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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 181 records · Page 10

Representation of Modes of Variability in 6 U.S. Climate Models

We compare the performance of several modes of variability across six US climate modeling groups, with a focus on identifying robust improvements in recent models (including those participating in the Coupled Model Intercomparison Project (CMIP) Phase 6) compared to previous versions. In particular, we examine the representation of the Madden-Julian Oscillation (MJO), the El Ni˜no/Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Quasi-Biennial Oscillation (QBO) in the tropical stratosphere and the dominant modes of extra-tropical variability, including the Southern Annular Mode (SAM), the Northern Annular Mode (NAM) (and the closely related North Atlantic Oscillation (NAO)), and the Pacific-North American Pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g. the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

Modes of variability↗

Climate-invariant machine learning

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

54 ENVIRONMENTAL SCIENCES↗

Chemical Reactivity Through Adaptive Quantum Mechanics/Many-Body Representations: Theoretical Development, Software Implementation, and Applications (Final Report)

The main objective of this research project was the development and application of a new theoretical/computational framework to model chemical transformations and electronic excitations in fluid mixtures across different phases. Our theoretical/computational framework combines our data-driven many-body (DD-MB) potentials representing molecular interactions with adaptive schemes for modeling chemical reactions in solution. The combination of these two components resulted in an adaptive quantum mechanics/many-body (adQM/MB) method that largely suppresses discontinuities between QM and MM regions, which affect existing QM/MM methods, and thus provides an accurate representation of both quantum mechanical and environmental effects through a rigorous description of mutual polarization between QM and MM regions. The implementation of our DD-MB potentials and adQM/MB method in popular software enabled computer simulations of solvation phenomena, reactive processes, and electronic excitations in fluid mixtures with chemical and spectroscopic accuracy, representing a major step toward realistic computer simulations of a wide range of molecular systems relevant to the DOE mission.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Arctic Black Carbon Aerosol Deposition Study North Slope of Alaska 2020- Sonic Anemometer Measurements

Particles are removed from the atmosphere through both wet and dry deposition. These processes are poorly understood, though they constitute important uncertainties in climate and air quality models. This project aims to use observational constraints on particle fluxes to improve model representations of dry deposition. Within this data set, we measured wind properties using a sonic anemometer. Measurements were made at the meteorological tower between 9 Sept and 25 Oct 2021. This site is a coastal tundra location, and received snow during the project.

Gill Windmaster 3D Ultrasonic Anemometer↗

The Misrepresentation of the Southern African Easterly Jet in Models and Its Implications for Aerosol, Clouds, and Precipitation Distributions

The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, in this work, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.

54 ENVIRONMENTAL SCIENCES↗

Vegetation responses to air dryness amplify future land surface warming

Temperature exerts a first-order control on vegetation photosynthesis and transpiration. Yet most studies investigating temperature impacts on plants rely on near-surface air temperature, rather than canopy temperature—the temperature plants actually experience. Because canopy temperature directly regulates ecosystem function, it provides a more accurate measure of vegetation–climate interactions. Combining Earth System Model (ESM) simulations and satellite observations in a dual emergent constraint, here we show that canopy temperature is projected to increase substantially more than air temperature (~0.11-degrees more or a 16% increase in their difference) over the 21st century. The ESM ensemble median fails to capture these stronger increases in the majority of vegetated regions. We find that the largest projected increases in the difference between canopy and air temperature are predicted to occur in regions where elevating moisture stress—particularly rising vapor pressure deficit—increasingly constrains vegetation growth and transpiration. This implies that future warming will impose stronger constraints on plant function than currently estimated. Relying on air temperature alone will therefore lead to systematic underestimation of temperature effects on photosynthesis, vegetation growth, and the land carbon sink. Accurate representation of canopy temperature in ESMs is thus essential to improve projections of ecosystem responses and feedbacks to climate change.

Green, Julia K↗

Developments in steady and unsteady aerodynamics for use in aeroelastic analysis and design

A review is given of seven research projects which are aimed at improving the generality, accuracy, and computational efficiency of steady and unsteady aerodynamic theory for use in aeroelastic analysis and design. These projects indicate three major thrusts of current research efforts: (1) more realistic representation of steady and unsteady subsonic and supersonic loads on aircraft configurations of general shape with emphasis on structural-design applications, (2) unsteady aerodynamics for application in active-controls analyses, and (3) unsteady aerodynamics for the frequently critical transonic speed range. The review of each project includes theoretical background, description of capabilities, results of application, current status, and plans for further development and use.

Yates, E. C., Jr.↗

Dislocation Patterning in Deforming Crystals: Theory, Computational Predictions and Validation (Final Technical Report)

This project was awarded for an initial period of three years, followed by a no-cost extension for one year and a funded one-year renewal. During the first four years, the project focused on investigating the role of dislocation reactions and dislocation correlation in dislocation patterning in FCC metals. During the fifth year, the scope of research was expanded to investigate the effects of composition inhomogeneity on the mesoscale plastic response of Body-Centered Cubic (BCC) alloys. In addition to its impact on metal hardening during deformation, dislocation patterning provides the microstructure information required to understand phenomena like fracture and recrystallization in metals. The Continuum Dislocation Dynamics (CDD) framework was the methodology used during the first four years, followed by the use of Discrete Dislocation Dynamics (DDD) during the fifth year. CDD is a density-based formalism of dislocation dynamics in which the plastic deformation of crystals is predicted together with the mesoscale dislocation patterns by tracking the space-time evolution of dislocations as driven by the applied stress, short-range and long-range interactions of dislocations, and cross slip. CDD is a crystal mechanics approach in which the plastic constitutive part is replaced with the equations of dislocation dynamics, which is driven by the internal stress via a mobility laws, while the evolution of the density gives the eigenstrain required to update the internal stress itself. The governing equations are thus those of crystal mechanics cast as an eigenstrain problem and those of dislocation transport and reactions. Our investigations during the first four years were driven by the hypothesis that that dislocation patterning is triggered by spatiotemporal dislocation density and internal stress fluctuations, and that such fluctuations influence the collective dislocation dynamic through their effects on the short-range reaction rates and the collective dislocation mobility. This hypothesis was tested by modeling the influence of the dislocation reactions and dislocation correlations on collective dislocation dynamics. The main research components during the first four years were: 1) Reformulation of the CDD framework to integrate the dislocation correlations and dislocation reactions. 2) Simulation of the dislocation correlations and quantifying their contribution to the long-range stress of complex dislocation systems. 3) Computational implementation of the updated CDD model for FCC single crystals and development of an efficient CDD code. 4) Investigation of dislocation patterning in FCC crystals based on the updated CDD model. Some key findings from these investigations were: 1) The patterning of dislocations is initiated by cross slip and internal stress fluctuations, and the refinement of the pattern results from junction formation. 2) Patterning is more prominent under high stress. 3) The correlation stress was found to be a significant part of the mean field stress in continuum representations of dislocation dynamics. During the last year of the project, we have established a method for performing dislocation dynamics in inhomogeneous alloys and demonstrated the impact of composition inhomogeneity on the characteristics of initial yielding and dislocation character in BCC alloys. We also tested this method using an irradiated ferritic alloy in which the composition and dislocation loops effects are both present. In doing so, we have considered two aspects of the role of inhomogeneity, the creation of internal coherency stress due to the dependence of the lattice parameter on the local composition, and the dependence of the dislocation mobility on the local composition. These two aspects resulted in an unexpected behavior of the dislocation system, and, in turn, the yielding behavior of BCC alloys. Key findings include: 1) The composition inhomogeneity in BCC alloys alters the well-known role of screw dislocations by forcing a level of waviness on the average dislocation character, thus destroying the screw character dominance in the yielding observed in pure or homogeneous BCC metals. 2) In the case of irradiated alloys, while composition inhomogeneity by itself and dislocation loops by themselves result in some hardening, the superposition of the two mechanisms does not result in the sum of the two contributions via any known rule. The latter result contradicts the classical works related hardening via multiple mechanisms, which state that various hardening mechanisms can be added linearly or using some Pythagorean additive rule. We were able to rationalize this unexpected result by a closer loop at the origin of the hardening mechanisms themselves, which both involved long-range elastic interactions with dislocations. We reached the conclusion that the hardening resulting from the total stress associated with these two mechanisms (current work) differs from the sum of the effects of the individual stress fields (classical literature). We have also found a major flaw in the classical theory of spinodal hardening. The latter theory never considered the impact of composition undulation on the solute hardening. We have built a new general theory accounting for the effect of solute friction together with the composition undulations on the dislocation configuration at Critical Resolved Shear Stress (CRSS), which is yielding a new definition of the spinodal hardening and new different results. This work is the first to recognize the lack of the role of solute in spinodal hardening theory of alloys.

36 MATERIALS SCIENCE↗

Arctic Black Carbon Aerosol Deposition Study North Slope of Alaska 2020- SP2 Measurements

Particles are removed from the atmosphere through both wet and dry deposition. These processes are poorly understood, though they constitute important uncertainties in climate and air quality models. This project aims to use observational constraints on particle fluxes to improve model representations of dry deposition. Within this data set, we measured refractory black carbon with a single particle soot photometer (SP2) at the ARM facility’s North Slope of Alaska site. Measurements were made at the meteorological tower between 9 Sept and 27 Oct 2021. This site is a coastal tundra location, and received snow during the project.

Black carbon mass concentration↗

Arctic Black Carbon Aerosol Deposition Study North Slope of Alaska 2020- UHSAS Measurements

Particles are removed from the atmosphere through both wet and dry deposition. These processes are poorly understood, though they constitute important uncertainties in climate and air quality models. This project aims to use observational constraints on particle fluxes to improve model representations of dry deposition. Within this data set, we measured fluxes of size resolved particles with an ultrahigh sensitivity aerosol spectrometer (UHSAS) at the ARM facility’s North Slope Alaska site. Measurements were made at the meteorological tower between 9 Sept and 25 Oct 2021. This site is a coastal tundra location and received snow during the project.

Aerosol concentration↗

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

58 GEOSCIENCES↗

Long-term basin-scale hydropower expansion under alternative scenarios in a global multisector model

Abstract Hydropower is an important source of renewable, low-carbon energy. Global and regional energy systems, including hydropower, may evolve in a variety of ways under different scenarios. Representation of hydropower in global multisector models is often simplified at the country or regional level. Some models assume a fixed hydropower supply, which is not affected by economic drivers or competition with other electricity generation sources. Here, we implement an endogenous model of hydropower expansion in the Global Change Analysis Model, including a representation of hydropower potential at the river basin level to project future hydropower production across river basins and explore hydropower’s role in evolving energy systems both regionally and globally, under alternative scenarios. Each scenario utilizes the new endogenous hydropower implementation but makes different assumptions about future low-carbon transitions, technology costs, and energy demand. Our study suggests there is ample potential for hydropower to expand in the future to help meet growing demand for electricity driven by socioeconomic growth, electrification of end-use sectors, or other factors. However, hydropower expansion will be constrained by resource availability, resource location, and cost in ways that limit its growth relative to other technologies. As a result, all scenarios show a generally decreasing share of hydroelectricity over total electricity generation at the global level. Hydropower expansion varies across regions, and across basins within regions, due to differences in resource potential, cost, current utilization, and other factors. In sum, our scenarios entail hydropower generation growth between 36% and 119% in 2050, compared to 2015, globally.

54 ENVIRONMENTAL SCIENCES↗

Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: A modeling study

Seasonality is a key feature of the biosphere and the seasonal dynamics of soil carbon (C) emissions represent a fundamental mechanism regulating the terrestrial–climate interaction. We applied a microbial explicit model—CLM-Microbe—to evaluate the impacts of microbial seasonality on soil C cycling in terrestrial ecosystems. Additionally, the CLM-Microbe model was validated in simulating belowground respiratory fluxes, that is, microbial respiration, root respiration, and soil respiration at the site level. On average, the CLM-Microbe model explained 72% (n = 19, p < 0.0001), 65% (n = 19, p < 0.0001), and 71% (n = 18, p < 0.0001) of the variation in microbial respiration, root respiration, and soil respiration, respectively. We then compared the model simulations of soil respiratory fluxes and soil organic C content in top 1 m between the CLM-Microbe model with (CLM-Microbe) and without (CLM-Microbe_wos) seasonal dynamics of soil microbial biomass in natural biomes. Removing soil microbial seasonality reduced model performance in simulating microbial respiration and soil respiration, but led to slight differences in simulating root respiration. Compared with the CLM-Microbe, the CLM-Microbe_wos underestimated the annual flux of microbial respiration by 0.6%–32% and annual flux of soil respiration by 0.4%–29% in natural biomes. Correspondingly, the CLM-Microbe_wos estimated higher soil organic C content in top 1 m (0.2%–7%) except for the sites in Arctic and boreal regions. Our findings suggest that soil microbial seasonality enhances soil respiratory C emissions, leading to a decline in SOC storage. An explicit representation of soil microbial seasonality represents a critical improvement for projecting soil C decomposition and reducing the uncertainties in global C cycle projection under the changing climate.

59 BASIC BIOLOGICAL SCIENCES↗

Sensitivities of the West Greenland Current to Greenland Ice Sheet Meltwater in a Mesoscale Ocean/Sea Ice Model

Abstract Meltwater from the Greenland Ice Sheet can alter the continental shelf/slope circulation and cross-shelf freshwater fluxes and limit deep convection in adjacent basins through surface freshening. We explore the impacts on the West Greenland Current and eastern Labrador Sea with different vertical distributions of the meltwater forcing. In this study, we present the results from global coupled ocean/sea ice simulations, forced with atmospheric reanalysis, that are mesoscale eddy-active (∼2–3-km horizontal spacing) and eddy-permitting (∼6–7-km horizontal spacing) in the study region. We compare the West Greenland Current in mesoscale eddy-active and eddy-permitting without meltwater to highlight the role of small-scale features. The mesoscale eddy-active configuration is then used to assess the change in the eastern Labrador Sea when meltwater is added to the surface or vertically distributed to account for mixing within fjords. In both simulations with meltwater, the West Greenland and West Greenland Coastal Currents are faster than in the simulation with no meltwater; their mean surface speeds are the highest in the vertical distribution case. In the latter case, there is enhanced baroclinic conversion at the shelf break compared to the simulation with no meltwater. When meltwater is vertically distributed, there is an increase in baroclinic conversion at the shelf break associated with increased eddy kinetic energy. In addition, in the eastern Labrador Sea, the salinity is lower and the meltwater volume is greater when meltwater is vertically distributed. Therefore, the West Greenland Current is sensitive to how meltwater is added to the ocean with implications for the freshening of the Labrador Sea. Significance Statement Our goal is to understand how the flux of freshwater across the West Greenland continental slope into the Labrador Sea is modified by meltwater from the Greenland Ice Sheet. We compare the simulations of the ocean that capture key dynamics along the West Greenland continental slope that have no meltwater, meltwater added to the ocean surface, and meltwater distributed vertically to represent the mixing within fjords. When meltwater is added, the currents along the continental slope are faster, with the greatest increase when meltwater is vertically distributed. In that case, there is enhanced freshening of the Labrador Sea because modified density gradients generate more eddies. Proper representation of the vertical structure of meltwater is important for projecting the impact of freshwater on the subpolar North Atlantic.

58 GEOSCIENCES↗

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗

VotE-Dams: a compilation of global dams' locations and attributes (v1)

This dataset represents a compilation of two global and three USA-specific datasets of dam locations and their attributes. The major hurdle toward developing this compilation was the identification of duplicates within the source datasets, especially given the variable precision of dam location coordinates. The most immediately-useful product in this dataset is a spreadsheet (VotE-Dams_v1.csv) that documents the unique dams found across the datasets, their coordinates, and their ids within the respective source datasets. We do not reproduce the source datasets (GRaND, GOODD, GeoDAR, NID, and EHA) here, but their download locations are provided in the README files ('Overview' tab). Some of the source datasets are provided as shapefiles, which require geospatial data software to open (e.g. QGIS/ArcGIS for graphical display, geopandas for Python, rgdal for R, many others freely available). The provided README documents metadata of the source datasets and provides attribute-linking information (i.e. matches attributes among various source datasets that contain the same, or similar, information but have different names). Note that the README is provided as both .xslx and a collection of .csvs (one per tab in the .xslx file). We suggest using the .xlsx version that preserves images, formatting, and sheets. .xlsx files can be viewed using (free) Google Docs or Microsoft Excel.Finally, we provide Technical Documentation.pdf that describes the procedures used to identify unique and duplicate dams.The title of this dataset refers to our 'Veins of the Earth' (VotE) project, which seeks to provide a flexible, scale-free representation of the Earth's river networks. Dams are a critical component of VotE as they heavily influence flows throughout river networks.

54 ENVIRONMENTAL SCIENCES↗

Graphics Flutter Analysis Methods, an interactive computing system at Lockheed-California Company

An interactive computer graphics system, Graphics Flutter Analysis Methods (GFAM), was developed to complement FAMAS, a matrix-oriented batch computing system, and other computer programs in performing complex numerical calculations using a fully integrated data management system. GFAM has many of the matrix operation capabilities found in FAMAS, but on a smaller scale, and is utilized when the analysis requires a high degree of interaction between the engineer and computer, and schedule constraints exclude the use of batch entry programs. Applications of GFAM to a variety of preliminary design, development design, and project modification programs suggest that interactive flutter analysis using matrix representations is a feasible and cost effective computing tool.

Radovcich, N. A.↗

Landsat analysis of lake quality

The trophic status of a number of inland lakes in Wisconsin has been assessed. The feasibility of using both photographic and digital representations of Landsat imagery was investigated during the lake classification project. The result of the investigation has been a semi-automatic data acquisition and handling system which, in conjunction with an analytical categorization scheme, can be used to classify all the significant lakes in the state.

Scarpace, F. L.↗