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An integrated approach to derive relative permeability from capillary pressure
Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability curve can be determined. Relative permeability is a numerical description of the interaction between two or more fluids and the porous media. It is a critical parameter for various tools that characterize subsurface multiphase flow systems, such as numerical simulation for carbon sequestration, oil and gas development, and groundwater contamination remediation. Therefore, it is critical to get a good statistical distribution of relative permeability in the porous media under study. Empirical formula for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility that is required to match laboratory-derived relative permeability curves. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability curves. Mercury intrusion capillary pressure (MICP) data from the Morrow 'B' Sandstone coupled with interfacial tension and contact angle measurements were used to create a suite of relative permeability curves. Furthermore, these curves were then calibrated to a small sample of existing laboratory curves to elucidate common fitting parameters for the formation that were then used to create relative permeability curves from MICP data that does not have an associated laboratory-measured relative permeability curve.
ERA5-Land Data for LASSO-CACTI Overview Paper
The European Centre for Medium-Range Weather Forecasts (ECMWF) generated a soil reanalysis dataset for the land component of the fifth generation of European ReAnalysis (ERA5), referred to as ERA5-Land. This is a model-generated dataset, with the original version available for the period 1950 to present. The version archived in this DOE ARM product is a subset of the data is for the period of the CACTI field campaign plus several preceding months, specifically from August 1, 2018 through March 22, 2019 with hourly intervals. The ARM copy is also a sub-region of the original global product; the ARM copy is for -60 to -5 °N by -105 to -30 °W. Only variables necessary to drive the WRF-Hydro model are included, which are the 2-m temperature and specific humidity, 10-m wind components, surface pressure, rain rate, and downward surface short and longwave radiation. These data have been obtained from the Copernicus Data Store.
Mixing Height and Air Mass Data
Hourly mixing heights, inversion heights and surface pressure gradients are available for Ponca, OK and Oklahoma City, OK from 1/1/24 - 1/6/25. Hourly mixing heights, inversion heights, air mass types and surface pressure gradients are available for Plymouth, MA and six sites offshore of New England.
Wind and Weather Variability within the Californian Offshore Wind Energy Areas
Weather variability over the Northeast Pacific (NEP) region and its influence on wind resources within the Californian offshore wind energy areas (WEAs) at Humboldt and Morro Bay are characterized using 20-years reanalysis model and satellite data. The hub-height (180 m) winds at both locations are predominantly northwesterly driven by the NEP high pressure system, with strong coastal gradients in surface pressure, fluxes, planetary boundary layer (PBL) depths and cloudiness. These sharp coastal gradients and strong annual cycles of temperature and moisture advections pose potential challenges in accurately modeling the local wind resource. Hub-height wind speeds and power capacity factors significantly vary for different regimes of PBL depths, surface fluxes and rain area fractions. This highlights the importance of studying the physical mechanisms driving these weather regimes, hence our analysis of how large-scale NEP weather variability drives the local meteorology at the WEAs. Furthermore, at both WEAs, PBL tops and cloud boundaries intersect the rotor layer (80-280 m) more than 30% and 20% of the time, respectively. While PBL depths significantly modulates hub-height winds and power, cloud boundaries do not have a similar impact, likely due to reanalysis errors in simulating cloud boundaries accurately. These findings underscore the challenges in deploying tall wind turbines in shallow cloudy boundary layers, where the interaction between clouds, precipitation, and atmospheric layers can impact turbine efficiency. As turbines grow taller and are deployed in more complex meteorological conditions, understanding these interactions is crucial for improving wind power forecasting and optimizing energy production in coastal regions.
Amphiphilic Baskets for Supramolecular Nanoarchitectures at Interfaces: Inverted Monolayer Formation on Water
Interfacial chemistry of molecular baskets remains poorly understood despite their promise for supramolecular applications of detection and sequestration of toxic molecules including those of illicit drugs, organophosphorus compounds, and anticancer agents. We present a fundamental investigation of the interfacial behavior of three amphiphilic supramolecular baskets (ASB 4, 8, and 12), having increasingly longer yet linear alkyl chains at the top of their bowl-shaped cavity. The studies were completed at the air−water interface to elucidate surface activity, interfacial stability, self-assembly, and monolayer organization that drive inverted monolayer formation, in which the molecular arms orient toward the aqueous phase in a configuration opposite to that typically observed for lipids. Herein, surface pressure−area isotherms of ASB 4, 8, 12, deposited on a water surface, were performed in tandem with nonequilibrium relaxation experiments to quantify surface activity, thermodynamic stability, and monolayer compressibility of the baskets’ monolayer assembly. Brewster angle microscopy enabled direct visualization of morphological evolution, aggregation, and packing at the interface. We show that systematic extension of the hydrocarbon arms, from four to 12 methylene groups, progressively modifies intermolecular packing, drives distinct two-dimensional aggregation pathways, and increases number densities at the air−water interface. Atomistic molecular dynamics simulations corroborate many of these experimentally observed trends and provide mechanistic detail on the cooperative roles of basket topology and interfacial concentration in regulating the hydration structure and dynamics within the cavities generated by surface-adsorbing baskets, consistent with observed variations in surface activity and packing. Our results establish how the topology of these unique supramolecules and their concentration govern interfacial organization and offer a rational framework for designing amphiphiles with predictable behavior at soft interfaces.
Study of CO 2 Adsorption Properties on the SrTiO 3 (001) Surface with Ambient Pressure XPS
The adsorption properties of CO 2 on the SrTiO 3 (001) surface were investigated using ambient pressure X-ray photoelectron spectroscopy under elevated pressure and temperature conditions. On the Nb-doped TiO 2 -enriched (1 × 1) SrTiO 3 surface, CO 2 adsorption, i.e., the formation of CO 3 surface species, occurs first at the oxygen lattice site under 10 –6 mbar CO 2 at room temperature. The interaction of CO 2 molecules with oxygen vacancies begins when the CO 2 pressure increases to 0.25 mbar. The adsorbed CO 3 species on the Nb-doped SrTiO 3 surface increases continuously as the pressure increases but starts to leave the surface as the surface temperature increases, which occurs at approximately 373 K on the defect-free surface. On the undoped TiO 2 -enriched (1 × 1) SrTiO 3 surface, CO 2 adsorption also occurs first at the lattice oxygen sites. Both the doped and undoped SrTiO 3 surfaces exhibit an enhancement of the CO 3 species with the presence of oxygen vacancies, thus indicating the important role of oxygen vacancies in CO 2 dissociation. When OH species are removed from the undoped SrTiO 3 surface, the CO 3 species begin to form under 10 –6 mbar at 573 K, thus indicating the critical role of OH in preventing CO 2 adsorption. The observed CO 2 adsorption properties of the various SrTiO 3 surfaces provide valuable information for designing SrTiO 3 -based CO 2 catalysts.
Thermal-Fluid and Thermal-Structural Response of the T-Tube Modular Divertor to Spatiotemporally Varying Heat Loads
Tungsten (W) is the leading candidate for divertor target plates because of its high melting point (>3000°C), thermal conductivity, and ultimate tensile stress. While W and its alloys are the only solid materials that can survive the high heat fluxes incident on the divertor, W’s low-ductility high ductile-to-brittle transition temperature of ~600°C and relatively low recrystallization temperature (RT) of ~1300°C pose structural (among other) challenges. The objective of this work is to estimate the thermal-fluid and thermal-structural performance of the helium (He)-cooled T-tube divertor, which was originally developed by the Advanced Reactor Innovation and Evaluation Study (ARIES) using numerical simulations. Here, predictions of temperature distributions across the plasma-facing structural component and surface pressures from computational fluid dynamics simulations are used to determine stress distributions using commercial structural finite element modeling software over a range of fusion-relevant conditions. The maximum allowable incident heat fluxes are determined based on the temperature limits imposed by the ITER elastic Structural Design Criteria for In-vessel Components (SDC-IC) and the maximum RT over a range of He mass flow rates and presented in the form of performance design charts. Our recent work found that thermal- structural criteria accounting for the low ductility of W in a finger-type modular divertor constrain the maximum incident heat fluxes to values well below the ITER specifications, and those based on considering only the RT demonstrate that integrated thermal-fluid and elastic structural performance evaluation are required for accurate assessment of divertor performance. This novel analysis of the T-tube considers how nonuniform and transient incident heat fluxes affect its thermal-fluid and thermal-structural performance, as well as the effect of volumetric heating, which can be as great as 27% of the power incident on the divertor surface. The W tile of the T-tube, with its relatively large plasma-facing area of ~15 cm 2 , will likely experience significant spatial variations in incident heat flux. This work therefore assesses whether steady-state incident heat flux profiles with a peak of 10 MW/m 2 and maximum heat flux gradients of 200 MW/m 2 per m exceed the structural limits imposed by the ITER elastic SDC-IC and the maximum RT over a range of fusion-relevant conditions. The effect of transient heat fluxes typical of plasma detachment and reattachment from the target plate due, for example, to gas injection are also evaluated
Projection-based multifidelity linear regression for data-scarce applications
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations with limited, costly high-fidelity evaluations. We introduce two projection-based multifidelity linear regression approaches with linear and nonlinear features that leverage principal component basis vectors for dimensionality reduction and combine multifidelity data through: (i) a direct data augmentation using low-fidelity data, and (ii) a data augmentation incorporating explicit linear corrections between low-fidelity and high-fidelity data. The data augmentation approaches combine high-fidelity and low-fidelity data into a unified training set and train the linear regression model through weighted least squares with fidelity-specific weights. We introduce a proximity-based weighting scheme with automatic weight selection strategy through cross-validation. Here, the proposed multifidelity linear regression methods are demonstrated on approximating the surface pressure field of a hypersonic vehicle in flight and the temperature field on an aircraft disc braking system. In an ultra low-data regime of no more than twelve high-fidelity samples, multifidelity linear regression achieves approximately 2% – 12% improvement in median accuracy and a higher R 2 score relative to single-fidelity methods at comparable computational cost.
Observed Increase in Tropical Cyclone‐Induced Sea Surface Cooling Near the U.S. Southeast Coast
Abstract Tropical cyclones (TCs) induce substantial upper‐ocean mixing and upwelling, leading to sea surface cooling. In this study, we explore changes in TC‐induced cold wakes along the United States (U.S.) Southeast and Gulf Coasts during 1982–2020. Our study shows a significant increase in TC‐induced sea surface temperature (SST) cooling of about 0.20°C near the U.S. Southeast Coast over this period. However, for the U.S. Gulf Coast, trends in TC‐induced SST cooling are insignificant. Analysis of the large‐scale oceanic environments indicate that the increasing TC‐induced cold wakes near the Southeast coast have been predominantly caused by the cooling of subsurface waters in that region. This upper‐ocean change is attributed to the enhancement of surface pressure gradient across land‐sea boundary and the associated increase in alongshore winds over there. Further analysis with climate models reveals the important role of anthropogenic forcings in driving these changes in the atmospheric circulation response along the U.S. Southeast Coast.
Environmental Controls on Water Vapor Deuterium Excess in the Coastal Boundary Layer: An Information Theory Perspective
We use information theory to quantify the environmental controls on water vapor deuterium excess (D-excess) in coastal Southern California from June 2023 through February 2024. Using Shannon entropy, mutual information (MI), and joint mutual information, metrics that capture both linear and nonlinear relationships, we identify the most informative variables and variable combinations governing D-excess across contrasting marine and continental regimes. Relative humidity with respect to sea surface temperature (RHS) is consistently the strongest individual predictor, explaining up to 27% of D-excess variability during marine conditions but only 10% in continental air masses. The Relative humidity(RHS) + sea surface temperature (SST) combination demonstrates synergistic effects, where their joint influence (explaining up to 36% of D-excess variability) exceeds what either variable achieves individually, confirming their coupled influence on deuterium excess. Wind direction complements RHS most effectively during continental conditions. The best three-variable combination (RHS + SST + Planetary Boundary Layer height) explains 38% of D-excess variability in marine air, while no combination exceeds 20% explanatory power during continental periods. Information theory shows that heteroscedasticity in D-excess relationships indicates regime shifts in controlling processes and quantifies fundamental constraints on predictor variables: some environmental factors like surface pressure or water vapor flux contain insufficient information content to explain D-excess variability regardless of their physical relevance. These results highlight the different predictability limits between marine and continental regimes, challenging the adequacy of linear models and providing a rigorous framework for quantifying the information content of isotope-climate relationships with implications for both modern and paleoclimate applications.
Savannah River Site Climatology for Research Activities Conducted Onsite and in P-Area (1994-2023)
This report presents a climatology of meteorological variables at the Savannah River Site (SRS) using measurements from the P-Area and Central Climatology meteorological towers to support research activities at the Savannah River Site. Key values included in this report are wind, surface pressure, temperature, relative humidity, soil moisture, cloud fraction, cloud base, lightning, atmospheric stability, and boundary layer parameters. The Central Climatology tower is useful in that it is instrumented at four levels, allowing for an examination of height dependence on some variables.
Savannah River Site Climatology for Research Activities Conducted Onsite and in P-Area (1994-2023)
Executive Summary This report presents a climatology of meteorological variables at the Savannah River Site (SRS) using measurements from the P-Area and Central Climatology meteorological towers to support research activities at the Savannah River Site. Key values included in this report are wind, surface pressure, temperature, relative humidity, soil moisture, cloud fraction, cloud base, lightning, atmospheric stability, and boundary layer parameters. The Central Climatology tower is useful in that it is instrumented at four levels, allowing for an examination of height dependence on some variables.
Concurrent measurement of strain and chemical reaction rates in a calcite grain pack undergoing pressure solution: Evidence for surface-reaction controlled dissolution
Pressure solution is inferred to be a significant contributor to sediment compaction and lithification, especially in carbonate sediments. For a sediment deforming primarily by pressure solution, the compaction rate should be directly related to the rate of calcite dissolution, transport along grain contacts, and calcite reprecipitation. Previous experimental work has shown that there is evidence that deformation in wet calcite grain packs is consistent with control by pressure solution, but considerable ambiguity remains regarding the rate limiting mechanism. We present the results of laboratory compaction experiments designed to directly measure calcite dissolution and precipitation rates (recrystallization rates) concurrently with strain rate to test whether measured rates are consistent with predicted rates both in absolute magnitude and time evolution. Recrystallization rates are measured using trace element chemistry (Sr/Ca, Mg/Ca) and isotopes (87Sr/86Sr) of fluids flowing slowly through a compacting grain pack as it is being triaxially compressed. Imaging techniques are used to characterize the grain contacts and strain effects in the post-experiment grain pack. Our data show that calcite recrystallization rates calculated from all three geochemical parameters are in approximate agreement and that the rates closely track strain rate. The geochemically inferred rates are close to predicted rates in absolute magnitude. Uncertainty in grain contact dimensions makes distinguishing between surface reaction control and diffusion control difficult. Measured reaction rates decrease faster than predicted from standard pressure solution creep flow laws. This inconsistency may indicate that calcite dissolution rates at grain contacts are more complex, and more time-dependent, than suggested by geometric models designed to predict grain contact stresses.
Xanthos-Lake Dataset
The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.
Modeling and testing of a highly sensitive surface acoustic wave pressure sensor for liquid depth measurements
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Surface intermediates identified using IR spectroscopy during the catalytic oxidation of C 2 H 6 on IrO 2 (1 1 0) at near-ambient pressures
Characterizing surface intermediates during catalysis is essential for developing mechanistic models of catalytic reactions and for identifying rate-controlling steps. Here, in this work, we used polarization-dependent reflection absorption infrared spectroscopy (PD-RAIRS) to investigate surface intermediates that form on IrO 2 (1 1 0) during the catalytic oxidation of C 2 H 6 at pressures near 0.5 Torr. Our results show that adsorbed CO and HCO 2 are the only intermediates detectable with PD-RAIRS across a wide range of temperatures and reactant compositions during complete C 2 H 6 oxidation, giving rise to dominant peaks at about 2093 and 1310 cm −1 , respectively. These assignments were corroborated by RAIRS measurements following CO and HCOOH adsorption in ultrahigh vacuum, RAIRS with C 2 H 6 – 18 O 2 mixtures during catalysis and vibrational frequencies and IR intensities computed using density functional theory. We find that increasing the C 2 H 6 mole fraction in the reaction mixture raises the CO surface coverage relative to HCO 2 , correlating with increasing catalytic oxidation rates. The formation of CO and HCO 2 surface species at temperatures well below catalytic ignition indicates that the overall rate of C 2 H 6 oxidation on IrO 2 (1 1 0) is governed by steps late in the reaction sequence, such as H 2 O formation, CO oxidation and HCO 2 dehydrogenation. These findings provide key insight into the mechanism of alkane oxidation on IrO 2 (1 1 0) and valuable input for first-principles microkinetic modeling.
Surface Reactivity of Carbon in High-Enthalpy, Atmospheric-Pressure Nitrogen and Air Plasmas
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