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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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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Automated electrical demand peak leveling in a manufacturing facility with short term energy storage for smart grid participation

Demand side management of energy is a vital function in the smart grid that allows for greater integration of renewable energy resources, and is facilitated with economic incentives in energy and demand pricing schedules. Manufacturing systems can take advantage of these incentives to reduce their energy costs through active energy management. This paper is a simulation analysis on the implementation of a novel algorithm that levelizes the maximum power load of an industrial bakery that operates under an on-peak demand price structure. The algorithm takes advantage of an untapped thermal energy storage resource in the facility, a chilled glycol buffer tank, to level the short intra-day power oscillations that the facility experiences. One of the key advantages of the algorithm is that it does not require precise demand forecasting or complex control algorithms, such as model predictive control. Even with substantial error in peak power estimates (5%), the algorithm is expected to result in at least a 2% peak reduction. Under ideal prediction, the algorithm reduces the on-peak facility power maximum by 7.3% of the possible 9.0% reduction the storage can provide. Further, the algorithm uses very few facility specific inputs, and can readily be adapted for any facility with short intra-day oscillatory power profiles and untapped storage capacity. The key insight of the paper is the employment of a novel algorithm to leverage untapped energy storage in manufacturing facilities to transform them into smart grid participants with no major capital investment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Control of Weld Residual Stress in a Thin Steel Plate through Low Transformation Temperature Welding Consumables

Low transformation temperature welding (LTTW) consumables are characterized by a low martensite start temperature and a large fraction of martensite forming in the weld. It can efficiently reduce the tensile residual stress because the volume expansion associated with the martensitic transformation compensates for the thermal contraction during cooling. In this work, a LTTW wire, designated as EH200B, was created for the arc welding of advanced high-strength steel thin plates. In comparison to conventional ER70S-3 wires, this LTTW wire generated an opposite distortion pattern. Neutron diffraction measurements along the center thickness of the welded plates showed the maximum residual stress along the longitudinal direction (LD) in the weld region, and the heat-affected zone (HAZ) immediately adjacent to the weld region was reduced from ~330 MPa to below 240 MPa by using the LTTW wire. A finite element (FE) model was developed to predict the residual stress distributions of the plates welded under these two wires. The simulation results showed reasonable agreement with the volume-average neutron diffraction data. Compressive residual stress in the weld region using the LTTW wire was predicted by the FE method. Electron backscattered diffraction and x-ray diffraction measurements confirmed ~90% martensite was present in the LTTW weld. The fatigue life of DP980 steel lap joint panels using EH200B wire nearly doubled that of ER70S-3 wire. This improvement was attributed to the high strength and low LD residual stress in the weld and HAZ immediately adjacent to the weld.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tidal Stream Energy Resource Characterization in the Salish Sea

Tidal stream energy holds great potential as a renewable energy source in regions of the world where tidal currents are strong and predictable. The tidal stream energy resource of a region is strongly controlled by its tidal wave characteristics, the local bathymetry, and coastal geometric features. The Salish Sea, a large estuary on the Pacific Northwest coast, represents a great tidal stream resource because of its strong tidal currents in many tidal channels. However, the tidal resource in the Salish Sea has not been systematically characterized, primarily because of its large area and complex bathymetry and coastlines. This paper presents a modeling study conducted to characterize the tidal energy resource of the Salish Sea based on a high-resolution three-dimensional tidal hydrodynamic model of the Salish Sea, which was extensively validated using data derived from 10 tidal gauge stations and 132 Acoustic Doppler Current Profiler stations. Model validation results indicated the Salish Sea hydrodynamic model is skillful in simulating tidal wave propagation and velocity distributions in the Salish Sea. Based on model results, a total of 16 tidal channels with strong tidal currents, 9 in the San Juan Islands and Rosario Strait regions and 7 in the Puget Sound, were identified as hotspots for potential tidal energy development. Velocity probability distributions and exceedance curves of cross-channel average velocity magnitudes were calculated at all 16 channels based on the recommendations of the International Electrotechnical Commission Technical Specifications for tidal energy resource characterization. The tidal energy resource at the 16 hotspots was also characterized using power density distributions and kinetic energy fluxes. The ranking of the kinetic energy fluxes suggested that Admiralty Inlet, Rosario Strait, and Middle Channel are the top three hotspot sites in the Salish Sea for tidal stream energy development. The study demonstrated the need for a high-resolution 3-D modeling framework for accurate simulation of tidal currents in large complex estuarine systems, in the context of tidal energy resource characterization.

tidal stream energy, resource characterization, Sa↗
DOE OSTI2022

Description of global EGAM in the maximum of local frequency during current ramp-up discharges in DIII-D

Energetic-particle-induced geodesic acoustic modes, EGAMs (Fu, Phys. Rev. Let., vol. 101, 2008, pp. 185002), driven by neutral beam injection (NBI), have been observed in many DIII-D tokamak experiments (Nazikian et al., Phys. Rev. Lett., vol. 101, 2008, pp. 185001). This mechanism has been theoretically investigated in (Qiu et al., Plasma Phys. Control. Fusion, vol. 52, 2010, pp. 095003), using a sharp energetic particle distribution function, and in (Qu et al., Plasma Phys. Control. Fusion, vol. 59, 2017, pp. 055018), where the dispersion relation and eigenmode behaviour were obtained for the situation of early beam scenario, that is, for times smaller than the beam slowing down time. In this work, we extend these studies determining the eigenmode for beyond the slowing down time, in a scenario with reverse safety factor q profile, where a small concentration of energetic ions can produce an off-axis maximum in the GAM dispersion relation. Here, the characteristics of EGAM are analytically studied with the drift kinetic equation together with the MHD code NOVA. The toroidal energetic ion transit frequency, coupled with the GAM frequency, produces the maximum in the dispersion relation where the eigenmode can be found. The quantitative correspondence of experimental results with the predictions of the proposed model is analysed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

An Advanced Microscopic Energy Consumption Model for Automated Vehicle:Development, Calibration, Verification

The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.

Ma, Ke↗
DOE OSTI2023

Bentonite buffer under high temperature: laboratory experiments and coupled process modeling

Abstract. Bentonite buffer in a geological repository will be simultaneously heated from decaying radioactive waste and hydrated from the surrounding host rock, triggering complex and coupled THMC (thermal–hydrological–mechanical–chemical) processes. Understanding the THMC behavior of bentonite-based engineered barrier system (EBS) is key to the evaluation and prediction of its long-term performance. Studies on the THMC process have been focused on conditions under 100 ∘C, as most design concepts impose a thermal limit of 100 ∘C in bentonite. Recently, studies under high-temperature conditions have been conducted to evaluate the possibility of raising the thermal limit and expanding the data/knowledge base to increase the confidence level. In this abstract, we present a series of bench-scale laboratory experiments at high temperatures (up to 200 ∘C) and the corresponding modeling work. Two sets of column tests were conducted, and each set consisted of two test columns: a control column undergoing only hydration (non-heated) and an experiment column experiencing both heating and hydration (heated). During the experiment, frequent X-ray computed tomography (CT) images were collected to provide a 3D visualization of the density distribution and present the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. The two sets of tests differ with respect to several experimental conditions, such as bentonite type, compacted density and water content, water chemistry, and hydration pressure, but the important difference is that the first set used bentonite powder with a dry density of 1.28 g cm−3, whereas the second set used granulated bentonite (mixture of pellets and powder) with a dry density of 1.45–1.5 g cm−3. In both sets of experiments, a comprehensive post-dismantling characterization of bentonite samples was carried out after the column tests had been running for 1.5 years. Comparing non-heated and heated columns, the temperature gradient led to lower degree of homogenization of bentonite after bentonite became fully saturated; comparing the first and second sets, granulated and powdered bentonite exhibited drastically different hydration behavior. A THM model with a 2D axisymmetric grid system was used to interpret the data from the first set of tests. The model considers the combined impact of saturation, fluid pressure, and porosity change due to swelling/compression on the spatiotemporal distribution of bulk density and movement of the thermocouple modules. Observations from the tests help us understand the early perturbation of bentonite buffer under high temperature, and data from these tests improve the calibration of key constitutive hydrological and mechanical models and, therefore, enhance the modeling capability with respect to calculating the long-term evolution of bentonite buffer.

Zheng, Liange↗
DOE OSTI2018

Model Identification of Inverter Nonlinear Control Dynamics

To comprehensively predict in silico potential disruptive dynamic performance of converter-based generation and develop solutions, the application of electromagnetic transient programs is desirable. However, these accurate simulations highly depend on inverter models of actual field implementations, which are often unavailable because they are proprietary. To address this problem, we propose a time-domain model identification method that can be used to determine the control models of inverters in continuous time from multiple experimental studies. The focus is on identifying the dynamic representation of controls because they dominate the dynamic response of renewable assets. To illustrate the applicability of the technique, we consider a subclass of inverter controllers for autonomous grid applications that rely on nonlinear oscillators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Improving the Representation of Soluble Iron in Climate Models

Mineral dust produced in the arid and semi-arid regions of the world is the dominant source of iron (Fe) in atmospheric aerosol inputs to the open ocean. The bioavailable Fe fraction of atmospheric dust is thought to regulate and occasionally limit the primary productivity in large oceanic regions, which influences the CO2 uptake from the atmosphere affecting the Earth’s climate. Because Fe bioavailability cannot be directly measured, it is assumed that the dissolved Fe or highly reactive Fe in the dust is bioavailable. The fraction of soluble Fe in dust is mainly controlled by: (1) the mineral composition of the soils and the emitted dust from the source areas; (2) the atmospheric processing that converts the Fe in Fe-bearing minerals into highly soluble forms of Fe. The project has mainly focused on constraining the mineral composition of dust aerosols (1), a previously neglected, yet a key issue to constrain the deposition of soluble iron. Deriving aerosol mineral composition requires global knowledge of the soil mineral content, which is available from poorly constrained global atlases. In addition, the mineral content of the emitted aerosol differs from that of the parent soil. Measurements of soil mineral fractions are based upon wet sedimentation (or ’wet sieving’) techniques that disturb the soil sample, breaking aggregates that are found in the original, undispersed soil that is subject to wind erosion. Wet sieving alters the soil size distribution, replacing aggregates that are potentially mobilized as aerosols with a collection of smaller particles. A major challenge is to derive the size-distributed mineral fractions of the emitted dust based upon their fractions measured from wet-sieved soils. Finally, representations of dust mineral composition need to account for mixtures of minerals. Examination of individual particles shows that iron, an element that is central to many climate processes, is often found as trace impurities of iron oxide attached to aggregates of other minerals. This is another challenge that has been tackled by the project. The project has produced a major step forward on our understanding of the key processes needed to predict the mineral composition of dust aerosols by connecting theory, modeling and observations. The project has produced novel semi-empirical and theoretical methods to estimate the emitted size distribution and mineral composition of dust aerosols. These methods account for soil aggregates that are potentially emitted from the original undisturbed soil but are destroyed during wet sieving. The methods construct the emitted size distribution of individual minerals building upon brittle fragmentation theory, reconstructions of wet-sieved soil mineral size distributions, and/or characteristic mineral size distributions estimated from observations at times of high concentration. Based on an unprecedented evaluation with a new global compilation of observations produced with the project support, we showed that the new methods remedy some key deficiencies compared to the previous state-of-the-art. This includes the correct representation of Fe-bearing phyllosilicates at silt sizes, where they are abundant according to observations. In addition, the quartz fraction of silt particles is in better agreement with measured values. In addition, we represent an additional class of iron oxide aerosol that is a small impurity embedded within other minerals, allowing it to travel farther than in its pure crystalline state. We assume that these impurities are least frequent in soils rich in iron oxides (as a result of the assumed effect of weathering that creates pure iron oxide crystals). The mineral composition of dust is also important to other interaction with climate - through shortwave absorption and radiative forcing, nucleation of cloud droplets and ice crystals, and the heterogeneous formation of sulfates and nitrates - and to its impacts upon human health. Despite the importance of mineral composition, models have typically assumed that soil dust aerosols have globally uniform composition. The results of this project will allow an improved estimation of the dust effects upon climate and health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. Furthermore, the results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.

14 SOLAR ENERGY↗
DOE OSTI2024

Dynamic Surface Incorporation of Pb 2+ Ions at the Actively Dissolving Calcite (104) Surface

The reaction of dissolved Pb 2+ with calcite surfaces at near-equilibrium conditions involves adsorption of Pb 2+ and precipitation of secondary heteroepitaxial Pb-carbonate minerals. A more complex behavior is observed under far-from-equilibrium conditions, including strong inhibition of calcite dissolution, development of microtopography, and near-surface incorporation of multiple monolayers (ML) of Pb 2+ without precipitation of secondary phases [where 1 ML ≡ 1 Ca/20.2 Å 2 , the crystallographic site density of the calcite (104) lattice plane]. However, the mechanistic controls governing far-from-equilibrium reactivity are not well understood. Here, in this study, we observe the interfacial incorporation of dissolved Pb 2+ during the dissolution of calcite (104) surfaces at pH ~3.7 in a flow-through reaction cell, revealing the formation of a ~1 nm thick Pb-rich calcite layer with a total Pb coverage of ~1.4 ML. These observations of the sorbed Pb distribution used resonant anomalous X-ray reflectivity, X-ray fluorescence, and nanoinfrared atomic force microscopy. We propose that this altered surface layer represents a novel sorption mode that is stabilized by conditions of sustained disequilibrium. This behavior may significantly impact the transport of dissolved metals during disequilibrium processes occurring in acid mine drainage and subsurface CO 2 injection and, if appropriately accounted for, could improve the predictive capability of geochemical reactive-transport models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Interactions of electrons with two lower hybrid waves

The effects of perturbed orbits on the interactions of electrons with two lower hybrid waves, one of which is resonant with electrons at a low phase velocity (v p1 = 3.8V the , where v p1 is the wave phase velocity and V the is the electron thermal speed) while the other is off-resonant at a high phase velocity (v p2 = 5.5V the ), have been studied by using the particle simulation code based on the gyro-kinetic electron and fully-kinetic ion (GeFi) model [Lin et al., Plasma Phys. Controlled Fusion 47, 657 (2005)]. When the amplitude of the off-resonant wave is sufficiently small so that the resonances of these two waves do not overlap, the variation of the resonant wave amplitude is similar to that predicted by O'Neil's theory [O'Neil, Plasma Fluid 8, 12 (1965)]. With the increasing amplitude, the two resonances overlap and large scale chaos emerges. As a result, the damping of the resonant wave can be enhanced, which is due to that the trapped electron orbits are significantly perturbed by the off-resonant wave. The diffusion process gives rise to the enhanced damping. In conclusion, when the overlap is sufficiently large, the damping of the off-resonant wave and the oscillatory behavior of the wave amplitude are observed. In addition, the resonant plateau in the distribution function can be broadened due to the change in the chaotic region boundaries as the electron perturbed orbits are taken into account.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Meteorological Controls on Wind Turbine Wakes

The primary control on the magnitude of the power losses induced by wind turbine wakes in large wind farms is the hub-height wind speed via its link to the turbine thrust coefficient. Hence, at low to moderate wind speeds (between cut-in and rated turbine wind speeds) when the thrust coefficient is high, wake losses are proportionally larger and decrease to be virtually undetectable at wind speeds above rated wind speeds. Wind direction is also critical. Not only does it determine the effective spacing between turbines but also the wind speed distribution is primarily determined by synoptic forcing and typically has a predominant direction from which wind speeds tend to be higher (from southwest for much of the central United States and northern Europe). Two other interlinked variables, turbulence intensity (TI), and atmospheric stability also dictate wake losses. Quantifying, understanding, modeling, and predicting this complex and interdependent system is therefore critical to understanding and modeling wind farm power losses due to wakes, and to optimizing wind farm layout. This paper quantifies the impact of these variables on the power loss due to wakes using data from the large offshore wind farms located at Horns Rev and Nysted in Denmark.

Barthelmie, RJ↗
DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2011

Detection and Production of Methane Hydrate

This project seeks to understand regional differences in gas hydrate systems from the perspective of as an energy resource, geohazard, and long-term climate influence. Specifically, the effort will: (1) collect data and conceptual models that targets causes of gas hydrate variance, (2) construct numerical models that explain and predict regional-scale gas hydrate differences in 2-dimensions with minimal 'free parameters', (3) simulate hydrocarbon production from various gas hydrate systems to establish promising resource characteristics, (4) perturb different gas hydrate systems to assess potential impacts of hot fluids on seafloor stability and well stability, and (5) develop geophysical approaches that enable remote quantification of gas hydrate heterogeneities so that they can be characterized with minimal costly drilling. Our integrated program takes advantage of the fact that we have a close working team comprised of experts in distinct disciplines. The expected outcomes of this project are improved exploration and production technology for production of natural gas from methane hydrates and improved safety through understanding of seafloor and well bore stability in the presence of hydrates. The scope of this project was to more fully characterize, understand, and appreciate fundamental differences in the amount and distribution of gas hydrate and how this would affect the production potential of a hydrate accumulation in the marine environment. The effort combines existing information from locations in the ocean that are dominated by low permeability sediments with small amounts of high permeability sediments, one permafrost location where extensive hydrates exist in reservoir quality rocks and other locations deemed by mutual agreement of DOE and Rice to be appropriate. The initial ocean locations were Blake Ridge, Hydrate Ridge, Peru Margin and GOM. The permafrost location was Mallik. Although the ultimate goal of the project was to understand processes that control production potential of hydrates in marine settings, Mallik was included because of the extensive data collected in a producible hydrate accumulation. To date, such a location had not been studied in the oceanic environment. The project worked closely with ongoing projects (e.g. GOM JIP and offshore India) that are actively investigating potentially economic hydrate accumulations in marine settings. The overall approach was fivefold: (1) collect key data concerning hydrocarbon fluxes which is currently missing at all locations to be included in the study, (2) use this and existing data to build numerical models that can explain gas hydrate variance at all four locations, (3) simulate how natural gas could be produced from each location with different production strategies, (4) collect new sediment property data at these locations that are required for constraining fluxes, production simulations and assessing sediment stability, and (5) develop a method for remotely quantifying heterogeneities in gas hydrate and free gas distributions. While we generally restricted our efforts to the locations where key parameters can be measured or constrained, our ultimate aim was to make our efforts universally applicable to any hydrate accumulation.

03 NATURAL GAS↗
DOE OSTI2025

Topography, surface water distribution and subsurface structure in 2023 across an Arctic coastal tundra site near Utqiagvik, Alaska

Subsurface electrical resistivity tomography (ERT), active layer thickness measurements, photogrammetry, and topographic data were collected in September 2023 along a 475 m long, 20 m wide corridor that traverses various polygon types within the Barrow Environmental Observatory (BEO) on the Alaskan Arctic Coastal Plain, approximately 4 miles from the Beaufort Sea near Utqiaġvik, Alaska. These measurements were designed to assess decadal changes in surface water distribution, topography, and subsurface structure across this dynamic landscape. This archive contains the datasets acquired in 2023 and references to the datasets acquired previously at the same location. The ERT survey was conducted along the 475 m transect using 0.5 m electrode spacing and a roll-along acquisition strategy. Thaw layer thicknesses were measured with a tile probe along the same transect. Photogrammetry data were acquired using an unoccupied aerial vehicle (UAV) and were used to generate a digital elevation model and an RGB mosaic. A real-time kinematic (RTK) GPS was used to survey the ERT electrodes and the ground control points for the aerial imagery. The dataset contains 5 *.csv data files, 6 *.csv metadata files, and 6 *.tif files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A Survey on Social Media to Enhance the Cyber-PhysicalSocial Resilience of Smart Grid

Smart sensor that enables power grid resiliency at distribution network is a very expensive solution and its implementation has been very sluggish. Social media has great potential to become an alternate abstract information channel that can replace/reinforce smart sensor capabilities towards a more resilient power grid. In this paper, we present a survey on the progress of utilizing social media as a platform to extract and analyse abstract data to produce intelligent information. This information can be utilized to plan and implement resources towards power grid resiliency from micro level (consumers) to macro level (cities/states). This paper discusses the reliability of social sensors, various concepts relating to social media and their implementation to solve problems, and how we can develop a framework based on the social media towards a resilient power grid. The realization of social media framework for information will benefit as robust communication and control channels that can be used together with prediction models to improve the accuracy and effectiveness of system operators' decision-making.

DISASTER RESPONSE, HASHTAGS, POWER GRID RESILIENCE↗