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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 NTRS1988

A plan for time-phased incorporation of automation and robotics on the US space station

A plan for the incorporation of Automation and Robotics technology on the Space Station is presented. The time phased introduction of twenty two selected candidates is set forth in accordance with a technology development forecast. Twenty candidates were chosed primarily for their potential to relieve the crew of mundane or dangerous operations and maintenance burdens, thus freeing crew time for mission duties and enhancing safety. Two candidates were chosen based on a potential for increasing the productivity of laboratory experiments and thus directly enhancing the scientific value of the Space Station. A technology assessment for each candidate investigates present state of the art, development timelines including space qualification considerations, and potential for technology transfer to earth applications. Each candidate is evaluated using a crew workload model driven by crew size, number of pressurized U.S. modules and external payloads, which makes it possible to assess the impact of automation during a growth scenario. Costs for each increment of implementation are estimated and accumulated.

Purves, R. B.↗
NASA NTRSDate not supplied

Spacecraft-Initiated Scheduling of Commercial Communications Services

We propose a software framework enabling a spacecraft in Earth orbit to schedule its own access to space communications services via ground stations and relay satellites. All operations are automated, without a human in the loop, allowing for highly responsive service to meet time-varying needs. We describe a modular software architecture allowing the framework to interface with the scheduling systems of several providers - both government and commercial. Specifically, we present the results of an experiment with Amazon Web Services (AWS) Ground Station, an operational commercial service provider. Proposed software executed scheduling of a ground station antenna with minimal lead time (less than 20 minutes before the start of a pass). During the contact AWS cloud infrastructure was used to demodulate, process, and distribute data products from an orbiting satellite. We believe this work demonstrates the framework's ability to interface with additional providers and potential to integrate more complex decision-making in the future to schedule optimal contacts.

Scheduling↗
DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2007

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats.

Spruce, Joseph P.↗
DOE OSTI2023

A Simple and Flexible Infusion Platform for Automated Native Mass Spectrometry Analysis

High throughput native mass spectrometry analysis of proteins and protein complexes has been enabled by recent development of infusion and liquid chromatography (LC) systems, which often include complete LC pumps without fully utilizing their gradient flows. We demonstrated a lower-cost infusion cart for native mass spectrometry applications using a single isocratic solvent pump that can operate at both nano- and high-flow configurations (0.05-150 $μ$L/min) for both infusion and online buffer exchange experiments. In conclusion, the platform is controlled via open-source software and can potentially be expanded for customized experimental designs, offering a lower cost alternative to laboratories with limited budgets and/or needs in student training.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Machine-learning-assisted insight into spin ice Dy 2 Ti 2 O 7

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy 2 Ti 2 O 7 . Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use an automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy 2 Ti 2 O 7 . The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Automatically Finding Ship-Tracks to Enable Large-Scale Analysis of Aerosol-Cloud Interactions

Ship tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol‐cloud interaction experiments. However, manually finding ship tracks from satellite data on a large scale is prohibitively costly while a large number of samples are required to improve our understanding. Here we train a deep neural network to automate finding ship tracks. The neural network model generalizes well as it not only finds ship tracks labeled by human experts but also detects those that are occasionally missed by humans. It finds more ship tracks than all previous studies combined and produces a map of ship track distributions off the California coast that matches well with known shipping traffic. Our technique will enable studying aerosol effects on low clouds using ship tracks on a large scale, which will potentially narrow the uncertainty of the aerosol‐cloud interactions.

aerosol cloud interactions↗
NASA NTRS1988

Automated seed manipulation and planting

Activities for the Fall Semester, 1987 focused on investigating the mechanical/electrical properties of wheat seeds and forming various Seed Planting System (SPS) concepts based on those properties. The Electrical Division of the design group was formed to devise an SPS using electrostatic charge fields for seeding operations. Experiments concerning seed separation using electrical induction (rearranging of the charges within the seed) were conducted with promising results. The seeds, when exposed to the high voltage and low current field produced by a Van de Graff generator, were observed to move back and forth between two electrodes. An SPS concept has been developed based on this phenomena, and will be developed throughout the Spring Semester, 1988. The Mechanical Division centered on SPS concepts involving valves, pumps, and fluids to separate and deliver seeds. An SPS idea utilizing the pressure difference caused by air as it rushes out of holes drilled in the wall of a closed container has been formulated and will be considered for future development. Also, a system of seed separation and delivery employing a combination of centrifugal force, friction, and air flow was considered.

Garcia, Ray↗
DOE OSTI2022

Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor

Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1-4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6], but the impact in an urban setting has not been studied yet. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated with real-world data in PTV Vissim traffic microsimulation software. Five forward-looking powertrain models, consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles, were developed to estimate the energy consumption of vehicles on the corridor. A comprehensive full factorial simulation study was performed. The simulation results indicate that for a traffic mix based on projected new vehicles sales in 2025, a 15% corridor-level energy consumption reduction can be achieved. The paper details the development and validation of the simulation framework, design of experiments conducted, a discussion of challenges faced, and results under various test conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Moving toward automated µFTIR spectra matching for microplastic identification: addressing false identifications and improving accuracy

Abstract Infrared spectroscopy is a widely used tool for studying microplastics and identifying microparticles. Researchers rely on spectral libraries to differentiate between synthetic and natural materials. Unfortunately, spectral library matching is not perfect, and best practices require researchers to use time consuming, manual peak matching to assess spectral matches. Moving toward automated matching requires increased confidence in the matching process. Using spectra matching software may increase the efficiency of particle identification, however some matching strategies may confuse natural materials such as cotton, silk, and plant matter with common classes of synthetics such as polyesters and polyamides. In this experiment, we prepared 22 pristine sample materials from natural and synthetic sources and measured micro-Fourier transform infrared (µFTIR) spectra in transmission mode for each sample using a Thermo Nicolet iN10 MX instrument. The collected spectra were then input into two spectral library matching systems (Omnic Picta and Open Specy), using a total of five identification routines. Next, we placed a subset of four pristine microplastic materials in a biologically active river system for two weeks to simulate environmental samples. These simulated environmental samples were processed using 10% hydrogen peroxide for 24 h to remove organic contamination and then identified using the strongest performing library. We found that libraries with fewer sample spectra produced lower correlation matches and that using derivative correction greatly reduced the number of inaccuracies in identifying materials as either natural or synthetic. We also found that environmental fouling reduced the correlation value of library matches when compared to pristine particles, however the effect was not consistent across the four materials tested. Overall, we found that the accuracy of automated library matching in the tested systems and processing routines varied from 64.1 to 98.0% for distinguishing between natural and synthetic materials, and that a high Hit Quality Index (HQI) did not always correlate with accuracy. These results are important for the microplastic field, demonstrating a need to rigorously test spectral libraries and processing routines with known materials to ensure identification accuracy.

Kozloski, Rachel↗
DOE OSTI2023

Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

An automated system for creep testing

A completely automated data collection system was devised to measure, analyze, and graph creep versus time using a PC, a 16 channel multiplexed analog to digital converter, and low friction potentiometers to measure length. The sampling rate for each experiment can be adjusted in the software to meet the needs of the material tested. Data is collected and stored on a diskette for permanent record and also for later data analysis on a different machine.

Spiegel, F. Xavier↗
NASA NTRS1992

Software Management Environment (SME) concepts and architecture, revision 1

This document presents the concepts and architecture of the Software Management Environment (SME), developed for the Software Engineering Branch of the Flight Dynamic Division (FDD) of GSFC. The SME provides an integrated set of experience-based management tools that can assist software development managers in managing and planning flight dynamics software development projects. This document provides a high-level description of the types of information required to implement such an automated management tool.

Hendrick, Robert↗
NASA NTRS1997

Towards High-Assurance High-Performance Program Synthesis

Domain-specific automatic program synthesis tools, also called application generators, are playing an ever-increasing role in software development. However, high-performance application generators require difficult manual construction, and are very difficult to verify correct. This paper describes research and an implemented system that transforms program synthesis tools based on deductive synthesis into high-performance application generators. Deductive synthesis uses theorem-proving to construct solutions when given problem specifications. The verification condition for a deductive synthesis tool is essentially the soundness of the implemented inference rules. Theory Operationalization for Program Synthesis (TOPS) synergistically combines reformulation, automated mathematical classification, and compilation through partial deduction to decision procedures. It transforms general-purpose deductive synthesis, with exponential performance, into efficient special-purpose deductive synthesis, with near-linear performance. This paper describes our experience with and empirical results of PD(TH) theory-based partial deduction - in which partial deduction of a set of first-order formulae is performed within the context of a background theory. The implemented TOPS system currently performs a special variant of PD(TH) in which the compilation process results in the transformation of a set of first order formulae into the theory of an instantiated library decision procedure augmented by a compiled unit theory.

Lowry, Michael↗
NASA NTRS1996

A Model-based Approach to Reactive Self-Configuring Systems

This paper describes Livingstone, an implemented kernel for a self-reconfiguring autonomous system, that is reactive and uses component-based declarative models. The paper presents a formal characterization of the representation formalism used in Livingstone, and reports on our experience with the implementation in a variety of domains. Livingstone's representation formalism achieves broad coverage of hybrid software/hardware systems by coupling the concurrent transition system models underlying concurrent reactive languages with the discrete qualitative representations developed in model-based reasoning. We achieve a reactive system that performs significant deductions in the sense/response loop by drawing on our past experience at building fast prepositional conflict-based algorithms for model-based diagnosis, and by framing a model-based configuration manager as a prepositional, conflict-based feedback controller that generates focused, optimal responses. Livingstone automates all these tasks using a single model and a single core deductive engine, thus making significant progress towards achieving a central goal of model-based reasoning. Livingstone, together with the HSTS planning and scheduling engine and the RAPS executive, has been selected as the core autonomy architecture for Deep Space One, the first spacecraft for NASA's New Millennium program.

Williams, Brian C.↗
NASA NTRS2017

Airspace Technology Demonstration 3 (ATD-3): Multi-Flight Common Route (MFCR) Technology Transfer Document Summary Version 1.0

This summary document and accompanying technology artifacts satisfy the second of three Research Transition Products (RTPs) defined in the ATD-3 Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan. This transfer consists of NASA's Multi-Flight Common Route (MFCR) research for efficient route corrections for en-route weather avoidance. The MFCR concept builds on the experience of the legacy Dynamic Weather Routes (DWR) and focuses on a better balance of potential savings with ATC acceptability, common route corrections options for multiple flights on similar routings, and better use of existing and/or modern automation for communication and coordination of route change options. All of these capabilities are expected to improve system performance significantly in terms of actual delay-reducing clearances issued to flights compared to that of the DWR tool and operating concept.

tech transfer↗
NASA NTRSDate not supplied

Automating Maneuvers: Considerations for Collision Avoidance

As more space operators implement large constellations of spacecraft, automating orbit maintenance maneuvers becomes a key feature of their operations concept to ensure that the workload is manageable. However, the practice of performing a maneuver without sharing the plan with other nearby spacecraft causes a risk that two spacecraft will collide, not only destroying the spacecraft involved, but creating debris that will affect all other spacecraft using that orbit regime. In order to share the maneuver plan, a predicted ephemeris file containing the maneuver must be sent to a central authority to screen against predicted trajectories of all other on-orbit objects to determine where and when close approaches will occur that may need to be mitigated. Currently the screening authority used by US operators is the 19th Space Defense Squadron; screenings are performed once every 8 hours, meaning that spacecraft using automated maneuvering need to allow 16 hours to share their maneuver plan via the screening process in advance of maneuver execution in case one screening is missed and the next needs to be used. In an effort to speed up the screening process to benefit spacecraft using automated maneuvering, a prototype system for performing near-real-time screenings has been developed in support of the NASA Starling mission, a constellation of four cubesats that fly at the same altitude as the SpaceX Starlink constellation. Both of these constellations perform automated maneuvering, so without screening the planned maneuvers before execution, the two constellations would risk a collision. This paper describes the traditional conjunction assessment (CA) process, the prototype real-time CA screening capability, plans for the experiment to test the prototype, and next steps.

conjunction assessment↗