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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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These spreadsheets include a Techno-Economic Analysis (TEA) summary and descriptions and links to mining data analyzed as part of this study. The TEA summary includes the results from several mining data-informed geothermal development models analyzed using the DOE's Geothermal Electricity Technology Evaluation Model (GETEM).
The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.
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This slide deck is intended to be presented at the Digital Innovation Center for Excellence (DICE) conference May 2025. A lot of this information has already been approved through other presentations, but some information is new.
Timely data on low altitude energetic protons is very important for predicting the effect on humans and satellites. Because of the high bioeffectivity of protons in tissue, determination of the geographical location of the south Atlantic anomally (SAA) would be especially useful for reducing the risk associated with extravehicular activities. Because the AP8 model was developed with an epoch several decades old, the location of the south Atlantic anomally is incorrectly positioned in the model. The lack of extensive present-epoch energetic proton data at low altitude has prevented the update of the AP8 proton model. While there are studies in progress which are attempting to update the model at low altitude by using the path-integrated column density of the residual atmosphere as an organizing parameter, success in those studies will be defined by consistency in organization of old data. New data will still be required to provide a good present epoch model. As an alternative to using instrumental measurements of proton fluxes, background effects, proxy data sets such as SEUs, anomalies in instruments, enhanced background in shielded sensors believed to be induced by high energy protons, and dosimeter data in which key parameters (energy, efficiency, geometric factor, shielding, etc.) are poorly known can also be used. This in situ data is not as precise as direct measurement of the proton flux as geometric factor and shielding etc. are not calibrated, but these factors do not affect determination of the location of the SAA.
A computational framework for the simulation of dynamic stall suppression with combustion-powered actuation (COMPACT) is validated against wind tunnel experimental results on a VR-12 airfoil. COMPACT slots are located at 10% chord from the leading edge of the airfoil and directed tangentially along the suction-side surface. Helicopter rotor-relevant flow conditions are used in the study. A computationally efficient two-dimensional approach, based on unsteady Reynolds-averaged Navier-Stokes (RANS), is compared in detail against the baseline and the modified airfoil with COMPACT, using aerodynamic forces, pressure profiles, and flow-field data. The two-dimensional RANS approach predicts baseline static and dynamic stall very well. Most of the differences between the computational and experimental results are within two standard deviations of the experimental data. The current framework demonstrates an ability to predict COMPACT efficacy across the experimental dataset. Enhanced aerodynamic lift on the downstroke of the pitching cycle due to COMPACT is well predicted, and the cycleaveraged lift enhancement computed is within 3% of the test data. Differences with experimental data are discussed with a focus on three-dimensional features not included in the simulations and the limited computational model for COMPACT.
NASA's Earth Science Data and Information System (ESDIS) Project collects Earth science data usage metrics on a daily basis through the ESDIS Metrics System (EMS). This includes metrics on distribution of data products, users, data volumes, and number of files, which are key parameters in evaluating system-level performance of any of the Distributed Active Archive Centers (DAACs) encompassed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS). EOSDIS data usage metrics illustrate the benefits of making NASA data openly available to the public and show a rapid growth in data distribution to a worldwide user community. In fact, each year since 2014 the EOSDIS has distributed over one billion data files of products from EOS satellite, airborne, and in situ observations. An assessment of the long-term trends of data usage metrics and user characterization provides insights into data usability.This study will focus on describing the EMS as a metrics collection tool and will provide a comprehensive analysis of EOSDIS data usage metrics over the last 10 years. This study will also characterize the product distribution metrics by various tools and services, such as Giovanni, the Open-source Project for a Network Data Access Protocol (OPeNDAP), and subsets, to address how these tools/services have extended the usage of data in the EOSDIS collection. Data usage patterns based on discipline and study area will further assist in understanding how EOSDIS data user needs have evolved over time. Results from this study will provide useful information for the DAACs that can help them improve the functionality of their tools and services as well as more efficiently allocate the resources necessary for enhanced access and availability of their data products. Knowledge of these metrics may also benefit user discovery of data in the EOSDIS collection, promote research collaboration, and stimulate new ideas from work and research conducted using specific datasets and data collections.
CALIPSO aerosol backscatter enhancement in the transition zone between clouds and clear sky areas is revisited with particular attention to effects of data selection based on the confidence level of cloud-aerosol discrimination (CAD). The results show that backscatter behavior in the transition zone strongly depends on the CAD confidence level. Higher confidence level data has a flatter backscatter far away from clouds and a much sharper increase near clouds (within 4 km), thus a smaller transition zone. For high confidence level data it is shown that the overall backscatter enhancement is more pronounced for small clear-air segments and horizontally larger clouds. The results suggest that data selection based on CAD reduces the possible effects of cloud contamination when studying aerosol properties in the vicinity of clouds.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-OPE Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3). This is the FLUXNET version of the carbon flux data for the site US-OPE Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) produced by applying the standard ONEFlux (1F) software. Site Description - The Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site is located at the Beltsville Agricultural Research Center in Prince George's County, MD, and consists of a 22-ha production field, with an adjacent riparian wetland and first-order stream. Scientists from several U.S. Federal agencies, universities (foreign and domestic), and private industry have conducted multidisciplinary research at this location since 1998.
Techniques developed to automatically process a large quantity of physiological data obtained during a 1000-flight study are described. To reduce this data reliably, a study program was conducted using physiological data from X-15 flights as a data source for experimenting with signal enhancement and noise elimination techniques. The techniques include an automatic means for counting heart rates, averaging electrocardiogram waveforms, plotting histograms of heart rate versus frequency, and counting respiration rates. These techniques were used to reduce more than 2000 hours of physiological data recorded in flight.
The R-Matrix code SAMMY is a widely used nuclear data evaluation code focused on the resolved range, which includes corrections for experimental effects. The code is still mostly written in Fortran 77, and uses a memory management system suitable for the time of its initial writing (1984). A modernization effort is under way to bring the code in-line with modern software development practices. A continuous-integration testing framework was added, automating the large existing set of test cases. It is run on every commit. The memory management was updated to current standard practices suitable for modern software analysis tools. The code can be obtained from https://code.ornl.gov/RNSD/SAMMY. The resonance parameters and covariance information are now stored in C++ objects shared by SAMMY and AMPX, the processing code that generates nuclear data libraries for SCALE. This allows for easier maintenance and access to the resonance parameters inside and outside of SAMMY. This feature is already used by accessing and changing parameters in memory in the Bayesian Monte Carlo Evaluation Framework for Cross Sections Nuclear Data and Integral Benchmark Experiments project, Further plans include the switch to the ENDF reading and writing routines in AMPX, as these routines are more robust, easier to maintain, and support more features. Of note here is support for the new GNDS format. Previously it wasn’t easy to share the full covariance matrix for evaluations containing more than one isotope due to limitations on the ENDF format; this is now supported in GNDS. The data are currently available in a binary SAMMY format and can be exported to GNDS to make them more widely available and sharable. The next step will be to use the same resonance processing code at 0K in AMPX and SAMMY as one of the available Reich-Moore R-Matrix formalism. The first step toward this goal is to isolate the reconstruction into a module that takes resonance parameters as its input and does not depend on SAMMY global parameters. This goal has been achieved and it should now be possible to more easily change the resonance formalism and add enhancements as the Phenomenological R-Matrix parameterization of direct, doorway, and compound nuclear reactions discussed elsewhere on this conference. This concerted modernization and enhancement effort provides multiple advantages to the nuclear data community. It will allow parameter optimization using enhanced formalisms, including experimental effects, that better match complex experimental data. Then those evaluated parameters can immediately be passed off to AMPX to be reconstructed with the exact same cross section model and be put into a data library for subsequent testing using SCALE and the Valid Benchmark suite or other suitable benchmark suites.
Task-based programming models and execution paradigms provide a means to decompose a computation by expressing it as a graph in which each node represents a specific computation operating on memory objects and the edges define the dependencies in the execution flow. In this execution model, independent nodes in the graph can be executed concurrently in different computing devices, making it suitable for heterogeneous systems in which computing devices with different architectures coexist. However, careful memory orchestration across heterogeneous devices is needed because copies of the same memory object may reside in multiple devices during execution. Manually ensuring such an orchestration is quite challenging. Not only must an application developer guard against race conditions, but they must also optimize data movement between the host and devices because unnecessary data movement significantly impacts performance. To mitigate these challenges, we enhance the IRIS heterogeneous runtime and introduce IRIS-MEMFLOW–a data flow–enabled portable memory abstraction for seamlessly orchestrating memory in diverse heterogeneous computing environments. By using data-flow analysis, IRIS-MEMFLOW guards against race conditions while multiple heterogeneous devices access memory objects. IRIS-MEMFLOW also optimizes data movement between the host and devices without manual intervention. As a result, IRIS provides improved programming productivity, performance, and portability for multidevice heterogeneous executions in high-performance computing and cloud systems that run diverse architectures from different vendors. The efficacy of IRIS-MEMFLOW is evaluated through experiments that show its capability in terms of programming productivity, multidevice heterogeneity, portability, and low overhead versus the state of the art.
A description is presented of two enhancements for a two-camera, video imaging system that increase the accuracy and efficiency of the system when applied to the determination of three-dimensional locations of points along a continuous line. These enhancements increase the utility of the system when extracting quantitative data from surface and off-body flow visualizations. The first enhancement utilizes epipolar geometry to resolve the stereo "correspondence" problem. This is the problem of determining, unambiguously, corresponding points in the stereo images of objects that do not have visible reference points. The second enhancement, is a method to automatically identify and trace the core of a vortex in a digital image. This is accomplished by means of an adaptive template matching algorithm. The system was used to determine the trajectory of a vortex generated by the Leading-Edge eXtension (LEX) of a full-scale F/A-18 aircraft tested in the NASA Ames 80- by 120-Foot Wind Tunnel. The system accuracy for resolving the vortex trajectories is estimated to be +/-2 inches over distance of 60 feet. Stereo images of some of the vortex trajectories are presented. The system was also used to determine the point where the LEX vortex "bursts". The vortex burst point locations are compared with those measured in small-scale tests and in flight and found to be in good agreement.
Microbial monitoring onboard the International Space Station (ISS) is essential for assessing the efficiency of the Environmental Control and Life Support Systems (ECLSS) and providing insight into potential risk to both crew and spacecraft. Historically, this monitoring required the need to culture organisms onboard, return these cultures to Earth, and then complete the identifications, a process that would take months. Over the past decade, and through numerous payloads, advances in molecular biology have enabled in-flight microbial identifications using nanopore sequencing. The swab-to-sequencer method resulting from these efforts was transitioned from research to operations for microbial monitoring under the Crew Health Care Systems (CHeCS) BioMole. Collectively, these accomplishments have propelled the swab-to-sequencer method to be selected as the Microbial Surface Monitor (MSM) for Gateway, as well as a payload on Artemis IV. However, the lack of cold stowage availability for Artemis requires modifications to the entire method due to the thermal instability of the reagents required for sample preparation. To achieve this, new development, optimization, and validations were undertaken. Key considerations included enzyme concentration, buffer compatibility, and equal or enhanced sensitivity and specificity. At each step, thorough side-by-side comparisons with the current ISS method were performed. The development of a robust shelf-stable method will ensure continued sequencing-based microbial monitoring for Artemis and beyond, providing data in near real-time, enhancing risk response time, and yielding clear insight into the microbiome of spacecraft.
Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.
Multispectral photography,infrared imagery, image enhancement, and oceanographic, radiometric, and meteorological data were used in the study of temporal estuarine flow dynamics, nearshore circulation, and the resulting dispersal of suspended and dissolved substances introduced from the continent. Repetitive multispectral photography, IR imagery, total radiance and irradiance, water surface temperatures, salinity, total suspended solids, visibility, current velocity, winds, dye implants, and high contrast image enhancement were used to observe and describe water mass boundaries in the nearshore zone and to attempt to establish on what repetitive scale these coastal features should be observed to better understand their behavior. Water mass variability patterns, seen naturally and with the use of dyes, along the North Carolina coast and in the Chesapeake Bay are being studied as synoptic data on the basic dynamics of circulation, flushing, and mixing in coastal waters.
The Integrated Resource Inventory for Southcentral Alaska (INTRISCA) Project comprised an integrated set of activities related to the land use planning and resource management requirements of the participating agencies within the southcentral region of Alaska. One subproject involved generating a region-wide land cover inventory of use to all participating agencies. Toward this end, participants first obtained a broad overview of the entire region and identified reasonable expectations of a LANDSAT-based land cover inventory through evaluation of an earlier classification generated during the Alaska Water Level B Study. Classification of more recent LANDSAT data was then undertaken by INTRISCA participants. The latter classification produced a land cover data set that was more specifically related to individual agency needs, concurrently providing a comprehensive training experience for Alaska agency personnel. Other subprojects employed multi-level analysis techniques ranging from refinement of the region-wide classification and photointerpretation, to digital edge enhancement and integration of land cover data into a geographic information system (GIS).
Zika virus (ZIKV) and dengue virus (DENV) are arthropod-borne pathogenic flaviviruses that co-circulate in many countries. To understand some of the pressures that influence ZIKV evolution, we mimic the natural transmission cycle by repeating serial passaging of ZIKV through cultured mosquito cells and either DENV-naive or DENV-immune mice. Compared with wild-type ZIKV, the strains passaged under both conditions exhibit increased pathogenesis in DENV-immune mice. Application of reverse genetics identifies an isoleucine-to-valine mutation (I39V) in the NS2B proteins of both passaged strains that confers enhanced fitness and escape from pre-existing DENV immunity. Introduction of I39V or I39T, a naturally occurring homologous mutation detected in recent ZIKV isolates, increases the replication of wild-type ZIKV in human neuronal precursor cells and laboratory-raised mosquitoes. Our data indicate that ZIKV strains with enhanced transmissibility and pathogenicity can emerge in DENV-naive or -immune settings, and that NS2B-I39 mutants may represent ZIKV variants of interest.