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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Probabilistic Assessment of Structural Integrity

A probability-based approach, combining deterministic and probabilistic methods, was developed for analyzing building and component failures, which are especially crucial for complex structures like nuclear power plants. This method links finite element and probabilistic software to assess structural integrity under static and dynamic loads. This study uses NEPTUNE software, which is validated, for a deterministic transient analysis and ProFES software for probabilistic models. In a case study, deterministic analyses with varied random variables were transferred to ProFES for probabilistic analyses of piping failure and wall damage. A Monte Carlo Simulation, First-Order Reliability Method, and combined methods were employed for probabilistic analyses under severe transient loading, focusing on a postulated accident at the Ignalina Nuclear Power Plant. The study considered uncertainties in material properties, component geometry, and loads. The results showed the Monte Carlo Simulation method to be conservative for high failure probabilities but less so for low probabilities. The Response Surface/Monte Carlo Simulation method explored the impact load–failure probability relationship. Given the uncertainties in material properties and loads in complex structures, a deterministic analysis alone is insufficient. Probabilistic analysis is imperative for extreme loading events and credible structural safety evaluations.

Mathematics↗
DOE OSTI2025

A unified ensemble soil moisture dataset across the continental United States

Abstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions and making recommendations for drought response planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Influence of the Kuroshio Intrusion on Deep Flow Intraseasonal Variability in the Northern South China Sea

Abstract Interactions between the open ocean and marginal seas have been suggested to be critical to the redistribution and dissipation of global energy. Here, we propose a mechanism for the upper open ocean influencing the deep flow in marginal seas that hinges on the formation and propagation of topographic Rossby waves (TRWs). Observations and high‐resolution simulations suggest substantial intraseasonal variability with periods of 5–60 days associated with the deep flow over continental slopes of the northern South China Sea (NSCS). These fluctuations generally account for over 40% of the total deep kinetic energy variability and the number can reach 70% over the slopes to the west of the Luzon Strait, southwest of the Dongsha Islands, and northeast of the Xisha Islands. By examining the spatiotemporal features of the fluctuations, we demonstrate that the intraseasonal variability of the deep flow in the NSCS is closely associated with TRWs. Using a recently developed multiscale energetics analysis in combination with a ray tracing model, we show that the energy sources of TRWs can be traced back to the east of the Dongsha Islands, where the Kuroshio intrusion and related eddies energize the TRWs primarily through pressure work. These waves propagate westward across the NSCS and drive the intraseasonal variability of the deep flow over continental slopes. Our findings highlight an energy pathway from the open ocean western boundary current to the abyssal marginal sea that could modulate regional circulation as well as exchanges between major ocean basins.

Quan, Qi↗
DOE OSTI2022

Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
DOE OSTI2023

Beyond Expected Values Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Electromechanical memcapacitor model offering biologically plausible spiking.

In this article, we introduce a new nanoscale electromechanical device - a leaky memcapacitor - and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movableplate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several types of spiking observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive, leaking resistive, and reset functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.

Zhang, Zixi↗
DOE OSTI2020

A coupled multipoint stress–multipoint flux mixed finite element method for the Biot system of poroelasticity

In this work, we present a mixed finite element method for a five-field formulation of the Biot system of poroelasticity that reduces to a cell-centered pressure–displacement system on simplicial and quadrilateral grids. A mixed stress–displacement–rotation formulation for elasticity with weak stress symmetry is coupled with a mixed velocity–pressure Darcy formulation. The spatial discretization is based on combining the multipoint stress mixed finite element (MSMFE) method for elasticity and the multipoint flux mixed finite element (MFMFE) method for Darcy flow. It uses the lowest order Brezzi–Douglas–Marini mixed finite element spaces for the poroelastic stress and Darcy velocity, piecewise constant displacement and pressure, and continuous piecewise linear or bilinear rotation. A vertex quadrature rule is applied to the velocity, stress, and stress–rotation bilinear forms, which block-diagonalizes the corresponding matrices and allows for local velocity, stress, and rotation elimination. This leads to a cell-centered positive-definite system for pressure and displacement at each time step. We perform error analysis for the semidiscrete and fully discrete formulations, establishing first order convergence for all variables in their natural norms. The numerical tests confirm the theoretical convergence rates and illustrate the locking-free property of the method.

42 ENGINEERING↗
DOE OSTI2025

Cataloging US state policy patterns towards microgrid deployment

Frequent extreme weather events have called for rigorous and timely efforts for alternative non-wire solutions. These efforts are getting more widespread to offer a perfect alternative as the conventional grid becomes progressively less resilient. One of these solutions is microgrids that can disconnect from the grid and offer grid resilience during an outage. While this technology is still finding its footing in the industry, states across the US are employing policy patterns and forms of instruments to support its deployment. This study includes a systemic review of the US by conducting a binary analysis of all 50 states (including Washington D.C, excluding other US territories) using seven variables. The results show four major policy approaches to microgrids: i) supporting microgrids through a definitive legislative activity leading to further policy action; ii) direct efforts from the public utilities commissions without a concrete legislative push; iii) initiatives from institutions other than the commissions; and lastly, iv) self-initiated community and private consumer efforts. The results help understand what policy instruments are being used in each of these patterns to support this niche technology that still faces regulatory challenges.

Furqan, Maham↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2023

An empirical calibration of the serpentine-water oxygen isotope fractionation at T = 25–100°C

The ability to precisely constrain the physical–chemical conditions of serpentinization, such as temperature and fluid source, is limited by the accuracy of calibrations for oxygen isotope fractionation between serpentine and water – i.e., 1000 lnα( serpentine-water ) – which disagree by up to 20‰ when extrapolated to T<200°C. In this study, we present a new empirical calibration of 1000 lnα( serpentine-water ) aiming to improve applications of oxygen isotope geochemistry to very low-T serpentinization (T<100°C). We used the high-spatial resolution capabilities of Secondary Ion Mass Spectrometry (SIMS) to analyze oxygen isotope ratios in mineral pairs of calcite + serpentine, quartz + serpentine and talc + serpentine co-crystallized at scales≤50μm in six serpentinite samples from the Samail ophiolite (Oman). SIMS analysis shows that the mineral pairs are relatively homogenous in oxygen isotope ratios with variability in δ 18 O values ≤2‰ (2s). Clumped isotope thermometry and petrological constraints indicate crystallization temperatures from 25 to 100°C for the investigated samples. These independent constraints on temperature allowed us to derive 1000 lnα( serpentine-water ) by combining Δ 18 O( quartz-serpentine ), Δ 18 O( talc-serpentine ) and Δ 18 O( calcite-serpentine ) measured by SIMS in the investigated samples with Δ 18 O( quartz-water ), Δ 18 O( talc-water ) and Δ 18 O( calcite-water ) calibrations available in the literature. Our empirical calibration of 1000 lnα( serpentine-water ) = 1.04±0.20 (2SE) × 10 6 /T 2 (T in K), from T = 25 to 100°C, is within uncertainty of former high-temperature empirical calibrations extrapolated to T<100°C and matches experimental calibrations when extrapolated to T=250°C. In conclusion, the new serpentine-water calibration enables more accurate reconstructions of fluid-rock interactions occurring during low-temperature serpentinization processes in various tectonic settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Effects of high-dose neutron irradiation at light-water reactor relevant temperature on the mechanical properties of SiC/SiC composites

For this study, the neutron dose-dependent evolutions and the underlying mechanisms of properties of SiC fiber-reinforced SiC matrix (SiC/SiC) composites at a temperature relevant to light-water reactors (∼600 K) were investigated and analyzed. Chemical vapor infiltrated (CVI) SiC/SiC composites reinforced with Hi-Nicalon Type S or Tyranno SA3 fiber were neutron-irradiated to doses up to 30 dpa. The irradiated composites retained their flexural strengths. The thermal diffusivity and dimensional changes were mostly retained from 2.0 to 30.2 dpa. Discrepancies in irradiation responses among CVI SiC/SiC composites from different sources were found. Additional microstructural analysis using Raman spectroscopy and numerical analysis on irradiation effect on residual stress were used to explain how the microstructural variables, especially of carbon interphases, affect the mechanical properties in the 30–40 dpa dose range.

Continuous fiber-reinforced ceramic matrix composi↗
DOE OSTI2021

Use of Physics to Improve Solar Forecast: Physics-Informed Persistence Models for Simultaneously Forecasting GHI, DNI, and DHI

Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (< 1 hour). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM)'s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. Overall, the results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 hours up to 6 hours. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The concentration of BTEX in selected urban areas of Malaysia during the COVID-19 pandemic lockdown

Volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene and xylene (BTEX) are air pollutants that harm human health. This study aims to identify BTEX concentrations before the lockdown known as the Movement Control Order was imposed (BMCO), during the implementation of the Movement Control Order (MCO), and then during the Conditional Movement Control Order (CMCO). These orders were introduced during the COVID-19 pandemic in Malaysia. The study utilised data measured by the continuous monitoring of BTEX using online gas chromatography instruments located at three urban area stations. Here, the results showed that the BTEX concentrations reduced by between –38% and –46% during the MCO compared to the BMCO period. The reduction of human mobility during the MCO and CMCO influenced the lower BTEX concentrations recorded at a station within the Kuala Lumpur area. The results of the BTEX diagnostic ratios and principal component analysis showed that the major source of BTEX, especially during the BMCO and CMCO periods, was motor vehicle emissions. Further investigation, using correlation analysis and polar plots, showed that the BTEX concentrations were also influenced by meteorological variables such as wind speed, air temperature and relative humidity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effects of vessel traffic and ocean noise on gray whale stress hormones

Human use of marinescapes is rapidly increasing, especially in populated nearshore regions where recreational vessel traffic can be dense. Marine animals can have a physiological response to such elevated human activity that can impact individual health and population dynamics. To understand the physiological impacts of vessel traffic on baleen whales, we investigated the adrenal stress response of gray whales (Eschrichtius robustus) to variable vessel traffic levels through an assessment of fecal glucocorticoid metabolite (fGC) concentrations. This analysis was conducted at the individual level, at multiple temporal scales (1–7 days), and accounted for factors that may confound fGC: sex, age, nutritional status, and reproductive state. Data were collected in Oregon, USA, from June to October of 2016–2018. Results indicate significant correlations between fGC, month, and vessel counts from the day prior to fecal sample collection. Furthermore, we show a significant positive correlation between vessel traffic and underwater ambient noise levels, which indicates that noise produced by vessel traffic may be a causal factor for the increased fGC. This study increases knowledge of gray whale physiological response to vessel traffic and may inform management decisions regarding regulations of vessel traffic activities and thresholds near critical whale habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metadynamics investigation of lanthanide solvation free energy landscapes and insights into separations energetics

Lanthanide ion solvation chemistry in nonaqueous phases is key to understanding and developing effective separation processes for these critical materials. Due to the complexity and inherent disorder of the solution phase, a comprehensive picture of the solvated metal ion is often difficult to generate solely from conventional spectroscopic approaches and electronic structure calculations, particularly in the extractant phase. In this work, we use classical molecular dynamics (MD) simulation with an advanced sampling technique, metadynamics, supplemented by experimental spectroscopy and speciation analysis, to measure lanthanide solvation free energy landscapes. We define coordination-based collective variables to probe the entire range of solvation configurations in the organic phase of lanthanum (La), europium (Eu), and lutetium (Lu) nitrate salts bound with a commonly used extractant, N,N′-dimethyl, N,N′-dioctylhexylethoxymalonamide (DMDOHEMA). The known lanthanide extraction trend of La ≈ Eu > Lu is readily explained by the measured free energy surfaces, which show consistent DMDOHEMA coordination from La to Eu, followed by loss of DMDOHEMA coordination from Eu to Lu. These simulations suggest how ligand crowding at the metal center can control selectivity, in this case resulting in the opposite extraction trend as observed with other conventional extractants, where the enthalpic contribution from increasing lanthanide charge density across the series dominates the extraction energetics. We also find that the presence of inner-sphere water, verified by time-resolved fluorescence, diversifies the accessible solvation structures. As a result, understanding solvation requires consideration of an entire thermodynamic ensemble, rather than the single dominant lowest-energy structure, as is often considered out of necessity in interpretation of spectroscopic data or in electronic structure-based ligand design approaches. In general, we demonstrate how metadynamics uniquely enables investigation of complex, multidimensional solvation energetic landscapes, and how it can explain selectivity trends where extraction is controlled by more complex mechanisms than simple charge density-based selectivity.

Wang, Xiaoyu↗
DOE OSTI2024

Modeling of the metal–insulator transition temperature in alio-valently doped VO 2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO 2 ) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO 2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Water alternative gas (WAG) optimization for a heterogeneous Brazilian pre-salt carbonate reservoir

Water alternating gas (WAG) is a cyclical process that involves alternating water and gas injections with the primary goal to improve sweep efficiency by maintaining initial high pressure, slowing water and gas breakthrough, and lowering oil viscosity. The objective of this work is to apply and optimize a WAG strategy on a carbonate field with light oil, compare it to the initially planned water-flooding strategy, and investigate the capability of WAG to improve field production. In this research, a compositional reservoir simulator was used to model a WAG process by injecting produced gas into the reservoir, using the same well structure as an optimized water-flooding strategy. Subsequently, a WAG strategy was created, optimizing the number and locations of wells, to facilitate a comparative analysis of the two recovery methods. The WAG optimization involved a detailed assessment of variables such as bottom hole pressure (BHP), WAG cycle duration, maximum gas oil ratio (GOR), and well positioning, to achieve a high net present value (NPV). The study focuses on the application of WAG optimization modeling in unconventional reservoirs, specifically pre-salt carbonate reservoirs, and investigates its implications on production strategy and forecast, emphasizing its potential for maximizing NPV and oil recovery in a recently producing field. The results showed that WAG improved reservoir performance when compared to water injection and produced a greater amount of oil. This solution showed potential to be tested under uncertainties (reservoir heterogeneity, faults, fractures, karsts, vugs, etc.) as future steps.

54 ENVIRONMENTAL SCIENCES↗