning the time period from 1985 through 2020 is provided. The data and their usage are explained.
Graphical data necessary for the analysis of planetary exploration missions to Saturn are presented. Positional and geometric information spanning the time period from 1985 through 2020 is provided. The data and their usage are explained.
The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.
The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.
Approaches used in the selection of lunar landing sites are discussed, giving attention to a survey of major constraints affecting site selection. The science objectives of lunar exploration are considered, taking into account exploration goals, the acquisition of required data, mission science planning, orbital science, lunar surface traverse capability, the mare basalts, lunar interior composition, regolith, breccias, lunar chronology, lunar surface processes, and the origin of the moon. Subjects examined in connection with Apollo system capabilities include the Apollo missions, development missions, lunar exploration missions, mission design requirements, navigation, launch vehicle considerations, methods of determining accessibility, lunar module descent considerations, landing site redesignation, and questions of landability.
A navigation experiment was performed which establishes Ultra-Stable Oscillator (USO) frequency stabilized one way return link Doppler TDRSS tracking data as a feasible option for mission orbit determination support at the Goddard Space Center Flight Dynamics Facility. The study was conducted using both one way and two way Tracking and Data Relay Satellite System (TDRSS) tracking measurements for the Cosmic Background Explorer (COBE) spacecraft. Tracking data for a 4 week period immediately follow the depletion of the helium supply was used. The study showed that, for both definitive orbit solution and short term orbit prediction (up to 4 weeks), orbit determination results based on one way return link Doppler tracking measurements are comparable to orbit determination results based on two way range and two way Doppler tracking measurements.
The Mossbauer (MB) spectrometers on the Mars Exploration Rovers (MER) Spirit (Gusev crater) and Opportunity (Meridiani Planum) have detected 14 Fe-bearing phases, and mineralogical assignments have been made for all except 3. Identified Fe2+-bearing phases are olivine, pyroxene, ilmenite, and troilite. Magnetite and chromite are present as mixed Fe(2+) and Fe(3+) phases. Identified Fe(3+) phase are jarosite, hematite, goethite, and nanophase ferric oxide (npOx). Fe(sup 0) (iron metal) is present as kamacite. Nanophase ferric oxide (npOx) is a generic name for octahedrally coordinated Fe(3+) alteration products that cannot be otherwise mineralogically assigned on the basis of MER data. On the Earth, npOx would include ferrihydrite, iddingsite, schwertmannite, akaganeite, and superparamagnetic hematite and goethite. The Mars Reconnaissance Orbiter CRISM instrument, a visible, near-IR hyperspectral imager (approximately 0.35 to 4 micron) enables mineralogical examination of Mars with a tool that is sensitive to H2O and to M-OH (M = Al, Si, Fe, Mg, etc.) at spatial resolution of about 20 m/pixel. We examined a CRISM image of the MER region of Gusev crater (Columbia Hills and plains to the west), looking for spectral evidence of the aqueous process apparent from the MER analyses. We also searched for spectral constraints for the mineralogical composition of our unidentified Fe-bearing phases and the forms of npOx present on Mars. We also consider evidence from analogue samples that the precursor for the goethite detected by MB in Clovis Class rocks is an iron sulfide. We suggest that there is some indirect evidence that elemental sulfur might be present to different extents in Clovis Class rocks, the Fe3Sulfate-rich soils, and perhaps even typical (Laguna Class) surface soils.
Lunar AIMP photographic data processing system which transforms photographic scans of moon into rectified lunar pictures, particulary ground processor system
The Earth Orbiting Satellite (EOS), scheduled for launch in the mid-1990s, will include a next-generation multispectral imaging system (HIRIS) having unprecedented spatial and spectral resolution. Its high resolution, however, comes at the cost of a raw data rate which exceeds the communication channel capacity assigned to the entire EOS mission. This paper explores noisy compression algorithms which may compress multispectral data by up to 30:1 or more. Algorithm performance is measured using both traditional and mission-oriented criteria (e.g., feature classification consistency). It is shown that vector quantization, merged with suitable preprocessing techniques, is the most viable candidate.
NASA sounding rocket 21.114, launched March 7, 1998, during the Coqui II campaign, provided neutral wind and plasma density measurements of a weak intermediate layer. The layer was centered near 140 km and had an approximate peak plasma density of 2200 cc. The measured winds were typically less than 40 m/s, in agreement with wind shear formation theory and coincident density observations. The data obtained during the flight allow us to explore the plasma density structure and wind field morphology of the intermediate layer. Coupled with simultaneous data from Arecibo Observatory, the upleg and downleg density profiles provide three spatially separated measurements that enable the first detailed investigation of the horizontal extent and variation of an intermediate layer.
The Advanced Modular Power Systems (AMPS) project is developing a modular approach to spacecraft power systems for exploration beyond Earth orbit. AMPS is intended to meet the need of reducing the cost of design development, test and integration and also reducing the operational logistics cost of supporting exploration missions. AMPS seeks to establish modular power building blocks with standardized electrical, mechanical, thermal and data interfaces that can be applied across multiple exploration vehicles. The presentation discusses the results of a cost analysis that compares the cost of the modular approach against a traditional non-modular approach.
At NASA, we develop myriad Eclipse RCP applications to provide situational awareness for remote systems. The Intelligent Robotics Group at NASA Ames Research Center has developed VERVE - a high-performance, robot user interface that provides scientists, robot operators, and mission planners with powerful, interactive 3D displays of remote environments.VERVE includes a 3D Eclipse view with an embedded Java Ardor3D scenario, including SWT and mouse controls which interact with the Ardor3D camera and objects in the scene. VERVE also includes Eclipse views for exploring and editing objects in the Ardor3D scene graph, and a HUD (Heads Up Display) framework allows Growl-style notifications and other textual information to be overlayed onto the 3D scene. We use VERVE to listen to telemetry from robots and display the robots and associated scientific data along the terrain they are exploring; VERVE can be used for any interactive 3D display of data.VERVE is now open source. VERVE derives from the prior Viz system, which was developed for Mars Polar Lander (2001) and used for the Mars Exploration Rover (2003) and the Phoenix Lander (2008). It has been used for ongoing research with IRG's K10 and KRex rovers in various locations. VERVE was used on the International Space Station during two experiments in 2013 - Surface Telerobotics, in which astronauts controlled robots on Earth from the ISS, and SPHERES, where astronauts control a free flying robot on board the ISS.We will show in detail how to code with VERVE, how to interact between SWT controls to the Ardor3D scenario, and share example code.
Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services
A brief description of the nature of comets and of their interactions with their environments is given. Past observations are discussed, and particularly the data obtained for the West and Bradfield comets are compared. Far-ultraviolet observations are discussed in terms of the possibility of detecting the more volatile species such as H2O, CO2 and CO, which all have signatures in the ultraviolet spectral region. The comets observed by the International Ultraviolet Explorer are listed with the corresponding data. The space missions to be made in 1985-1986 are mentioned and these are the Giotto spacecraft mission, and the OSS-3 observatory that will fly aboard the Space Shuttle.
Relationships between simultaneous plasma wave, magnetic field, and energetic heavy ion data obtained by the International Cometary Explorer (ICE) spacecraft in the large-scale solar wind particle pickup region surrounding Comet Giacobini-Zinner are examined. In particular, consideration is given to the conditions under which electrostatic emissions at frequencies of a few kilohertz and electromagnetic waves at a few tens of hertz are observed. It is shown that the data are consistent with the view that the kilohertz electrostatic emissions result from a beam-type instability excited by the pickup photoelectron population when the angle alpha between the magnetic field and the plasma velocity vectors is not too large. The data also suggest that the few tens of hertz electromagnetic waves may be excited by a ring-type instability associated with the pickup ion population, which occurs when the magnetic field is near to orthogonality with the flow.
Are NASA’s space flight instruments becoming cheaper or more expensive as time marches forward? After analyzing the costs of hundreds of instruments launched over the last 30 years, the short answer to this question is no… and yes. This paper gives a visual analysis of the cost time trends for various NASA space flight instrument types, such as optical, particles detectors, fields detectors and microwave instruments. In addition to the statistical approaches utilized, such as significance tests, cluster analysis and principle components analysis (PCA), we will also discuss the intangibles which are likely at play, including technological progress, NASA policy and the luck of the draw associated with mission manifests. This analysis was performed as the main driver for the NASA Instrument Cost Model (NICM) recent cost estimating model redesign. Started in 2004, the first version of NICM was based off of instruments launched from 1985-2005, or 20 years’ worth of data. As NICM hit its 10-year anniversary, we wanted to know: should NICM continue to only use the most recent 20 years’ worth of data (1995-2015)? Are instruments becoming cheaper or more expensive as time marches forward? There is evidence in favor of a drop in the median dollar-per-kg value across some instrument types, but little in others. Whereas further research is needed to substantiate, Particles and Optical-Planetary instrument types show moderate to strong evidence of a downward trend in dollar-per-kg. Further research is required to study the nature of this trend (shift, taper, cyclic, etc.). Little evidence for a similar downward trend was detected for Fields or Microwave instruments, or Optical instruments on Earth Orbiting spacecraft. We presented evidence in favor of a drop in the median dollar-per-kg value for Particles and Optical-Planetary instrument types. While similar evidence was weak at best for Fields and Microwave instruments. We can speculate as to the causes for this effect, but we are also equipped to begin to rule out, or at least prioritize, some of the suspected drivers. We observed, for Particles and Optical-Planetary instruments, that perhaps a launch manifest effect was playing part of the role in the observed decrease in dollar-per-kg over the years, noting that the more flagship class missions, which have more money to spend on their instruments, were seen in the earlier years in our data, versus the later years which were dominated by less expensive class missions. However, if this were a dominating driver, would we not have seen the downward trend in the Fields and Microwave instruments as well, which were drawn from that same launch manifest? The fact that we did not observe this helps us rule out the launch manifest effect, and other drivers, such as advances in technology, that seem to be more likely suspects. In that case, however, why would technology advances be helping the Particles and Optical-Planetary instruments only? Why would it not be impacting Optical-Earth Orbiting instruments? Further suspects were looked at as well and ruled out, such as the “Faster, Better, Cheaper”era of NASA development which did not seem to actually impact trends by instrument type on a dollar-perkg scale. VI. Future Work A. Time Series Detailed Statistical Assessment The analysis discussed above sets the foundation for a more rigorous time series analysis of the data. Time series analysis will further explore evidence to-date of time trends for the instrument types which showed the strongest indicators for a decrease in dollar-per-kg: Optical (Planetary) and Particles instruments. More than providing evidence and top-level significance tests, time series analysis would help elucidate what kind of trend that exists in the data, their significance and allow statistically based forecasting (see Figure 10)
The success in future long term space exploration missions will depend on the cooperation, coordination, and mutual understanding among the crew members. Micro-behaviors are momentary, subtle linguistic and paralinguistic indicators of thinking and feeling toward another member of the team (Cortina et al., 2001; Smith & Griffiths, 2022) that can significantly impact team dynamics and influence the overall team performance (Paromita & Chaspari, 2024). Due to their interactive nature, micro-behaviors have a sender (i.e., the team member expressing the micro-behavior) and a target (the team member impacted by the micro-behavior). Detection of these behaviors can assist in avoiding possible conflict among crew members and promoting the overall team success. Our prior research focused on an initial proof of concept of machine learning (ML) models and natural language processing (NLP) techniques that were used for automatically detect micro-behaviors among crew members of the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA) Campaigns 4 and 5 missions (Paromita et al., 2023). Results underscored the importance of incorporating contextual information in the ML models in the form of sentiment analysis, type of task, and dyadic interaction among team members. Here, we expand the scope of our prior work in two ways. First, we assess ML/NLP methods on new behavioral annotations coded using an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). Second, we expand the design of the ML model to preserve information about the role of each team member within the occurrence of the micro-behavior (in contrast to the previous model that only considered the sender and the target without determining the team member role). This allows to consider all team members' contributions in the conversation and model long-term dependencies in the dialogue. Our experiments for this study are conducted on data from 5 teams of the NASA HERA C4 (NASA grant NNX16AQ48G (PI: Bell)). Conversations were extracted from the 1.5 hour Team Interaction Battery (TIB) task that occurred 5 times in-mission per crew. This resulted in a total of 13,058 conversational turns (i.e., 17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls). Our findings with the revised behavioral coding and ML/NLP models indicate a 43.66% macro F1-score (i.e., 38.29% precision (P), 50.8% recall (R)) for a dialog state-tracking model that includes information from the sender only, and a 40.9% F1-score (i.e., 38.7% P, 43.36% R) for the same model that includes information from both the sender and the target of the micro-behavior. These are significantly higher compared to simple random forest models that classify behaviors strictly based on speech content and do not consider iterative team dynamics, achieving a 36.07% F1-score (i.e., 39.04% R, 33.53% P). Our findings demonstrate potential ways to leverage large conversational datasets to better capture complex team dynamics. We will discuss future directions including proposed models that can incorporate additional mission days and tasks beyond the TIB for objectively quantifying team behavior at high temporal resolution in space exploration missions.
Cloud Computing has been implemented in several commercial arenas. The NASA Nebula Cloud Computing platform is an Infrastructure as a Service (IaaS) built in 2008 at NASA Ames Research Center and 2010 at GSFC. Nebula is an open source Cloud platform intended to: a) Make NASA realize significant cost savings through efficient resource utilization, reduced energy consumption, and reduced labor costs. b) Provide an easier way for NASA scientists and researchers to efficiently explore and share large and complex data sets. c) Allow customers to provision, manage, and decommission computing capabilities on an as-needed bases