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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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ErrFilter is a tool that allows an analyst to explore data by defining simple Boolean filters on data. The tool also allows the user to sort the results by any attribute. The user sees both the distribution of filtered data as well as selected examples. The tool was designed to be applied generally to multivariate data associated with machine learning models.
This report describes an intercomparison of three popular broadband clear sky solar irradiance model results with measured data, as well as satellite-based model clear sky results compared to measured clear sky data. The authors conclude that one of the popular clear sky models (the Bird clear sky model developed by Richard Bird and Roland Hulstrom) could serve as a more accurate replacement for current satellite-model clear sky estimations. Additionally, the analysis of the model results with respect to model input parameters indicates that rather than climatological, annual, or monthly mean input data, higher-time-resolution input parameters improve the general clear sky model performance.
A self-consistent two-gap γ -model is used to quantitatively describe several thermodynamic properties of MgB 2 superconductor. The superconducting coupling matrix, ν i j , was obtained from the fitting of the superfluid density in the entire superconducting temperature range. Using this input, temperature-dependent superconducting gaps, specific heat, and upper critical fields were calculated with no adjustable parameters and compared with the experimental data as well as with the first-principles calculations. The observed agreement between fit and data shows that γ -model provides adequate quantitative description of the two-gap superconductivity in MgB 2 and may serve as a relatively simple and versatile self-consistent description of the thermodynamic quantities in multi-gap superconductors.
The observation and interpretation of weak EPR transitions, identified as 'forbidden' transitions, establish the existence of a new type of quadrupole interaction for cubic-symmetry imperfections. This interaction is simply a consequence of the ground-vibronic-state degeneracy. The signs as well as the magnitudes of the quadrupole-coupling coefficients are determined experimentally. These data agree well with the predictions of crystal field theory modified to account for a weak-to-moderate vibronic interaction (i.e., a dynamic Jahn-Teller effect).
A unique approach has been developed for stability and control derivative testing of the Space Shuttle orbiter during entry. Shuttle Program requirements have necessitated a minimum of testing. Therefore, flight tests concentrate on potential control problem areas predicted from wind tunnel data as well as anomalies discovered during flight testing. The object is to use the test data to remove center of gravity (cg), angle of attack, and elevon placards which are a function of these potential control problems. To ensure successful aerodynamic extraction from a minimum of testing, the following special measures have been taken. Exact maneuvers are designed preflight on Shuttle simulators. These maneuvers are duplicated and implemented during the flight by onboard software. An onboard instrumentation system was designed especially for aerodynamic parameter identification. State-of-the-art techniques are used in extracting aerodynamics.
It is immediately apparent from the examination of almost any synthetic aperture radar (SAR) data that the radar return is primarily a function of the topographic relief. Yet radar reflectance is dependent on both surface roughness and the dielectric constant of the surface material. These two parameters can in many cases be related to lithologic units. Thus, if the first-order terrain effects due to topographic relief could (in essence) be removed from the radar image, the SAR data might well be used for lithologic discrimination. Such an approach is evaluated. Landsat Thematic Mapper data along with ground truth are used to define training cells that characterize the various lithologic units in the area. By aggregating the radar data for these cells, curves of radar reflectance versus local incidence are estimated. The curves are then used to classify the test area. The results are compared with lithologic classification based upon multi-spectral (visible and infrared) data.
A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm.
During the 1998-1999 Antarctic field season, we installed three autonomous GPS stations in Marie Byrd Land, West Antarctica to measure glacio-isostatic rebound and rates of spreading across the West Antarctic Rift System. The systems collect data throughout the entire year and therefore must function during the warm, relatively mild summer, and cold, harsh winters. They are powered by gel cell batteries that are charged by wind and solar power. The system includes dual data logging capability. We log data at 5 minute intervals within the receiver and at 30 second intervals to a serial data logger. We do not require 365 days of continuous data for well determined crustal velocities, but rather long periods (greater than 24 hours) of continuous data distributed throughout the year. Therefore, for simplicity, we designed the system to accept occasional data interruptions. The batteries, in addition to supplying power, act as a thermal capacitive heat storage device to help regulate the temperatures within the system. This storage system absorbs the majority of the 10-15 watts of power from the receiver and 5 watts from the data logger which helps to maintain temperature for long periods of time. Power is switched off when the temperature within the system enclosure reaches 50'C and is reconnected at 20 C. If battery voltage drops too low the batteries will freeze. Therefore, we cut the power off when the batteries drop to a low voltage of 12.45V. Power is restored at 13.2V. The temperature and power hysteresis allows for a minimum of several days of data to be collected before system shutdowns. A check of all three stations in late January 1999 indicated that the thermal and power control systems are performing as expected. We plan to implement satellite telemetry to the systems during the 2000-2001 season following a year of development.
A recent microstructure-based FEM model that couples crystal-based plasticity, the B2<-> MB190 phase transformation and anisotropic elasticity at the grain scale is calibrated to recent data for polycrystalline NiTi (49.9 at.% Ni). Inputs include anisotropic elastic properties, texture and differential scanning calorimetry data, as well as a subset of recent isothermal deformation and load-biased thermal cycling data. The model is assessed against additional experimental data. Several experimental trends are captured - in particular, the transformation strain during thermal cycling monotonically increases and reaches a peak with increasing bias stress. This is achieved, in part, by modifying the martensite hardening matrix proposed by Patoor et al. [Patoor E, Eberhardt A, Berveiller M. J Phys IV 1996;6:277]. Some experimental trends are underestimated - in particular, the ratcheting of macrostrain during thermal cycling. This may reflect a model limitation that transformation-plasticity coupling is captured on a coarse (grain) scale but not on a fine (martensitic plate) scale.
Nuclear and solar thermal propulsion offer enhanced efficiencies for in-space travel which may open up the outer Solar System to both crewed and automated craft. However, these advanced propulsion systems require cutting-edge materials which are often not easily tested in the laboratory. Ab initio thermodynamics and other computational methods offer a way to test materials at high pressures and temperatures more quickly, and with reduced cost. Here, several case studies are presented which highlight the utility of modern simulation techniques. First, an extensive thermodynamic study was performed at the ab initio level to elucidate the role of hot hydrogen flow on the erosion of four refractory carbides to examine their appropriateness for use as channel coatings. A detailed analysis of reaction products informs estimates of erosion which are validated in comparison with heritage NERVA data. Specific material suggestions are made for both nuclear and solar designs. Secondly, we examined the mechanical behavior and chemical stability of various components of an idealized nuclear cermet core design using techniques spanning from the atomic to microscale. Next, the mechanical and chemical behavior of tungsten (a potential matrix material) and the stability of uranium mononitride (a potential ceramic fuel element), are characterized in the presence of hydrogen and at elevated temperatures with ab initio thermodynamics. Finally, the mechanical response and failure of tungsten under tensile strain is simulated at the micron scale with a combined finite element and dislocation dynamics approach. The resultant stress-strain data agrees well with experiments and leads to a workflow which can incorporate ab initio thermodynamic data into micron-scale simulations and thus provide real-world relevant estimates of erosion and stress-strain behavior. The power of this framework and our plans to use it in the future will be discussed.
Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from spaceflight biological and health studies are increasingly being made findable, accessible, interoperable, and reusable for the scientific public. These data, as well as space science-relevant biospecimens, are available through NASA’s Open Science Data Repository (OSDR), which is the new umbrella grouping of NASA GeneLab, the Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection (NBISC). The quality of data is underpinned by datasets having rich metadata (determined through Analysis Working Group members), processing pipelines to enable data reuse standards, and ontologies specifying terminology semantics (e.g., the Radiation Biology Ontology).
Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, elevated CO2, and many other abiotic stressors. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. Current OSDR standards include the ISA (Investigation-Study-Assay) experiment model, assay metadata configurations, and standardized terminology and ontologies. In 2024 OSDR will include a new suite of features for improved FAIR compliance including downloadable plant metadata templates, data submission tools and overall improved AI-readiness of plant datasets. AI/ML methods can be helpful tools to overcome the inherent challenges of space biology research (small sample size, sparse and heterogeneous data etc.). However these methods are built on an assumption of normalized and well-curated data. OSDR’s new curation tools will improve users ability to leverage ML and AI methods to model space biology data and better understand the complex effects of spaceflight on living systems across hierarchical biological levels. We look forward to sharing our advances with the spaceflight community.
Domi (Greek for "structure") is a Trilinos package that provides support for multi-dimensional data arrays distributed over multiple processors of a high performance computing system. The software supports data arrays of arbitrary number of dimensions mapped to a multi-dimensional grid of processors. Domi provides local access to the data, as well as the ability to compute global quantities such as norms, maxima and minima. Data arrays can be constructed with boundary and communication padding, necessary for finite difference algorithms, and provides high-level methods for communicating data between processors to update these buffers. The distributed maps and vectors in Domi can be converted to the equivalent data structures in Epetra and Tpetra (both of which assume unstructured data), using view semantics when appropriate, for use with other Trilinos solvers.
This is a comprehensive collection of all original surface and subsurface technical data, as well as various derivative subsurface models, collected from the FutureGen 2.0 project. This collection of data has been vetted for confidential or sensitive documents.
Introduction: The Venus Global Reference Atmospheric Model (Venus-GRAM) is one of the most widely used engineering models of Venus’ atmosphere. The Venus-GRAM upgrades are being developed by NASA Marshall Space Flight Center (MSFC) and NASA Langley Research Center (LaRC). This presentation will provide details regarding the upgrades that have been made to Venus-GRAM and the ongoing objectives, tasks, and milestones related to the GRAM upgrades funded by the NASA Science Mission Directorate (SMD). Venus-GRAM: Venus-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of the atmospheric properties of Venus. Venus-GRAM provides mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. Venus-GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path. It is extensively used by the engineering community because of its ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. GRAMs are currently available for Earth, Mars, Venus, Neptune, Titan, and Uranus. The lower atmosphere model in Venus-GRAM (up to 250 km) is based on the Venus International Reference Atmosphere (VIRA) [1]. The Venus-GRAM thermosphere (250 to 1000 km) is based on a MSFC-developed model [2] which assumes an isothermal temperature profile initialized using VIRA conditions at 250 km [3]. The VIRA version included in Venus-GRAM includes Pioneer Venus Orbiter and Probe data as well as Venera probe data, but it does not include a solid planet model or a high-resolution gravity model [4]. Venus-GRAM Upgrade Status: Code Modernization. Venus-GRAM has been rearchitected from Fortran to a common object-oriented C++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ release of the rearchitected Venus-GRAM is a straight conversion from the latest Fortran version. Model Upgrades. The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemeris has been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. In FY20, the GRAM project established contracts to improve the model data within Venus-GRAM. Hampton University is developing an empirical global model for Venus. The University of Wisconsin is reanalyzing the Venus Express radio occultation observations and analyzing the Akatsuki thermal imaging data. Upgraded Venus-GRAM Release. GRAM Suite Version 1.3 will be released in September 2021 and will contain the rearchitected Venus-GRAM, including the common GRAM framework and planet–specific code. A User Guide and Programmer’s Manual are released with all GRAMs. Conclusions: GRAMs are frequently used toolsets and vital in assessing effects of atmospheres on interplanetary spacecraft during the program life cycle process. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. Venus-GRAM atmosphere model upgrades will be included in the next phase of GRAM tasks. References: [1] Kliore, A. J. et al. (1985) ASR, 5, 11, 1-304. [2] Justh, H. L. et al. (2006) AIAA/AAS Astrodynamics Specialist Conference & Exhibit, Abstract AIAA-2006-6394. [3] Guide to Reference and Standard Atmosphere Models, BSR/AIAA G-003-2010. [4] Limaye, S. S. (2012), LPSC VEXAG Townhall Meeting. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD.
Anomalous behavior poses serious risks to assured performance and reliability of complex, high-consequence systems. For spaceborne assets and their state-of-health (SOH) telemetry, the challenges of high-dimensional data of varying data types are compounded by computational limitations from size, weight, and power (SWaP) constraints as well as data availability. Automated anomaly detection methods tend to perform poorly under these constraints, while current operational approaches can introduce delays in response time due to the manual, retrospective processes for understanding system failures. As a result, presently deployed space systems, and those deployed in the near future, face situations where mission operations might be delayed or only be able to operate under degraded capabilities. Here, we examine a near-term lightweight solution that provides real-time detection capabilities for rare events and assess state-of-the-art anomaly detection techniques against real SOH telemetry from space platforms. This report describes our methodology and research, which could support more automated capabilities for comprehensive space operations as well as for other resource-constrained edge applications.
Highlights: • Heat health-related Google search term frequency increased during heatwave events. • Google data correlated well with validated syndromic heat health indicators. • A similar timeliness was seen between daily Google and syndromic data in England. • Google search data could be of benefit in monitoring heat-related morbidity. One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684–0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat.
It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.