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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Constraints on simulated past Arctic amplification and lapse rate feedback from observations

The Arctic has warmed more rapidly than the global mean during the past few decades. The lapse rate feedback (LRF) has been identified as being a large contributor to the Arctic amplification (AA) of climate change. This particular feedback arises from the vertically non-uniform warming of the troposphere, which in the Arctic emerges as strong near-surface and muted free-tropospheric warming. Stable stratification and meridional energy transport are two characteristic processes that are evoked as causes for this vertical warming structure. Our aim is to constrain these governing processes by making use of detailed observations in combination with the large climate model ensemble of the sixth Coupled Model Intercomparison Project (CMIP6). We build on the result that CMIP6 models show a large spread in AA and Arctic LRF, which are positively correlated for the historical period of 1951–2014. Thus, we present process-oriented constraints by linking characteristics of the current climate to historical climate simulations. In particular, we compare a large consortium of present-day observations to co-located model data from subsets that show a weak and strong simulated AA and Arctic LRF in the past. Our analyses suggest that the vertical temperature structure of the Arctic boundary layer is more realistically depicted in climate models with weak (w) AA and Arctic LRF (CMIP6/w) in the past. In particular, CMIP6/w models show stronger inversions in the present climate for boreal autumn and winter and over sea ice, which is more consistent with the observations. These results are based on observations from the year-long Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic, long-term measurements at the Utqiagvik site in Alaska, USA, and dropsonde temperature profiling from aircraft campaigns in the Fram Strait. In addition, the atmospheric energy transport from lower latitudes that can further mediate the warming structure in the free troposphere is more realistically represented by CMIP6/w models. In particular, CMIP6/w models systemically simulate a weaker Arctic atmospheric energy transport convergence in the present climate for boreal autumn and winter, which is more consistent with fifth generation reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5). We further show a positive relationship between the magnitude of the present-day transport convergence and the strength of past AA. With respect to the Arctic LRF, we find links between the changes in transport pathways that drive vertical warming structures and local differences in the LRF. This highlights the mediating influence of advection on the Arctic LRF and motivates deeper studies to explicitly link spatial patterns of Arctic feedbacks to changes in the large-scale circulation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

'Self-consistent' production of ion conics on return current region auroral field lines - A time-dependent, semi-kinetic model

We describe initial results from a time-dependent, semi-kinetic model of plasma outflow incorporating wave-particle interactions along current-carrying auroral field lines. Electrostatic waves are generated by the current driven ion cyclotron instability (CDICI), causing perpendicular velocity diffusion of ions plus electron heating via anomalous resistivity when and where the relative drift between electrons and ions exceeds certain critical velocities. Using the local bulk parameters we calculate these critical velocities, and so are able to self-consistently switch on and off the heating of the various particle species. Due to the dependence of these critical velocities on the bulk parameters of the species the heating effects exhibit quite complex spatial and temporal variations. A wide range of ion distribution functions are observed in these simulations, including conics with energies of a few electron volts and 'ring' distributions. The rings are seen to be a natural result of transverse heating and velocity filter effects and do not require coherent acceleration processes. We also observe the formation of a density depletion in hydrogen and enhanced oxygen densities at high altitudes.

Brown, David G.↗
NASA NTRS2006

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Kim, E. J.↗
NASA NTRS2012

The NASA Soil Moisture Active Passive (SMAP) Mission - Science and Data Product Development Status

The Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, global mapping of near-surface soil moisture and its freeze-thaw state. The SMAP measurement system utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate on a spacecraft in a 685 km polar orbit with 6am/6pm nodal crossings, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments will yield global maps of soil moisture and freeze/thaw state at 10 km and 3 km resolutions, respectively, every two to three days. The 10-km soil moisture product will be generated using a combined radar and radiometer retrieval algorithm. SMAP will also provide a radiometer-only soil moisture product at 40-km spatial resolution and a radar-only soil moisture product at 3-km resolution. The relative accuracies of these products will vary regionally and will depend on surface characteristics such as vegetation water content, vegetation type, surface roughness, and landscape heterogeneity. The SMAP soil moisture and freeze/thaw measurements will enable significantly improved estimates of the fluxes of water, energy and carbon between the land and atmosphere. Soil moisture and freeze/thaw controls of these fluxes are key factors in the performance of models used for weather and climate predictions and for quantifYing the global carbon balance. Soil moisture measurements are also of importance in modeling and predicting extreme events such as floods and droughts. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including data from the SMOS and Aquarius missions. We report here on the development status of the SMAP data products. The Testbed simulations are designed to capture various sources of errors in the products including environment effects, instrument effects (nonideal aspects of the measurement system), and retrieval algorithm errors. The SMAP project has developed a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level l) and derived geophysical products (Level 2 and higher).

Nloku, E.↗
NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗
NASA NTRS2024

High Spectral Resolution Lidar – Generation 2 (HSRL-2) Retrievals of Ocean Surface Wind Speed: Methodology and Evaluation

Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of −0.13 and −1.05 m s−1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 m s−1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 m s−1, with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 m s−1 and between 7 and 13.3 m s−1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 m s−1 and an error of ∼ 1.5 m s−1, a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (−0.18 ± 1.52 m s−1) than in the winter (0.63 ± 2.07 m s−1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets.

Sanja Dmitrovic↗
DOE OSTI2026

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗
DOE OSTI2021

Quantifying microbial control of soil organic matter dynamics at macrosystem scales

Soil organic matter (SOM) stocks, decomposition and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly processes. For example, climate history shapes plant functional traits, which in turn interact with contemporary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochemistry that are suited to causative inference; (2) long-term, macrosystem-scale, observational and experimental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. Here, when employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Datasets for characterizing extreme events relevant to hydrologic design over the conterminous United States

Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites

Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Mechanism of the Generation of Donor–Acceptor Pairs in Heavily Doped n-ZrNiSn with the Ga Acceptor Impurity

The nature of the mechanism of the simultaneous generation of donor–acceptor pairs under heavy doping of n-ZrNiSn intermetallic semiconductor with the Ga acceptor impurity is established. Such spatial arrangement in the crystal lattice of ZrNiSn{sub 1–x}Ga{sub x} is found when the rate of movement of the Fermi level εF found from calculations of the density distribution of electron states coincides with that experimentally established from dependences lnρ(1/T). It is shown that when the Ga impurity atom (4s{sup 2}4p{sup 1}) occupies the 4b sites of Sn atoms (5s{sup 2}5p{sup 2}), structural defects of both acceptor nature and donor nature in the form of vacancies in the 4b site are simultaneously generated. The results are discussed in the scope of the Shklovskii–Efros model of a heavily doped and compensated semiconductor.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Effects of Fin Leading Edge Sweep on Shock-Shock Interaction at Mach 6

The effects of fin leading edge sweep on peak heating rates due to shock-shock interaction have been experimentally examined in the Langley 20-Inch Mach 6 Tunnel. The shock interaction was produced by the intersection of a planar incident shock (16.8 deg shock angle relative to the freestream, generated by a 9 deg wedge) with the bow shock formed around a O.5-inch diameter cylindrical leading edge fin. Heating distributions along the leading edge stagnation line have been obtained using densely spaced thin film resistive-type sensors. Schlieren images were obtained to illustrate the very complex shock-shock interactions. The fin leading edge sweep angle was varied from 15-degrees swept back to 45-degrees swept forward for a freestream unit Reynolds number of 2 x 10(exp 6)/ft. Two models were utilized during the study, one with 0.025-inch spacing between gage centers, and the other 0.015-inch spacing. Gage spatial resolution on the order of 0.015-in appeared to accurately capture the narrow spike in heating. Peak heating due to shock interaction was maximized when the fin was swept forward 15 deg and 25 deg, both promoting augmentations about 7 times the baseline value. The schlieren images for these cases revealed Type 4 and Type 3 interactions, respectively.

Berry, Scott A.↗
NASA NTRS2011

First Assessments of ICESat-2 Performance Using Aircraft Data

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Is a next-generation laser altimeter designed to continue key observations of ice sheet elevation change, sea ice freeboard, vegetation canopy height, earth surface elevation, and sea surface height. Scheduled for launch in early 2016, ICESat-2 will use a high repetition rate (approximately 10 kHz), small footprint (10m diameter) laser, and a single-photon-sensitive detection strategy (photon counting) to measure precise ranges to the earth's surface. Operating in the green (approximately 532nm), the six beams of ICESat-2 will provide improved spatial coverage compared with ICESat while the differences in transmit energy among the beams provide a large dynamic range. In order to evaluate models of predicted ICESat-2 performance, and provide ICESat-2-like data for algorithm development an airborne ICESat-2 simulator was developed and first flown in 2010, this simulator, the Multiple Altimeter Beam Experimental Lidar (MABEL) has now had three deployments in the western US, and will be deployed to the polar regions in spring of 2012. MABEL uses a similar measurement strategy to what will be used on ICESat-2. MABEL collects more spatially-dense data than ICESat-2 (approximately 2-cm along track) with a smaller 2m diameter footprint in 16 green channels and an additional 8 channels in the infrared. The comparison between frequencies allows for analysis of possible penetration of green energy into water or snow. We present MABEL data collected over deserts, forests, ocean water, lakes. snow covered mountains, and saft flats, provide examples of how these data are being used to develop algorithms that derive geophysical products from ICESat 2 and assess expected performances.

Neumann, Tom↗
DOE OSTI2019

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

Geological contacts in lateritic metal deposits (footwall topographies) often delineate the orebody boundaries. Spatial variations seen in such contacts are frequently higher than those for the metal grades of the deposit. Therefore, borehole spacing chosen based on the grade variations cannot adequately capture the geological contact variability. Consequently, models created using such boreholes cause high volumetric uncertainties in the actual and targeted ore extraction volumes, which, in turn, lead to high unplanned dilution and ore losses. In this paper, a method to design optimum ore/mining boundaries for lateritic metal deposits is presented. The proposed approach minimizes the dilution/ore losses and comprises two main steps. First, the uncertainty on the orebody boundary is represented using a set of stochastic realizations generated with a multiple-point statistics algorithm. Then, the optimal orebody boundary is determined using an optimization technique inspired by a model calibration method called Pilot Points. The pilot points represent synthetic elevation values, and they are used to construct smooth mining boundaries using the multilevel B-spline technique. The performance of a generated surface is evaluated using the expected sum of losses in each of the stochastic realizations. The simulated annealing algorithm is used to iteratively determine the pilot point values which minimize the expected losses. The results show a significant reduction in the dilution volume as compared to those obtained from the actual mining operation.

58 GEOSCIENCES↗
DOE OSTI2026

Life Cycle Analysis of Natural Gas Supply Chain and End Use Applications in the United States

Natural gas (NG) plays a crucial role in current and future energy systems in the United States due to its abundance and affordability. In this study a life cycle analysis of the NG supply chain in the United States was conducted using Argonne's R&D GREET model, examining stages from recovery to distribution using reported field data processed and documented by National Energy Technology Laboratory. Supply chain emissions were evaluated across multiple spatial scales, including national average, overall regional production, region-to-region, and basin-to-region scenarios. The GHG intensity of the U.S. average NG supply chain was estimated at 10.3 kg CO 2 e/MMBtu (lower heating value), with a range across regions from 7.8 kg CO 2 e/MMBtu (Northeast) to 15.1 kg CO 2 e/MMBtu (Pacific). The analysis further assessed how upstream NG emissions influence the life cycle GHG emissions of key end-use applications, including electricity generation (0.044–0.086 kg CO 2 e/kWh from upstream NG in combined cycle facilities), hydrogen production (1.04–2.20 kg CO 2 e/kg H 2 for steam methane reforming [SMR] and 1.06–2.23 kg CO 2 e/kg for autothermal reforming [ATR]), and transit bus operation utilizing compressed natural gas fuel (0.19–0.37 kg CO 2 e/mile) and hydrogen fuel (0.12–0.25 kg CO 2 e/mile for hydrogen produced in SMR and ATR).

compression↗
DOE OSTI2018

A Detailed Examination of the Topological Constraints of Lamellae-Forming Block Copolymers

A microscopic molecular model of polymeric molecules that captures the effects of topological constraints is used to consider how microphase segregation can alter the distribution of entanglements both in space and along chain contours. Such topological constraints are obtained by using the Z1 algorithm, and it is found that for diblock copolymers in the lamellar morphology they are not homogeneously distributed, but instead exhibit a spatial dependence as a consequence of the self-organization of the polymer blocks. The specific shape of the inhomogeneous distribution is affected by the molecular weight of the copolymer. The microscopic information obtained by these calculations is then compared with the corresponding results generated from a coarser description of entangled block copolymers that includes soft intermolecular interactions and slip-springs, whose role is to incorporate the effects of entanglements that are lost during coarse-graining. Finally, this comparison is helpful for improving coarse-grained simulation approaches for use in multiscale studies of large-scale, self-assembled multicomponent polymer systems.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Stochastic correlation analysis to rank the impact of intermittent wind generation on unreliability margins of power systems

Power systems are vulnerable to extreme contingencies (like an outage of a major generating substation or transmission line) that can cause significant generation and load loss and can lead to further cascading outages of other transmission facilities and generators in the system. Power systems operators take time-ahead actions (real time to day ahead) actions based on forecasted data as well as telemetry. However, there is a probability of extreme swings in wind generation that can push the power system that is already close to system boundaries into unreliable states. Increasing penetration on wind generation and uncertainties in short term forecast can move the system into operational states that can violate N-1 reliability criteria. While the power system planning accounts for worst case analyses to mitigate such emergencies, it is necessary to understand the spatial and temporal correlation of load buses with intermittent wind generation. The ranking of wind generators based on stochastic correlation to unreliability margins of load buses provides an insight of system vulnerabilities that can be corrected by appropriate emergency controls. A Monte-Carlo simulation of wind uncertainty forecast is performed to identify the probability distributions of unreliability margins. Ranking of wind generators is performed based on stochastic correlation analysis of the wind power generated and the load losses at the buses. This helps in not only identifying weak buses in the power system but also in identifying the vulnerable wind locations to plan for system reinforcements. Power system modeling and hybrid simulation to calculate unreliability margins are performed using DCAT and demonstrated on the 32-bus Nordic test system.

generators, wind forecasting, wind power generatio↗
DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗