Search NASA⌕ Search

SEARCH · Search NASA

Results for “temporal network”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements

Eddy covariance data from regional flux networks are direct in situ measurement of carbon, water, and energy fluxes and are of vital importance for understanding the spatio-temporal dynamics of the the global carbon cycle. FLUXNET links regional networks of eddy covariance sites across the globe to quantify the spatial and temporal variability of fluxes at regional to global scales and to detect emergent ecosystem properties. This study presents an assessment of the representativeness of FLUXNET based on the recently released FLUXNET2015 data set. We present a detailed high resolution analysis of the evolving representativeness of FLUXNET through time. Results provide quantitative insights into the extent that various biomes are sampled by the network of networks, the role of the spatial distribution of the sites on the network scale representativeness at any given time, and how that representativeness has changed through time due to changing operational status and data availability at sites in the network. To realize the full potential of FLUXNET observations for understanding emergent ecosystem properties at regional and global scales, we present an approach for upscaling eddy covariance measurements. Informed by the representativeness of observations at the flux sites in the network, the upscaled data reflects the spatio-temporal dynamics of the carbon cycle captured by the in situ measurements. In conclusion, this study presents a method for optimal use of the rich point measurements from FLUXNET to derive an understanding of upscaled carbon fluxes, which can be routinely updated as new data become available, and direct network expansion by identifying regions poorly sampled by the current network.

58 GEOSCIENCES↗
DOE OSTI2018

Mapping ecoregions under climate change: a case study from the biological ‘crossroads’ of three continents, Turkey

Context: Besides climate change vulnerability, most ecosystems are under threat from a history of improper land-use and conservation policies, yet there is little existing long-term ecological research infrastructure in Turkey. In regions with no ecological networks across large landscapes, ecoregion concept offers opportunities for characterizing the landscape under changing climate.Objectives: Aim is to develop contemporary and future quantitative ecoregions for Turkey based on climate model outputs, to identify climate change-sensitive areas of biodiversity and conservation significance, and to provide a framework for a comprehensive ecological observatory network design.Methods: Using Multivariate Spatio-Temporal Clustering and climate data contemporary and projected future distributions of Turkey’s ecoregions are delineated at several division levels.Results: Turkey’s contemporary ecoregions generally show a northward shift by the end of this century and the lengthening of the growing season across the country, especially eastward and northward. The increase in growing season length, along with the shift in precipitation seasonality and increasing growing season precipitation, shape future conditions within the climate change-sensitive areas. Apart from transboundary ecological and socioeconomic significance, these potentially vulnerable ecosystems also constitute the majority of Turkey’s biodiversity hotspots.Conclusions: Our study marks the first ‘ecoregionalization’ study for Turkey based on both contemporary and future climate scenarios. For countries like Turkey, where large-scale ecological networks have not been established, using such quantitative methodology for delineation of optimal ecoclimatic regions, and for mapping environments at risk from climate change provides an invaluable perspective for conservation planning strategies, and a framework for a comprehensive ecological observatory network design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Connecting Network Properties of Rapidly Disseminating Epizoonotics

Background: To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. Methods: Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) ‘connectivity’, a model that integrated bio-physical concepts (the agent’s transmission cycle, road topology) into indicators designed to measure networks (‘nodes’ or infected sites with short- and long-range links), and 2) ‘contacts’, which focused on infected individuals but did not assess connectivity. Results: The connectivity model showed five network properties: 1) spatial aggregation of cases (disease clusters), 2) links among similar ‘nodes’ (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a ‘‘20:800 pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. Conclusions: Geo-temporal constructs of Network Theory’s nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2011

Ground-Based Network and Supersite Observations to Complement and Enrich EOS Research

Since 1997 NASA has been successfully launching a series of satellites - the Earth Observing System (EOS) - to intensively study, and gain a better understanding of, the Earth as an integrated system. Space-borne remote sensing observations, however, are often plagued by contamination of surface signatures. Thus, ground-based in-situ and remote-sensing measurements, where signals come directly from atmospheric constituents, the sun, and/or the Earth-atmosphere interactions, provide additional information content for comparisons that confirm quantitatively the usefulness of the integrated surface, aircraft, and satellite datasets. Through numerous participations, particularly but not limited to the EOS remote-sensing/retrieval and validation projects over the years, NASA/GSFC has developed and continuously refined ground-based networks and mobile observatories that proved to be vital in providing high temporal measurements, which complement and enrich the satellite observations. These are: the AERO NET (AErosol RObotic NETwork) a federation of ground-based globally distributed network of spectral sun-sky photometers; the MPLNET (Micro-Pulse Lidar NETwork, a similarly organized network of micro-pulse lidar systems measuring aerosol and cloud vertical structure continuously; and the SMART-COMMIT (Surface-sensing Measurements for Atmospheric Radiative Transfer - Chemical, Optical & Microphysical Measurements of In-situ Troposphere, mobile observatories, a suite of spectral radiometers and in-situ probes acquiring supersite measurements. Most MPLNET sites are collocated with those of AERONET, and both networks always support the deployment of SMART-COMMIT worldwide. These data products follow the data structure of EOS conventions: Level-0, instrument archived raw data; Level-1 (or 1.5), real-time data with no (or limited) quality assurance; Level-2, not real high temporal and spectral resolutions. In this talk, we will present NASA/GSFC groundbased facilities, serving as network or supersite observations, which have been playing key roles in major international research projects over diverse aerosol regimes to complement and enrich the EOS scientific research.

Tsay, Si-Chee↗
DOE OSTI2022

Enhanced deep neural networks with transfer learning for distribution LMP considering load and PV uncertainties

As the flexibility of generation and demand increases in distribution systems, the residential loads are emerging as a promising means to participate in demand response and the transactive energy market. Market pricing is an instrumental mechanism for the distribution system operator to exploit the full potential of the flexible resources. The distribution locational marginal price (DLMP) can be used to guide the residential load consumption. This type of market signal helps the distribution system operator to optimize the scheduling of all resources while satisfying related network constraints through a day-ahead market. However, solving the optimization problem for large-scale systems can be computationally expensive. To address the scalability and practicability limitations of the DLMP framework, a learning-based approach is proposed in this paper to complement the day-ahead distribution market framework. Here, the proposed approach combines long short-term memory and transfer learning to develop deep neural network that can capture the spatial–temporal correlation of the input data. The model can determine the optimal DLMP for each node in a distribution system without the system parameters required to formulate the optimization problem. Testing results on IEEE 33-bus and 123-bus systems show that the proposed approach can generate a comparable DLMP against the optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Large Amplitude Spatial and Temporal Gradients in Atmospheric Boundary Layer CO2 Mole Fractions Detected With a Tower-Based Network in the U.S. Upper Midwest

This study presents observations of atmospheric CO{sub 2} mole fraction from a nine-tower, regional network deployed during the North American Carbon Program's Mid-Continent Intensive during 2007-2009. Within this network in a largely agricultural area, mean atmospheric CO{sub 2} gradients were strongly correlated with both ground-based inventory data and estimates from satellite remote sensing. The average seasonal drawdown for corn-dominated sites (35 ppm) is significantly larger than has been observed at other continental boundary layer sites. Observed growing-season median CO{sub 2} gradients are strongly dependent on local flux. The gradients between cross-vegetation site-pairs, for example, average 2.0 ppm/100 km, four times larger than the similar-vegetation site-pair average. Daily-timescale gradients are as large as 5.5 ppm/100 km, but dominated by advection rather than local flux. Flooding in 2008 led to a region-wide 23 week delay in growing-season minima. The observations show that regional-scale CO{sub 2} mole fraction networks yield large, coherent signals governed largely by regional sources and sinks of CO{sub 2}.

58 GEOSCIENCES↗
DOE OSTI2018

Road Network State Estimation Using Random Forest Ensemble Learning

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for the models were low, thus useful for real-time traffic management and traveler information applications.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Modeling Framework for the Assessment of a Sustainable Hydrogen Production and Supply Chain Network in California

The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.

Zaki, Mohammed Tamim↗
NASA NTRS1981

Mesoscale aspects of jet streak coupling and implications for the short term forecasting of severe convective storms

An analysis of a tornado outbreak in Wichita Falls, Texas was analyzed. The coupling of upper and lower tropospheric jet streaks, leading to severe storm outbreaks is illustrated. The high resolution SESAME data sets indicate that mass and momentum adjustments which couple upper and lower tropospheric jets occur within a 3 to 6 hr time frame over a 100 to 500 km domain, and establish the role of isallobaric forcing in the storm development. It is suggested that the output rate of data from the existing 12 hr network be increased to provide better temporal resolution of wind, mass and moisture data.

Uccellini, L. W.↗
NASA NTRS1988

Structure of magnetic fields on the quiet sun

To obtain quantitative temporal and spatial information on the network magnetic fields, auto- and cross-correlation techniques are applied to the Big Bear videomagnetogram data. The average size of the network magnetic elements derived from the auto-correlation curve is about 5700 km. The distance between the primary and secondary peak in the auto-correlation curve is about 17,000 km, which is half of the size of the supergranule as determined from the velocity map. The canceling features and the emergence of ephemeral regions are the major sources for the loss and replenishment of magnetic flux on the quiet sun.

Wang, Haimin↗
NASA NTRSDate not supplied

Runtime Analysis with R2U2: A Tool Exhibition Report

We present R2U2 (Realizable, Responsive, Unobtrusive Unit), a hardware- supported tool and framework for the continuous monitoring of safetycritical and embedded cyber-physical systems.With the widespread advent of autonomous systems such as Unmanned Aerial Systems (UAS), satellites, rovers, and cars, real-time, on-board decision making requires unobtrusive monitoring of properties for safety, performance, security, and system health. R2U2 models combine past-time and future-time Metric Temporal Logic, “mission time” Linear Temporal Logic, probabilistic reasoning with Bayesian Networks, and modelbased prognostics. The R2U2 monitoring engine can be instantiated as a hardware solution, running on an FPGA, or as a software component. The FPGA realization enables R2U2 to monitor complex cyber-physical systems without any overhead or instrumentation of the flight software. In this tool exhibition report, we present R2U2 and demonstrate applications on system runtime monitoring, diagnostics, software health management, and security monitoring for a UAS. Our tool demonstration uses a hardware-based processor-in-the-loop “iron-bird” configuration.

Johann Martin Schumann↗
NASA NTRSDate not supplied

Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone

A recent, interdisciplinary survey of more than 40 international remote sensing experts reviewed the strengths and limitations of current space-based observational capabilities of the Arctic Boreal Zone (ABZ) and made recommendations for improving upon these current capabilities (Duncan et al., 2019). General recommendations focused on the complementarity of sub-orbital and satellite observations, the need for measurement continuity, and the importance of stakeholder engagement. The development of a comprehensive and robust sub-orbital portion of an ABZ observing network can act to fill some temporal gaps in satellite coverage, provide detail unobtainable from space, and is necessary for validation and interpretation of satellite data. A priority across all types of observations is the continuation, enhancement, and/or creation of long-term, multi-satellite, climate-quality, and self-consistent data records of ABZ components, such as surface temperature, energy fluxes, or sea ice extent and volume, for improved determination of trends. Long-term passive satellite observations currently represent the only feasible option for monitoring change of the ABZ at broad spatial scales required to address pressing science challenges. Active sensors are particularly attractive for the ABZ as they do not depend on reflected sunlight and so join passive microwave sensors in having a significant advantage over passive visible and infrared sensors in the low-light conditions that are typical of the ABZ for several months of the year. Application of consistent retrieval algorithms to multiple data sets, as well as careful characterization of satellite instruments and their temporal evolution, helps to ensure data quality and consistency. Among the needs ranked as “Most Important” are those associated with gaining a process-based understanding of the ABZ carbon cycle and hydrologic cycle (which includes sea level rise) as they have the potential to affect a large portion of Earth’s population.

Remote sensing↗
DOE OSTI2020

CSB-RNN: A Faster-Than-Realtime RNN Acceleration Framework with Compressed Structured Blocks

Recurrent Neural Networks (RNN) is widely applied to temporal sequence analysis, where real-time performance is usually in demand. However, RNN suffers a heavy computational workload as the model comes with a large weight matrix. To alleviate the pain, model compression (pruning) schemes have been proposed for RNN that pruning the redundant (near-zero) weight-values. On the one hand, the non-structured pruning methods achieve a considerable pruning rate while bringing the computational irregularity, which is un-friendly to parallel-hardware. On the other hand, the existing structured pruning methods consider the hardware parallelism; However, they suffer a poor pruning rate due to the restrict constraints on pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with the novel compressed structured block (CSB) technique. The CSB-pruned RNN model comes with both fine-granularity that benefits the pruning rate and regular structure that facilitates the hardware-parallelism. Further, we propose a novel hardware architecture for inferencing the CSB-pruned model. Different from conventional parallel hardware, this architecture solves the block-workload imbalance issue and achieves an over 95% hardware utilization. With the experiments on 10 RNN models in 5 application domains, the CSB-RNN realizes 7×-20× lossless compression and up to 50× acceptable lossy-compression, which is 2×-7× to the prior art. With the addition of the novel hardware, the compressed-RNN inference reaches a super real-time latency of 10-400µs with FPGA implementation.

Shi, Runbin↗
DOE OSTI2018

Simulating the Effect of Fracture Connectivity on Repository Performance with GDSA Framework - 18589

This work examines the possible migration of radionuclides from a deep geologic nuclear waste repository sited in fractured crystalline host rock. The key safety concern is the potential effect on waste isolation of the inter-connectivity of the fracture network, which is primarily established by the temporal evolution of the temperature and stress fields at the time of original rock intrusion and cooling. Two end members are considered in these simulations, one with a high degree of connectivity to the biosphere, such that advective transport through the fracture and fault network controls radionuclide migration, and the other with a low degree of connectivity, such that slow diffusive transport through the crystalline rock matrix is the controlling process for radionuclide migration to the biosphere. Both end members have several areally extensive, high-transmissivity deformation zones, whose distance to the repository is an important factor for waste isolation capability. Uncertainties in fracture properties (transmissivity, orientation, radius) and fracture distribution also give rise to uncertainty in waste isolation capability (i.e., the overall connectivity of the repository with the surface biosphere). These types of natural system uncertainties are generally present in any geologic site-characterization program and thus represent an important factor in assessing repository performance in hard rock environments. The waste considered in these simulations are spent fuel rod assemblies from the U.S. commercial nuclear reactor fleet. Thermal output of the waste must be considered in the simulations and can influence the rate and timing of waste package failure, waste form degradation, and fluid flux due to thermal expansion around the repository horizon. To achieve a high-fidelity representation of radionuclide transport in fractures and rock matrix, combined with thermal energy transport and fluid flow in fractures and matrix, the mathematical model is solved numerically in a parallel high-performance computing (HPC) environment on a finite volume unstructured grid consisting of approximately 5 million cells, using Geologic Disposal Safety Assessment (GDSA) Framework (https://pa.sandia.gov), an open-source performance assessment tool for deep underground disposal of nuclear waste. GDSA Framework uses PFLOTRAN to solve the balance equations on a three-dimensional grid with heterogeneous properties, using multiple processors in a parallel configuration based on domain decomposition. The fracture networks in these simulations are originally generated as discrete fracture networks (DFNs), which are sets of two-dimensional planes distributed in a three-dimensional domain. The method used in GDSA Framework maps the stochastically generated DFN to an equivalent continuous porous medium (ECPM) domain that allows for the simulation of coupled heat flow, fluid flow, and radionuclide transport, including heat conduction through the matrix of the fractured rock, which is a process not easily modeled in a DFN representation. Computational efficiency is also greatly enhanced using the ECPM method, allowing for a realistic representation and analysis of uncertainties in a multi-realization performance assessment of a deep geologic repository. The effect of fracture connectivity on the waste isolation safety function, as brought to light by these GDSA Framework simulations, points to the importance of including a realistic representation of uncertainties in fracture properties and distribution (effectively, uncertainty in spatial heterogeneity) in repository safety assessment simulations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.

17 WIND ENERGY↗