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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sensitivity of Near-Surface Variables in the RUC Land Surface Model in the Weather Research and Forecasting Model

In this study, we investigate the parametric sensitivity of near-surface variables, such as sensible heat flux, latent heat flux, ground heat flux, hub-height wind speed and land surface temperature, to the parameters used in the Rapid Update Cycle (RUC) land surface model (LSM) during a wintertime and summertime period. The model simulations are compared with observations collected from the second Wind Forecast Improvement Project (WFIP2) field campaign. The results suggest that parameters related to snow/ice and thermal processes can have significant impact on the simulated near-surface variables. Out of the 11 examined parameters, only 6 of them have considerable influences on the model behaviors and explain about 60 ~ 80 % of the estimated total variance of the simulated variables. In addition, the magnitude of the parametric sensitivity varies with season. For instance, parameters associated with snow/ice processes are dominant during the wintertime whereas those associated with thermal processes are more important during the summertime. Furthermore, the impact of the identified parameters on the simulated variables is highly related to the topography. There is a high degree of sensitivity to the parameter values over the slope region. This points out the importance of collecting field observations over steep areas to better quantity the appropriate values of key parameters. Overall, our findings provide a better understanding of the RUC LSM behavior associated with parameter uncertainties and can be used to improve the forecasting skill of land surface processes via calibration of the most uncertain model parameters.

17 WIND ENERGY↗
DOE OSTI2023

Mitigation-Aware Bidding Strategies in Electricity Markets

Market power exercise in the electricity markets distorts market prices and diminishes social welfare. Many markets have implemented market power mitigation processes to eliminate the impact of such behavior. The design of mitigation mechanisms has a direct influence on investors' profitability and thus mid-/long-term resource adequacy. In order to evaluate the effectiveness of the existing market power mitigation mechanisms, this paper proposes a mitigation-aware strategic bidding model and studies the bidding strategies of the market participants under current practice. The proposed bidding model has a bilevel structure with strategic participant's profit maximization problem in the upper level and the dispatch problem for market operators in the lower level. In particular, the consideration of potential offer mitigation is incorporated as upper-level constraints based on the conduct and impact tests. This bilevel problem is reduced to a single-level mixed-integer linear program using the KKT optimality conditions, duality theory, and linearization. Numerical results illustrate how a strategic player can exercise market power to achieve a higher profit even under the current market power mitigation process and we analyze the social impact that the market power exercise results.

Wu, Yiqian↗
DOE OSTI2025

Thermodynamic and Kinetic Mechanisms Governing the Synthesis of Nickel-Poor Cathodes

A deeper understanding of the thermodynamics and kinetics governing the lithiation and layering mechanisms of NMC cathode materials (LiNixMnyCozO 2 , where x + y + z = 1) offers valuable insights for enhancing synthesis methods and improving cathode performance. By employing atomistic and mesoscale approaches informed by in situ powder X-ray diffraction (PXRD) experiments, critical parameters for comprehending lithiation and layering processes and reaction rates were identified. The mesoscale approach captured the evolution of the phases and crystallite size observed in the in situ PXRD, revealing the differences in reaction rates with the use of different lithium salts and starting precursors. Ab initio molecular dynamics (AIMD) underscored the importance of vacancies and structural defects in promoting ion mobility and facilitating the nucleation of a layered domain. This nucleation disrupts the symmetry of disordered phases, ultimately creating a strained phase that serves as a buffer between layered and disordered regions. The lithiation and layering processes reflect a dynamic balance between the thermodynamic drive for a low-energy layered structure and the kinetic of diffusion, which is influenced by temperature and lithium vacancy concentration. Overall, reaction mechanisms are driven by the inherent defects of the intermediate phase that differ for NMC cathode materials. The lithium salts impact the rates of lithiation and layering, with a much slower process for Li 2 CO 3 .

25 ENERGY STORAGE↗
DOE OSTI2022

Global distribution, formation and fate of mineral‐associated soil organic matter under a changing climate: A trait‐based perspective

Abstract Soil organic matter (SOM) is the largest actively cycling reservoir of terrestrial carbon (C), and the majority of SOM in Earth's mineral soils (~65%) is mineral‐associated organic matter (MAOM). Thus, the formation and fate of MAOM can exert substantial influence on the global C cycle. To predict future changes to Earth's climate, it is critical to mechanistically understand the processes by which MAOM is formed and decomposed, and to accurately represent this process‐based understanding in biogeochemical and Earth system models. In this review, we use a trait‐based framework to synthesize the interacting roles of plants, soil micro‐organisms, and the mineral matrix in regulating MAOM formation and decomposition. Our proposed framework differentiates between plant and microbial traits that influence total OM inputs to the soil (‘feedstock traits’) versus traits that influence the proportion of OM inputs that are ultimately incorporated into MAOM (‘MAOM formation traits’). We discuss how these feedstock and MAOM formation traits may be altered by warming, altered precipitation and elevated carbon dioxide. At a planetary scale, these feedstock and MAOM formation traits help shape the distribution of MAOM across Earth's biomes, and modulate biome‐specific responses of MAOM to climate change. We leverage a global synthesis of MAOM measurements to provide estimates of the total amount of MAOM‐C globally (~840–1540 Pg C; 34%–51% of total terrestrial organic C), and its distribution across Earth's biomes. We show that MAOM‐C concentration is highest in temperate forests and grasslands, and lowest in shrublands and savannas. Grasslands and croplands have the highest proportion of soil organic carbon (SOC) in the MAOM fraction (i.e. the MAOM‐C:SOC ratio), while boreal forests and tundra have the lowest MAOM‐C:SOC ratio. Drawing on our trait framework, we then review experimental data and posit the effects of climate change on MAOM pools in different biomes. We conclude by discussing how MAOM is integrated into soil C models, and how feedstock and MAOM formation traits may be included in these models. We also summarize the projected fate of MAOM under climate change scenarios (Representative Concentration Pathways 4.5 and 8.5) and discuss key model uncertainties. Read the free Plain Language Summary for this article on the Journal blog.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Earth has humans, so why don’t our climate models?

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. In this work, we describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scalar Flux Profiles in the Unstable Atmospheric Surface Layer Under the Influence of Large Eddies: Implications for Eddy Covariance Flux Measurements and the Non‐Closure Problem

How convective boundary-layer (CBL) processes modify fluxes of sensible (SH) and latent (LH) heat and CO 2 (F c ) in the atmospheric surface layer (ASL) remains a recalcitrant problem. Here, large eddy simulations for the CBL show that while SH in the ASL decreases linearly with height regardless of soil moisture conditions, LH and F c decrease linearly with height over wet soils but increase with height over dry soils. This varying flux divergence/convergence is regulated by changes in asymmetric flux transport between top-down and bottom-up processes. Such flux divergence and convergence indicate that turbulent fluxes measured in the ASL underestimate and overestimate the “true” surface interfacial fluxes, respectively. While the non-closure of the surface energy balance persists across all soil moisture states, it improves over drier soils due to overestimated LH. The non-closure does not imply that F c is always underestimated; F c can be overestimated over dry soils despite the non-closure issue.

Geology↗
DOE OSTI2023

X-Ray Scattering Data Processing and Analysis

Semicrystalline polymers have a degree of crystallinity that depends on chain structure and crystallization conditions. Thermomechanical properties are influenced by the degree of crystallinity and crystalline morphology. A common method used to understand crystalline content, crystal and amorphous layer thickness, and layer thickness distributions, is X-ray scattering (XS). This report uses XS to first investigate crystalline orientation in high density polyethylene (HDPE) samples. Then, a single orientation is analyzed via XS in a temperature study to investigate crystalline behavior under changing temperatures. Lastly, block copolymer filaments extruded at varying speeds are analyzed. The X-ray data are used to gain a baseline understanding of crystalline behavior in these unique filaments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2025

Fault Network Geometry Modulates Earthquake Source Spectra Across Scales

Earthquake source spectra provide unique insights into the earthquake rupture process. Motivated by previous research suggesting that complex fault geometries enhance high‐frequency seismic radiation, we study the influence of fault network geometry on earthquake source spectra using multiple independent observations. At regional scales, we examine correlations of stress drop measurements with surface fault trace misalignment in Southern California, Japan, and Central Italy. At a global scale, we examine correlations of moment‐rate function complexity of large earthquakes with focal mechanism variability, a proxy for local fault complexity. Despite significant scatter in the observations, we find overall consistent positive correlations. The concept that elastic interactions of discrete fault structures during the earthquake rupture process generates high‐frequency ground motions offers a coherent framework for interpreting our observations. These findings suggest that variations in fault complexity explain why some earthquakes produce stronger high‐frequency ground motions than others.

Lee, Jaeseok [Brown Univ., Providence, RI (United ↗
DOE OSTI2025

Harnessing Machine Learning to Predict MoS 2 Solid Lubricant Performance

Physical vapor deposited (PVD) molybdenum disulfide (MoS 2 ) solid lubricant coatings are an exemplar material system for machine learning methods due to small changes in process variables often causing large variations in microstructure and mechanical/tribological properties. Here, in this work, a gradient boosted regression tree machine learning method is applied to an existing experimental data set containing process, microstructure, and property information to create deeper insights into the process-structure–property relationships for molybdenum disulfide (MoS 2 ) solid lubricant coatings. The optimized and cross-validated models show good predictive capabilities for density, reduced modulus, hardness, wear rate, and initial coefficients of friction. The contribution of individual deposition variables (i.e., argon pressure, deposition power, target conditioning) on coating properties is highlighted through feature importance. The process-property relationships established herein show linear and non-linear relationships and highlight the influence of uncontrolled deposition variables (i.e., target conditioning) on the tribological performance.

MoS2↗
DOE OSTI2020

Topographic Controls on the Distribution of Summer Monsoon Precipitation over South Asia

In this work, we use a high-resolution (9 km) one-way nested regional climate modeling framework to systematically evaluate individual and aggregate roles of various components of topography in the precipitation distribution during South Asian Summer Monsoon (SASM). While it is not the arrival but the north-northwestward extent of summer monsoon precipitation that is regulated by the presence of elevated surfaces in South Asia, both the thermal and mechanical topographic forcing play a role. Topography not only provides mechanical uplifting and prevents dry air entrainment, it also helps regulate the strength of upper level anticyclone and tropical easterly jet, all of which are necessary to sustain a conducive environment for monsoon precipitation in the South Asian summer. In turn, precipitation feedbacks positively by accelerating the tropospheric diabatic warming through atmospheric latent heating and by maintaining the moisture supply through local recycling. Modifications in the topography, particularly the removal of the Tibetan Plateau, substantially modulates the moist flow in the lower troposphere and the anticyclonic circulation in the upper troposphere to an extent that the northern branch of SASM becomes substantially weaker. These changes give way to negative precipitation anomalies and eventually to an environment where the cycle of feedbacks that runs the process of monsoon progression over land becomes inefficient. The design of topographic modification experiments can influence precipitation response; therefore, we advise caution in the interpretation of these and previously reported results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Co-Based superalloy morphology evolution: A phase field study based on experimental thermodynamic and kinetic data

Cobalt-based superalloys with two phase $\gamma$/$\gamma '$ microstructures offer great promise as candidates for next-generation high-temperature alloys for applications, such as turbine blades. It is essential to understand the thermodynamic and kinetic factors that influence the microstructural evolution of these alloys in order to optimize the alloy compositions and processing steps with a goal to improve their coarsening, creep and rafting behavior. We are using a continuum phase field approach to study the diffusion process and to predict the equilibrium shapes of Co-Al-W $\gamma '$ precipitates. In order to obtain quantitatively predictive capabilities, we extract chemical free energies for the $\gamma$/$\gamma '$ phases based on CALculation of PHAse Diagrams (CALPHAD) thermodynamic data and diffusion mobilities for Co alloys based on CALPHAD kinetic data. We also use experimental or first-principles data for other quantities, such as misfit strain and interface information, for the parameterization of our model. A particular focus of our study is to understand how different energy balances, misfit strain and kinetics affect the coarsening and rafting behavior of $\gamma '$ precipitates, and the sensitivity of the final precipitate shape to materials parameters. Here, we find that the equilibrium shape of the precipitate results from a delicate competition between chemical, interfacial, and elastic energies, and it is very sensitive to changes in model parameters. Here, we examine how modeling input parameters affect the equilibrium shape of precipitates and relate these parameters to experimentally available values.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic coevolution of baseflow and multiscale groundwater flow system during prolonged droughts

Field and numerical studies suggest that baseflow is composed of waters from a spectrum of groundwater flow paths termed the Groundwater Flow System (GWFS) – from shallow hillslope contributions to watershed-scale deep circulation originating in headwaters and discharging into lowland rivers. Here, we explore the evolution of the GWFS under prolonged droughts to understand its dynamics and multiscale nature, and to elucidate its role in baseflow generation and recession at the watershed scale. In this work, we consider three drought scenarios of varying severity and simulate groundwater flow in a 2-D cross-section of an idealized watershed with deep permeable bedrock, tracking the evolution of flow paths, baseflow, and residence times during the recession process. We find that baseflow generation at different drainage stages, and within different subwatersheds, is influenced distinctly by flow paths of different scales, depending on the relative strength of the flow paths and the position of the subwatersheds relative to the recharge/discharge zones of the deeper watershed-scale groundwater circulation. Despite having the same local relief, geology, and climate, baseflow from each subwatershed has a distinct recession behavior and time-dependent residence time distribution. Also, the hydraulic and transport characteristics of baseflow generation co-evolve and are strongly affected by the connection state of the water table to subwatersheds. These findings suggest that asynchrony and dissimilarity of baseflow generation from hillslopes under the impact of the watershed-scale groundwater flow, and interactions with local-scale and intermediate-scale groundwater flow, must be taken into account when interpreting baseflow recession data and building conceptual baseflow models at the watershed scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Biogeochemical Controls on Wood Degradation as a Source of Bioavailable Carbon in Denitrifying Bioreactors

Woodchip bioreactors (WBRs) are important tools for the removal of nitrate in agricultural drainage, but their effectiveness is often limited by the slow degradation of lignocellulosic wood residues into bioavailable forms of carbon that fuel denitrifying microorganisms. Here, we examine biogeochemical factors regulating wood degradation in saturated woodchip beds, with a focus on the effects of dissolved oxygen (DO), iron (Fe), and manganese (Mn) in generating oxidative activity that can enhance wood decomposition. Woodchips from a 10-year-old WBR were characterized with bulk techniques and a novel combination of μX-ray scattering, μXRF, and μXANES to visualize the depletion of crystalline cellulose as a proxy for wood degradation. Woodchips from upstream portions of the reactor exhibited the greatest degradation, probably due to greater DO exposure, and degradation was localized to a 100 to 200 μm thick surface layer that was also associated with higher concentrations of Fe and Mn. Greater degradation was associated with faster nitrate removal. μXANES analysis of Fe and Mn in the surface layer indicated the presence of a microenvironment in which oxygenation reactions of Fe(II) and Mn(II) could contribute to the formation of reactive oxidants involved in the degradation of lignocellulose. In conclusion, our results provide new insight into biogeochemical properties that influence wood decomposition in WBRs at both micro- and macroscales and how these wood degradation processes are coupled with denitrification.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Pyrite (001) Interface Chemistry is Controlled by a Sulfoxy Termination

Pyrite (FeS 2 ) is the most common sulfide mineral on Earth, forming through inorganic reactions in the crust and oceanic hydrothermal systems and via microbially driven processes in anaerobic sediments. The pyrite–water interface is the site of a wide range of adsorption and reaction processes in Earth systems including oxidation that dramatically affects the geochemistry of surface waters and influences global carbon and oxygen cycles. Mechanistic geochemical models of pyrite interfacial reactivity, however, are limited by the lack of experimentally derived atomistic structures of the reduced and reacting surfaces. Here, in this work, we reveal the atomic-scale structure of the pyrite (001)-water interface that forms at very low oxygen partial pressures, relevant to suboxic environments in Earth. The interface structure and surface speciation were obtained using the crystal truncation rod method supported by ambient-pressure photoelectron spectroscopy and density functional theory calculations. The surface is dominantly composed of disulfide groups bound to a single oxygen atom, forming a sulfoxy group that has no known molecular or bulk mineral analog. This surface is interpreted as the first step in the oxidative dissolution of pyrite. The sulfoxy group is readily protonated through surface acid–base reactions that alter the structure of interfacial water and the free energy of interfacial reactions. Surface iron sites are not oxidized. Surprisingly, this interface can likely develop in equilibrium with bulk pyrite in some reducing and acidic solutions. This termination is therefore likely representative of pyrite surfaces under a vast range of experimental, industrial and Earth conditions.

oxidation↗
DOE OSTI2024

SEI Formation and Lithium-Ion Electrodeposition Dynamics in Lithium Metal Batteries via First-Principles Kinetic Monte Carlo Modeling

The stabilization and enhanced performance of lithium metal batteries (LMBs) depend on the formation and evolution of the Solid Electrolyte Interphase (SEI) layer as a critical component for regulating the Li metal electrodeposition processes. This study employs a first-principles kinetic Monte Carlo (kMC) model to simulate the SEI formation and Li + electrodeposition processes on a lithium metal anode, integrating both the electrochemical electrolyte reduction reactions and the diffusion events giving place to the SEI aggregation processes during battery charge and discharge processes. The model replicates the competitive interactions between organic and inorganic SEI components, emphasizing the influence of the cycling regime. Results indicate that grain boundaries within the SEI facilitate faster lithium-ion transport compared to crystalline regions, crucial for improving the performance and stability of LMBs. The findings underscore the importance of dynamic SEI modeling for further development of next-generation high-energy-density batteries.

25 ENERGY STORAGE↗