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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Recent Results from the Lunar Reconnaissance Orbiter Mission and Plans for the Extended Science Phase

The Lunar Reconnaissance Orbiter spacecraft (LRO), launched on June 18, 2009, began with the goal of seeking safe landing sites for future robotic missions or the return of humans to the Moon as part of NASA's Exploration Systems Mission Directorate (ESMD). In addition, LRO's objectives included the search for surface resources and to investigate the Lunar radiation environment. After spacecraft commissioning, the ESMD phase of the mission began on September 15, 2009 and completed on September 15, 2010 when operational responsibility for LRO was transferred to NASA's Science Mission Directorate (SMD). The SMD mission was scheduled for 2 years and completed in September, 2012. The LRO mission has been extended for two years under SMD. The extended mission focuses on a new set of goals related to understanding the geologic history of the Moon, its current state, and what it can tell us about the evolution Of the Solar System. Here we will review the major results from the LRO mission for both exploration and science and discuss plans and objectives going forward including plans for the extended science phase out to 2014. Results from the LRO mission include but are not limited to the development of comprehensive high resolution maps and digital terrain models of the lunar surface; discoveries on the nature of hydrogen distribution, and by extension water, at the lunar poles; measurement of the day and night time temperature of the lunar surface including temperature down below 30 K in permanently shadowed regions (PSRs); direct measurement of Hg, H2, and CO deposits in the PSRs, evidence for recent tectonic activity on the Moon, and high resolution maps of the illumination conditions as the poles. The objectives for the second and extended science phases of the mission under SMD include: 1) understanding the bombardment history of the Moon, 2) interpreting Lunar geologic processes, 3) mapping the global Lunar regolith, 4) identifying volatiles on the Moon, and 5) measuring the Lunar atmosphere and radiation environment.

Vondrak, Richard↗
DOE OSTI2019

R and DE Robotic Sensor Intern Team: Remote 3D Mapping Sensor Suite

The primary goal of the sensor suite is to provide an internal 3D map of the HCAEX tunnel. When looking for a sensor that would provide XYZ point cloud data for mapping the decision was between a stereo camera, a 3D Lidar, or a 2D Lidar that would need to be spun around. The issues with the stereo camera were that it does not have a large field-of-view and that its depth information was not nearly as accurate as even the baseline Lidars. The selection between Lidars is detailed. The Hokuyo UTM-30LX-EW shown provides a 270 deg. FOV with high accuracy and resolution while the largest FOV on a 3D Lidar is 40 deg.. This was the reason a 2D Lidar was chosen over the 3D Lidar. Even though the 2D Lidar requires additional components and software to generate a 3D map, its FOV advantage is too great given that the HCAEX tunnel is a relatively confined space. The rotating planar scan from the 2D Lidar can be stitched into a 3D scan as shown in the graphic below. Along with XYZ data, the Lidar provides return intensity data in color which can be used to distinguish between different materials within the tunnel. To provide visual feedback a camera system needed to be identified. The common solution for this is a high resolution camera with a small FOV mounted on a pan-tilt system. But, given the critical nature of the visual feedback we looked into solutions without the need for moving parts. Two panospheric cameras give a FOV large enough to get a full 360 deg. view.. Camera color data (RGB) can also be combined with the 3D mapping data from the Lidar and overlaid on the map to give the user the ability to see inside the entire length of the tunnel as if they were inside. Situational awareness is required for the crawler in the tunnel in order for it to be able to register the 3D scans to create a final map. For this we procured a Microstrain 3DM-GX5-25 Inertial Measurement Unit (IMU). To perform the Lidar rotation we have two servos driving the pan and tilt. The servo controls and most data processing will be performed via an onboard computer. The sensor components are to be packaged together using 3D printed mounts into a standalone unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2017

Skillful Spring Forecasts of September Arctic Sea Ice Extent Using Passive Microwave Data

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

melt onset↗
NASA NTRS1991

VLSI architectures for geometrical mapping problems in high-definition image processing

This paper explores a VLSI architecture for geometrical mapping address computation. The geometric transformation is discussed in the context of plane projective geometry, which invokes a set of basic transformations to be implemented for the general image processing. The homogeneous and 2-dimensional cartesian coordinates are employed to represent the transformations, each of which is implemented via an augmented CORDIC as a processing element. A specific scheme for a processor, which utilizes full-pipelining at the macro-level and parallel constant-factor-redundant arithmetic and full-pipelining at the micro-level, is assessed to produce a single VLSI chip for HDTV applications using state-of-art MOS technology.

Kim, K.↗
NASA NTRS2008

A Successful Infusion Process for Enabling Lunar Exploration Technologies

The NASA Vision for Space Exploration begins with a more reliable flight capability to the International Space Station and ends with sending humans to Mars. An important stepping stone on the path to Mars encompasses human missions to the Moon. There is little doubt throughout the stakeholder community that new technologies will be required to enable this Vision. However, there are many factors that influence the ability to successfully infuse any technology including the technical risk, requirement and development schedule maturity, and, funds available. This paper focuses on effective infusion processes that have been used recently for the technologies in development for the lunar exploration flight program, Constellation. Recent successes with Constellation customers are highlighted for the Exploration Technology Development Program (ETDP) Projects managed by NASA Glenn Research Center (GRC). Following an overview of the technical context of both the flight program and the technology capability mapping, the process is described for how to effectively build an integrated technology infusion plan. The process starts with a sound risk development plan and is completed with an integrated project plan, including content, schedule and cost. In reality, the available resources for this development are going to change over time, necessitating some level of iteration in the planning. However, the driving process is based on the initial risk assessment, which changes only when the overall architecture changes, enabling some level of stability in the process.

Over, Ann P.↗
NASA NTRSDate not supplied

2024 IEEE GRSS Data Fusion Contest Flood Rapid Mapping

The Challenge Task As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Flood rapid mapping products play an important role in informing flood emergency response and management. These maps are generated quickly from remote sensing data during or after an event to show the extent of flooding. They provide important information for emergency response, and damage assessment. The aim of this challenge is to develop data fusion algorithms that generate flood maps by processing spatial data from a variety of sources. The goal of this IEEE challenge (sponsored by NASA and CNES) is to design and develop an algorithm that will combine multi-source data to classify flood surface water extent–that is, water and non-water areas. Provided data sources include optical and SAR remote sensing images as well as a digital terrain model. The output is a gridded flood map where each grid cell is labeled water or non-water. The difficulty of detecting flooded areas can vary greatly depending on the conditions in the area of interest and the event. This data fusion challenge has two tracks representing this variance.

Jacqueline J Le Moigne-stewart↗
NASA NTRSDate not supplied

Benchmark Problem Development for Testing Maturity of Intelligent Contingency Management Tools

This paper presents a process used to develop appropriate scenarios and metrics for evaluating the maturity of intelligent contingency management algorithms. A benchmark scenario is a reference point against which something can be measured, compared, or assessed. Creating an accurate benchmark requires considerable research and expertise. The scenario itself is an artificial representation of a real-world event, designed to achieve a set of learning objectives through experiential learning. Designing an effective benchmark simulation scenario requires careful planning, including identification of clear objectives; capability assessment of the algorithm/tool being evaluated; assessment of necessary levels of fidelity; development of a process flow map of events and event interactions; and identification of metrics that map back to objectives. Thus, a benchmark scenarios for contingency management might consist of one or several commonly used functions taken from real world applications, used for evaluation, characterization and performance measurement of a contingency management algorithm. Behavior of the contingency management algorithm under different environmental conditions should then be able to be predicted using a set of benchmark functions. The paper describes the resulting benchmark problem as an illustration of the application of this process.

Jon Holbrook↗
NASA NTRS1999

Polarization Reversal Over Flooded Regions and Applications to Large-Scale Flood Mapping with Spaceborne Scatterometers

We present the polarization reversal in backscatter over flooded land regions, and demonstrate for the first time the utility of spaceborne Ku-band scatterometer for large-scale flood mapping. Scatterometer data were collected over the globe by the NASA Scatterometer (NSCAT) operated at 14 GHz on the Japanese ADEOS spacecraft from September 1996 to June 1997. During this time span, several severe floods occurred. Over most land surface, vertical polarization backscatter (Sigma(sub upsilon(upsilon)) is larger than horizontal polarization backscatter (sigma(sub hh)). Such polarization characteristics is reversed and sigma(sub upsilon(upsilon)) is smaller than sigma(sub hh) over flooded regions, except under a dense forest canopy. The total backscatter from the flooded landscape consists of direct backscatter and boundary-interaction backscatter. The direct term is contributed by direct backscattering from objects protruding above the water surface, and by backscattering from waves on the water surface. The boundary-interaction term is contributed by the forward scattering from the protruding objects and then reflected from the water surface, and also by the forward scattering from these objects after the water-surface reflection. Over flooded regions, the boundary-interaction term is dominant at large incidence angles and the strong water-surface reflection is much larger for horizontal polarization than the vertical one due to the Brewster effect in transverse-magnetic waves. These scattering mechanisms cause the polarization reversal over flooded regions. An example obtained with the Analytic Wave Theory is used to illustrate the scattering mechanisms leading to the polarization reversal. We then demonstrate the utility of spaceborne Ku-band scatterometer for large-scale flood mapping. We process NSCAT data to obtain the polarization ratio sigma(sub hh)/sigma(sub upsilon(upsilon)) with colocated data at incidence angles larger than 40 deg. The results over Asian summer monsoon regions in September-October 1996 indicate flooded areas in many countries such as Bangladesh, India, Lao, Vietnam, Cambodia, and China. Reports documented by the United Nation Department of Humanitarian Affairs (now called UN Office for the Coordination of Humanitarian Affairs) show loss of many lives and severe flood related damages which affected many million people in the corresponding flooded areas. We also map the NSCAT polarization ratio over the same regions in the "dry season" in January 1997 as a reference to confirm our results. Furthermore, we obtain concurrent ocean wind fields also derived from NSCAT data, and Asia topographic data (USGS GTOPO30) to investigate the flooded area. The results show that winds during summer monsoon season blowing inland, which perplex flood problems. Overlaying the topographic map over NSCAT results reveals an excellent correspondence between the confinement of flooded area within the relevant topographic features, which very well illustrates the value of topographic wetness index. Finally, we discuss the applications of future spaceborne scatterometers, including QuikSCAT and Seawinds, for flood mapping over the globe.

Nghiem, Son V.↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

New Tank Mapping Method Improves Waste Removal Process

Savannah River Mission Completion is the Liquid Waste (LW) contractor at the Savannah River Site (SRS). The LW mission is tasked with treating and disposing of legacy nuclear waste. There are multiple facilities involved in this work, including the Concentration, Storage, and Transfer Facilities (CSTF), the Defense Waste Processing Facility (DWPF), the Salt Waste Processing Facility (SWPF), and the Saltstone Production Facility (SPF). The CSTF includes 43 underground waste tanks used to store and support processing of radioactive liquid waste. Waste removal activities, such as salt dissolution campaigns and sludge agitation, are conducted within the CSTF waste tanks to convert the waste into a form that allows for downstream processing at other LW facilities. While performing these waste removal campaigns, camera inspections are performed to assess the quantity and distribution of the remaining waste within the waste tank (i.e. saltcake or sludge). Understanding the quantity and distribution of the salt/sludge within the waste tanks allows for improved waste removal strategies (e.g. mixing pump operation) and refined safety controls. Typically, several camera inspections are performed during a waste removal transfer to verify the elevation of the visible salt/sludge mounds against the known elevation of the liquid surface. The camera inspection footage must then be interpreted by a trained engineer who will develop a 2-D map that depicts the waste distribution at various elevations within the waste tank. This tank mapping is then used in conjunction with conservative assumptions to evaluate the volume of saltcake or sludge that is present within the waste tank.

Mini, Melany↗
DOE OSTI2024

Neural lumped parameter differential equations with application in friction-stir processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a “lumped” element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. Here, in this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Progress In The Scene-To-Map Registration Investigation

The geometric accuracy of the scene-to-map registration process for P-format LANDSAT MSS data for scenes from Kansas and Louisiana/Mississippi is discussed. Large scale row and column bias values and row and column standard deviation values were measured for the P-format data sets indicating a poor georegistration accuracy for these geometrically corrected LANDSAT MSS scenes. Experimental work is underway with A-format LANDSAT MSS scenes from the same locations to examine the influence of the number of ground control points and the spatial distribution of ground control points on geometric registration accuracy. An early conclusion from this work is that the root mean square approach for assessing how well the ground control points fit the mapping equations measures a different aspect of georegistration accuracy than does the approach of evaluating the bias (offset) and standard deviation using independently chosen ground reference points.

Dow, D. D.↗
NASA NTRS2013

Saturns Thermal Emission at 2.2-cm Wavelength as Imaged by the Cassini RADAR Radiometer

We present well-calibrated, high-resolution maps of Saturn's thermal emission at 2.2-cm wavelength obtained by the Cassini RADAR radiometer through the Prime and Equinox Cassini missions, a period covering approximately 6 years. The absolute brightness temperature calibration of 2% achieved is more than twice better than for all previous microwave observations reported for Saturn, and the spatial resolution and sensitivity achieved each represent nearly an order of magnitude improvement. The brightness temperature of Saturn in the microwave region depends on the distribution of ammonia, which our radiative transfer modeling shows is the only significant source of absorption in Saturn's atmosphere at 2.2-cm wavelength. At this wavelength the thermal emission comes from just below and within the ammonia cloud-forming region, and yields information about atmospheric circulations and ammonia cloud-forming processes. The maps are presented as residuals compared to a fully saturated model atmosphere in hydrostatic equilibrium. Bright regions in these maps are readily interpreted as due to depletion of ammonia vapor in, and, for very bright regions, below the ammonia saturation region. Features seen include the following: a narrow equatorial band near full saturation surrounded by bands out to about 10deg planetographic latitude that demonstrate highly variable ammonia depletion in longitude; narrow bands of depletion at -35deg latitude; occasional large oval features with depleted ammonia around -45deg latitude; and the 2010-2011 storm, with extensive saturated and depleted areas as it stretched halfway around the planet in the northern hemisphere. Comparison of the maps over time indicates a high degree of stability outside a few latitudes that contain active regions.

composition↗
DOE OSTI2026

Focused ion beam milling of polycrystalline diamond

Focused ion beam (FIB) technology has a large potential for precise milling of ultrahard materials such as diamond. However, studies of the sputtering of diamond with ion species and energies relevant to the FIB technology are scarce. Here, we report a systematic study of the sputtering of polycrystalline diamond surfaces with ion beams in a wide range of ion energies (2 - 30 keV), incident beam angles (0 - 80°), and ion masses (N, O, Ar, and Xe). Results show that the sputter yield is relatively insensitive to ion energy but strongly depends on the ion species and incident beam angles. Bombardment with O ions results in sputter yields that are significantly larger than ballistic predictions due to chemical etching effects. Characteristic surface ripples develop for oblique beam incidence angle irradiation. The critical beam tilt angle for the onset of ripple formation and the wavelength of the ripples depend on ion species and energies. Ripples appear to suppress ion reflection at high beam tilt angles and, hence, a peak in the sputter yield angular dependence. In conclusion, these results have direct implications for the development of process parameter maps for high-precision ion-beam milling of diamond.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Terrestrial Demonstration of Orbital Mapping and Validation Capabilities Over a Lunar Surface Analog

The Lunar Navigation Maps (LuNaMaps) project has improved existing tools and processes and developed new tools and processes to support the generation and validation of navigation maps of the lunar surface from orbital imagery. To demonstrate the advancements made through the LuNaMaps project, we conducted a terrestrial demonstration obtaining “orbital” imagery of the Lunar Surface Proving Ground (LSPG) at Astrobotic’s test facility in Mojave California. The LSPG is a 100 m by 100 m pad built to mimic the features and appearance of the lunar surface. In this paper we describe the planning and results of the test, including the capture of imagery for building the maps, the map building processes, building of a “truth map” using traditional surveying tools, and the map validation processes. We demonstrate how the built map compares to the “truth map” and how the validation processes provided insight to this comparison. Additionally, we describe an upcoming partner test in which the navigation maps will be used in a terrain relative navigation (TRN) technology demonstration over the same LSPG surface.

mapping↗
DOE OSTI2024

Parallel quantum computing simulations via quantum accelerator platform virtualization

Quantum circuit execution is a central task in quantum computation. Due to inherent quantum-mechanical constraints, quantum computing workflows often involve a considerable number of independent measurements over a large set of slightly different quantum circuits. Here we discuss a simple model for parallelizing such quantum circuit executions that is based on introducing a large array of virtual quantum processing units (mapped to HPC nodes in our case) as a parallel quantum computing platform. Implemented within the XACC framework, the model can readily take advantage of its backend-agnostic features, enabling parallel quantum computing/simulation over any target backend supported by XACC. We illustrate the performance of this approach by demonstrating strong scaling in two pertinent domain science problems, namely in computing the gradients for the multi-contracted variational quantum eigensolver and in data-driven quantum circuit learning, where we vary the number of qubits and the number of circuit layers. Here, the latter simulation leverages the cuQuantum library to run efficiently on GPU-accelerated HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

Through the integration of machine learning (ML) techniques alongside additive manufacturing (AM) experimentation, we demonstrate an iterative process to rapidly predict laser-material interactions and melt pool geometries throughout the build parameter space for a bismuth telluride thermoelectric (TE) material. In doing so, we determined process parameters that created crack-free, highly dense (>99 %) n-type bismuth telluride (Bi 2 Te 2.7 Se 0.3 ) parts through laser powder bed fusion (LPBF). Furthermore, the ML-assisted understanding of the processing space allowed for the identification of build parameters that successfully yielded geometrically enhanced Bi 2 Te 2.7 Se 0.3 parts with reduced build times and no increase in experimental effort.

36 MATERIALS SCIENCE↗