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At least 397 records · Page 22

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NASA NTRS2017

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

D↗

NASA NTRS2014

The EDSN Intersatellite Communications Architecture

The Edison Demonstration of Smallsat Networks (EDSN) is a swarm of eight 1.5U Cubesats developed by the NASA Ames Research Center under the Small Spacecraft Technology Program (SSTP) within NASA Space Technology Mission Directorate (STMD). EDSN, scheduled for launch in late 2014, is designed to explore the use of small spacecraft networks to make synchronized, multipoint scientific measurements, and to organize and pass those data to the ground through their network. Networked swarms of these small spacecraft will open new horizons in astronomy, Earth observations and solar physics. Their range of applications include the formation of synthetic aperture radars for Earth sensing systems, large aperture observatories for next generation telescopes and the collection of spatially distributed measurements of time varying systems, probing the Earth's magnetosphere, Earth-Sun interactions and the Earth's geopotential. The EDSN communications network is maintained and operated by a simple set of predefined rules operating independently on all eight spacecraft without direction from ground based systems. One spacecraft serves as a central node, requesting and collecting data from the other seven spacecraft, organizing the data and passing it to a ground station at regular intervals. The central node is rotated among the spacecraft on a regular basis, providing robustness against the failure of a single spacecraft. This paper describes the communication architecture of the EDSN network and its operation with small spacecraft of limited electrical power, computing power and communication range. Furthermore, the problems of collecting and prioritizing data through a system that has data throughput bottlenecks are addressed. Finally, future network enhancements that can be built on top of the current EDSN hardware are discussed.

Communications↗

NASA NTRS1992

Mars planetary geodesy using earth-based observations of Mars landers

The potential for earth-based radiometric observations of a network of Mars surface landers to provide accurate determination of the Mars rotational orientation in inertial space is investigated. An error budget is presented for the carrier phase data type and related to system requirements for the surface landers. Differencing the carrier phase observations for a pair of Mars landers can provide extremely high precision due to common-mode error cancellation. Results of a covariance analysis are presented which show that Mars orientation can be determined to better than 10 milliarcsec, corresponding to decimeter distances at the planet surface. Recommendations on how to incorporate these concepts into future Mars missions, such as the Mars Environmental Survey, are discussed.

Edwards, C. D., Jr.↗

DOE OSTI2021

Learning viscoelasticity models from indirect data using deep neural networks

In this study, we propose a novel approach to model viscoelasticity materials, where rate-dependent and non-linear constitutive relationships are approximated with deep neural networks. We assume that inputs and outputs of the neural networks are not directly observable, and therefore common training techniques with input–output pairs for the neural networks are inapplicable. To that end, we develop a novel computational approach to both calibrate parametric and learn neural-network-based constitutive relations of viscoelasticity materials from indirect displacement data in the context of multiple-physics systems. We show that limited displacement data holds sufficient information to quantify the viscoelasticity behavior. We formulate the inverse computation – modeling viscoelasticity properties from observed displacement data – as a PDE-constrained optimization problem and minimize the error functional using a gradient-based optimization method. The gradients are computed by a combination of automatic differentiation and implicit function differentiation rules. The effectiveness of our method is demonstrated through numerous benchmark problems in geomechanics and porous media transport.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Estimation Matrix Calibration of PMU Data-driven State Estimation Using Neural Network

Linear state estimation (LSE) is a phasor measurement unit (PMU) data-based power system state estimation that incorporates a linear measurement model in rectangular coordinates. Due to the high computational efficiency and high observational time-resolution, LSE can act as a supplementary state estimation in a wide-area monitoring system (WAMS). The performance of LSE is relatively sensitive to noises in measurements. Therefore, the estimation accuracy relies heavily on the accuracy of the estimation matrix, which is directly influenced by the measurement weight matrix. This paper proposes two novel calibration method of the estimation matrix using neural networks. One is based on the minimum absolute network loss (ANL), and the other is based on the minimum average squared network loss (ASNL). Both methods are tested and compared with LSE algorithms on the IEEE 14-bus system

neural network↗

NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗

NASA NTRS1971

Applications of laser ranging and VLBI observations for selenodetic control

The observation equations necessary to utilize lunar laser ranging and very long baseline interferometry measurements were developed for the establishment of a primary control network on the moon. The network consists of coordinates of moon points in the selenodetic Cartesian coordinate system, which is fixed to the lunar body, oriented along the three principal axes of inertia of the moon, and centered at the lunar center of mass. The observation equations derived are based on a general model in which the unknown parameters included: the selenodetic Cartesian coordinates, the geocentric coordinates of earth stations, parameters of the orientation of the selenodetic coordinate system with respect to a fixed celestial system, the parameters of the orientation of the average terrestrial coordinate system with respect to a fixed celestial coordinate system, and the geocentric coordinates of the center of mass of the moon, given by a lunar ephemeris.

Fajemirokun, F. A.↗

DOE OSTI2023

A Scalable PDC Placement Technique for Fast and Resilient Monitoring of Large Power Grids

The wide-area measurement system (WAMS) is a key enabler of real-time monitoring of power grids. The essential goals of WAMS design are fast and resilient data transfer from phasor measurement units (PMU) to phasor data concentrators (PDC). We propose a scalable two-stage PDC placement technique for minimizing the end-to-end delay while maintaining resiliency. In the prescreening stage, the plausible candidates of PDC configurations are identified based on a graph theory-based multi-median function (MMF). Here, in this article, a computationally efficient meta-heuristic algorithm is used to address scalability. In the candidate selection stage, two different algorithms, namely, Suurballe's and Dijkstra's, are employed to identify the best of those plausible PDC configurations as the final design. This technique not only minimizes the hop paths between PMUs and PDCs, but also ensures network resiliency against single PMU, PDC, or communication link failure by incorporating the roles of PMUs in power grid observability into routing policy. Simulation results on the IEEE 57-bus test power system and the 2000-bus test power system demonstrate the effectiveness and scalability of the proposed technique.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

An Update on the Hydrological Land Surface Data and Services at NASA GES DISC

"The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth Observing System data centers that document, process, archive and distribute data from Earth science missions and related projects. The GES DISC hosts many hydrological land surface data products including North American Land Data Assimilation System (NLDAS), Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and assimilated-Gravity Recovery and Climate Experiment (GRACE). The Land Surface Model (LSM) products contain model output, including heat fluxes, rain, snow, soil temperature, soil moisture and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at the GES DISC ranges from hourly to monthly and the spatial resolution 0.1 degree to 1.0 degree. The variability of Earth’s water and energy cycles can be characterized with these spatially and temporally consistent, and quality controlled hydrological land surface data. These data could support ongoing modeling activities, and help improve our understanding of land-surface-atmosphere interactions and their impact on climate. Over the years, new data is added, and older data is reprocessed and updated. This presentation provides a summary table of the hydrological data and discusses recent updates to the data and data services at the GES DISC. New data products include (1) GRACE Global Version 3.0 drought indicators and (2) FLDAS monthly global and Central Asia daily data. Reprocessed and updated products include (1) NLDAS Version 2.0 and (2) GRACE United States Version 4.0 drought indicators. Lastly, this presentation will discuss the opportunities for accessing some of our hydrology data from the cloud. As the hydrology land surface data is moved to the cloud, users can expect faster service for longer time series, notably the hydrology data rods time series service."

Ashley Heath↗

DOE OSTI2020

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning for Reliable and Sustainable Power Systems. Final Report

In this project, Clarkson University, in collaboration with Southern Methodist University and University of Pittsburgh, conducted the research to model, design, and implement a sophisticated generation and transmission co-optimization planning decision tool, called Multi-stage and Multi-timescale robust Co-Optimization Planning (MMCOP). The MMCOP decision tool intends to facilitate generation and transmission co-optimization planning of emerging power systems, while mitigating risks and uncertainties in both short-term operation dynamics and long-term policy and technology changes. Long-term power system planning aims at optimizing asset utilization by investing in a proper mix of various generation technologies and transmission lines to supply the future load growth. In particular, the Clean Power Plan (CPP), which is designed to combat climate change and reduce carbon emissions by setting a national limit on carbon pollution from power plants, may dramatically change the landscape of the power industry by further promoting clean energy and phasing out emissions-intensive generation technologies. In addition, novel non-wire alternatives (e.g., demand response (DR), distributed generation (DG), energy efficiency (EE), and smart grid technologies) and the computational complexity for large-scale systems significantly complicate the system planning procedure even further. However, existing conventional planning approaches neglect short-term variability and uncertainty of renewable energy, hourly chronological operation details, and physical nonlinear characteristics of the alternating current transmission network. As a result, existing conventional planning approaches may not work properly, and power systems reliability could be in jeopardy. In observing the limitations of existing conventional planning approaches and addressing new challenges of emerging power systems, the main scope of this project to develop co-optimization planning models within a multi-stage and multi-timescale framework. In particular, random contingencies, key uncertainty factors, and AC power flows are included to derive expansion plans while considering both long-term reliability and short-term flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Predicting Cislunar Orbit Lifetimes from Initial Orbital Elements

The volume of space between Earth’s geosynchronous orbit out to the Moon’s sphere of influence, including the lunar Lagrange points, is crucial for the successful planning and execution of space missions, but not fully understood dynamically. This region is a part of cislunar space. Trajectories through cislunar space are influenced by the gravitational forces of the Sun, Earth, Moon, and other Solar System planets leading to typically unpredictable and chaotic trajectory behavior. It is therefore difficult to predict the stability of an trajectory through cislunar space from a set of initial conditions or orbital elements. We simulate one million cislunar orbits to train a self-organizing map (SOM) to cluster the trajectories and orbits into families based on how long they remain stable within the cislunar space. Using the trained SOM, we are able to predict the stable lifetime of a trajectory through cislunar space from a set of initial orbital elements to within an accuracy of 10% for 8% of simulated trajectories and within 50% for 43% of the simulated trajectories. Clustering in the SOM suggests that a variety of trajectory morphologies have similar lifetimes. Once trained, the SOM can predict the stable lifetime of a given cislunar trajectory within milliseconds. The methods developed in this work enable the rapid identification of stable cislunar orbits and trajectories that could be used for future space exploration. Moreover, the developed SOM method can generate orbital and trajectory lifetime estimates from minimal observational data, such as a single two line element, making it useful for early warning systems and large-scale sensor network operations.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2008

Global Test Range: Toward Airborne Sensor Webs

This viewgraph presentation reviews the planned global sensor network that will monitor the Earth's climate, and resources using airborne sensor systems. The vision is an intelligent, affordable Earth Observation System. Global Test Range is a lab developing trustworthy services for airborne instruments - a specialized Internet Service Provider. There is discussion of several current and planned missions.

Mace, Thomas H.↗

NASA NTRS2014

A Spatially Coherent Global Soil Moisture Product with Improved Temporal Resolution

Global soil moisture products that are completely independent of any type of ancillary data and solely rely on satellite observations are presented. Additionally, we further develop an existing downscaling technique that enhances the spatial resolution of such products to approximately 11 km. These products are based on internal modules of the Land Parameter Retrieval Model (LPRM), an algorithm that uses the radiative transfer equation to link soil moisture, vegetation optical depth and land surface temperature to observed brightness temperatures. The soil moisture product that is independent of any type of ancillary data uses the internally calculated dielectric constant as a soil moisture proxy. This data product is not influenced by errors associated with coarse-scale global soil property maps or by any other type of forcing (e.g. re-analysis) data and is therefore solely based on satellite microwave observations. The second step builds upon recent developments to increase the spatial resolution of the LPRM retrievals using a smoothing filter downscaling method. With this method we can attain a spatial resolution that can be more useful at the scale of local and regional hydrological studies as well. The steps presented in this paper were applied to observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The newly derived data sets were validated using ground-based observations from the International Soil Moisture Network (ISMN). The internally calculated dielectric constant product results in significantly more days with valid retrievals than the original soil moisture data products, in particular over arid regions. The dielectric constant product resulted in similar correlations with in situ data as the original soil moisture data product. Together, these findings demonstrate the usefulness of this new dielectric constant product for the hydrological modeling community and climate studies. A case study on the Australian Fitzroy catchment demonstrated that the downscaled data product has a more detailed spatial description of soil moisture, especially during wet and dry conditions with more pronounced dry and wet regions within the catchment. The increased resolution data products could therefore improve runoff predictions and this study demonstrated the potential added value of a transitioning from a spatial resolution of 56 km toward a higher resolution of 11 km. The hydrological implications of these newly developed data records are not only linked to AMSR-E satellite data, but also to the next generation Soil Moisture Active and Passive (SMAP) mission where a 9 km spatial resolution is the target resolution for satellite soil moisture products. The new data products will not replace the current LPRM products, but will be added to the existing array of data products and will become publicly available through our data portals.

spatial↗

NASA NTRS2020

Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS) and Other Assimilated Hydrological Data at NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides science support for several data sets relevant to agriculture and food security, including the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS), or FLDAS data set. The GES DISC is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remote sensing and model data, and provides reliable and robust data access and other services to users worldwide. Beyond data archive and access, the GES DISC offers many services to visualize and analyze the data. This presentation provides a summary of the hydrological data available at the GES DISC, along with an overview of related data services. Specifically, the FLDAS data set has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The FLDAS global monthly data have a 0.1 x 0.1 degree spatial resolution covering the period from January 1982 to present. Global FLDAS monthly anomaly and monthly climatology data are also available at the GES DISC to evaluate how current conditions compare to averages over the FLDAS 35-year period. Several case studies using the FLDAS soil moisture, evapotranspiration, rainfall, runoff, and surface temperature data will be presented.

Loeser, Carlee↗

NASA NTRS2020

High-Resolution Mapping of SO2 Using Airborne Observations from the GeoTASO Instrument During the KORUS-AQ Field Study: PCA-Based Vertical Column Retrievals

The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument is an airborne hyperspectral spectrometer measuring backscattered solar radiation in the ultraviolet (290–400 nm) and visible (415–695 nm) wavelength regions. This paper presents high-resolution sulfur dioxide (SO2) maps over the Korean Peninsula, produced by SO2 retrievals from GeoTASO measurements during the Korea–United States Air Quality Field Study (KORUS-AQ) from May to June 2016. The highly sensitive GeoTASO instrument with a spatial resolution of ~250 m × 250 m can detect point emission sources of SO2 within its fields of view, even without merging multiple overlapping observations. To retrieve SO2 vertical columns from the GeoTASO measurements, we apply an algorithm based on principal component analysis (PCA), which is effective in suppressing noise and biases in SO2 retrievals. The retrievals successfully capture SO2 plumes and various point sources such as power plants, a petrochemical complex, and a steel mill, located in South Chungcheong Province, some of which are not detected by a ground-based in situ measurement network. Spatial distributions of SO2 from GeoTASO observations in source areas are consistent with those from the Stack Tele-Monitoring System reports and airborne in situ SO2 measurements. Comparisons of SO2 retrievals from GeoTASO and existing satellite sensors demonstrate the significance of high-resolution SO2 observations, by indicating that GeoTASO detects small SO2 emission sources that are not precisely resolved by single overpasses of satellites. To assess future geostationary SO2 observations having a higher spatial resolution, we upscale the GeoTASO SO2 retrievals to a spatial resolution of the Geostationary Environment Monitoring Spectrometer (GEMS). Since the upscaled GeoTASO retrievals also detect SO2 plumes clearly, we expect from GEMS to identify even small SO2 emission sources over Asia.

Chong, Heesung↗

NASA NTRSDate not supplied

Measuring Upwelling Longwave in the Presence of an Obstruction

One of the key measurements from the Clouds and the Earth’s Radiant Energy System (CERES) satellite is Earth emitted or longwave (LW) radiation. The Baseline Surface Radiation Network (BSRN) aims to provide upwelling LW observations of the best possible quality across all their measurement sites. The disestablished CERES Ocean Validation Experiment (COVE), located at Chesapeake Light Station, approximately 25 kilometers east of Virginia Beach, Virginia (coordinates: 36.90N, 75.71W) was a validation site for CERES measurements and part of the BSRN network. One of the measurements at COVE was upwelling LW radiation made with an Eppley pyrgeometer. However, this measurement was complicated due to the Light Station tower being in its field of view. According to our estimates, the Light Station tower altered 15% of the upwelling LW radiation. To resolve this issue, we obtain a different upwelling longwave value using data from an infrared radiation thermometer (IRT), a pyrgeometer that measures downwelling longwave, and meteorological data. Using an IRT allows conversion from sea surface temperature to a water emission value, the downwelling pyrgeometer provides the reflected flux of the downward longwave radiation, and the meteorological data accounts for the air affects between the ocean surface and the upwelling LW measurement height. Comparing the upwelling LW pyrgeometer value with the newly derived value shows the unwanted consequence of the tower. The tower appears to enhance the upwelling LW signal during the summer, most obvious on a summer clear day, but depresses the upwelling signal, even more, during winter sky conditions (both clear and overcast). The tower affects the upwelling LW measurement in these scenarios up to 5% (20 W/m^2) when compared to the newly derived value. BSRN target uncertainty is 2%. Installing a pyrgeometer to measure upwelling LW without obstructions may not be possible at some measurement sties. This issue can be mitigated using other measurements to derive an upwelling LW value.

Bryan Fabbri↗

NASA NTRS1991

Observation and numerical simulation of a convective initiation during COHMEX

Under a synoptically undisturbed condition, a dual-peak convective lifecycle was observed with the COoperative Huntsville Meteorological EXperiment (COHMEX) observational network over a 24-hour period. The lifecycle included a multicell storm, which lasted about 6 hours, produced a peak rainrate exceeding 100 mm/hr, and initiated a downstream mesoscale convective system. The 24-hour accumulated rainfall of this event was the largest during the entire COHMEX. The downstream mesoscale convective system, unfortunately, was difficult to investigate quantitatively due to the lack of mesoscale observations. The dataset collected near the time of the multicell storm evolution, including its initiation, was one of the best datasets of COHMEX. In this study, the initiation of this multicell storm is chosen as the target of the numerical simulations.

Song, J. Aaron↗

DOE OSTI2021

Shear Stress Dependence of Force Networks in 3D Dense Suspensions

The geometric organization and force networks of 3D dense suspensions that exhibit both shear thinning and thickening have been examined as a function of varying strength of interparticle attractive interactions using lubrication flow discrete element simulations. Significant rearrangement of the geometric topology does not occur at either the local or global scale as these systems transition across the shear thinning and shear thickening regimes. In contrast massive rearrangements in the balance of attractive, lubrication, and contact forces are observed with interesting behavior of network growth and competition. In agreement with prior work, in shear thinning regions the attractive force is dominant, however as the shear thickening region is approached there is growth of lubrication forces. Lubrication forces oppose the attraction forces, but as viscosity continues to increase under increasing shear stress, the lubrication forces are dominated by contact forces that also resist attraction. Contact forces are the dominant interactions during shear thickening and are an order of magnitude higher than their values in the shear-thinning regime. At high attractive interaction strength, contact networks can form even under shear thinning conditions, however high shear stress is still required before contact networks become the driving mechanism of shear thickening. Analysis of the contact force network during shear thickening generally indicate a uniformly spreading network that rapidly forms across empty domains, however the growth patterns have structure that are significantly dependent upon the strength of interparticle interactions, indicating subtle variations in the mechanism of shear thickening.

Edens, Lance E.↗