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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 829 records · Page 46

FAST Model Calibration and Validation of the OC5-DeepCwind Floating Offshore Wind System Against Wave Tank Test Data

During the Offshore Code Comparison Collaboration, Continued, with Correlation (OC5) project, which focused on the validation of numerical methods through comparison against tank test data, the authors created a numerical FAST model of the 1:50-scale DeepCwind semisubmersible system that was tested at the Maritime Research Institute Netherlands ocean basin in 2013. The OC5 project revealed a general underprediction of loads and motions by the participating numerical models. This paper discusses several model calibration studies that were conducted to identify potential model parameter adjustments that help to improve the agreement between the numerical simulations and the experimental test data. These calibration studies cover wind-field-specific parameters (coherence, turbulence), and hydrodynamic and aerodynamic modeling approaches, as well as rotor model (blade-pitch and blade-mass imbalances) and tower model (structural tower damping coefficient) adjustments. These calibration studies were conducted based on relatively simple calibration load cases (wave only/wind only). The agreement between the final FAST model and experimental measurements is then assessed based on more complex combined wind and wave validation cases. The analysis presented in this paper does not claim to be an exhaustive parameter identification study but is aimed at describing the qualitative impact of different model parameters on the system response. This work should help to provide guidance for future systematic parameter identification and uncertainty quantification efforts.

17 WIND ENERGY↗

The role of ballistic erosion and sedimentation in lunar stratigraphy

Aspects of base surge transport are considered along with questions regarding the applicability of base surge transport to lunar sedimentation, the ballistic transport of crater and basin ejecta, Copernicus crater ballistics, and the effects of ejecta impact on preexisting lunar ground. An ejecta emplacement model is discussed and attention is given to the structure of the surface of continuous deposits of craters and basins, the thickness of crater and basin deposits, and the characteristics of impact melts.

Oberbeck, V. R.↗

An investigation of MAGSAT and complementary data emphasizing precambrian shields and adjacent areas of West Africa and South America

There is lack of acceptance of quantitative crustal models by specialists on regional geology because of the problem of east-west banding on MAGSAT anomaly maps. The possibility of some of the Earth's magnetic field being created in the mantle below the Curie isotherm is discussed as well as the need to remove spherical harmonics of degrees less than 15 from the anomaly map. Two dimensional models were produced for northern South America that might be considered as working models. These models suggest that the presumed Amazon River depression is better developed near Belem and in the upper Amazon basin than in the central Amazon basin. An attempt is proposed for separating the core field from the crustal field using a flat-Earth model.

Hastings, D. A.↗

Implementation of hydrologic models at Goddard Space Flight Center

Major watershed simulation models were implemented on the computer system at NASA Goddard Space Flight Center, and their operation was verified. Historical and physiographic data were acquired for two Maryland river basins (Monocasy River above Jug Bridge and Patuxent River near Laurel, Maryland) and the models were calibrated to simulate them. GSFC personnel were instructed in model operation after the models were implemented.

Source record↗

Evaluation of Water and Energy Cycles in Contemporary Atmospheric Reanalyses in US Basins

Global atmospheric reanalyses have become an important tool for climate studies, owing to the inclusion of observation driven model quantities that are not easily observed, if at all. This includes the study of regional water and energy budgets, despite the fact that the physical terms of the budgets do not balance, owing in large part to the influence of assimilated observations. Here, we focus on the regional water and energy budgets for key river basins in the US, related to the Humans and Hydroclimate in the US (H2US, a Global Energy and Watercycle EXchanges, GEWEX, regional hydroclimate project). Regional budgets and their residuals (related to the assimilated observations) from contemporary reanalyses will be evaluated and intercompared to assess the improvements over previous reanalyses and identify their strengths and weaknesses as contributions to H2US. When available, the observational analysis increments will be quantified directly, and as residuals when that information is missing. Early results show improvements over the Mississippi River basin water cycle closure, and reduced analysis increments. However, the energy analysis increments show non negligible seasonality. Results for the Colorado River basin and National Climate Assessment (NCA) regions will also be presented. The results will emphasize the need for reanalyses and models to produce complete budget equation terms.

Michael Bosilovich↗

Soil Moisture and Mesoscale Convective Complex Development During the 1993 US Midwest Flood: Results from the MM5-PLACE Atmosphere/Land-Surface Model

The 1993 US Midwest Flood produced record levels of flooding, in the Mississippi River Basin. This flooding resulted from repeated frontal passages and mesoscale convective complexes (MCCs) during the months of June and July. A better understanding of processes that influenced MCC development during the 1993 Flood may lead to improved forecasts of heavy precipitation and flooding. Here, we consider the impact of soil moisture on MCC development during a two-day period (June 23-24) of the 1993 US Midwest Flood. The purpose of this study is to assess the importance of soil moisture distribution on the timing, intensity, and location of heavy precipitation. In this study, the MM5-PLACE Atmosphere/Land-Surface Model is utilized. The atmospheric component consists of the Penn State/NCAR MM5 mesoscale model, and the land-surface component consists of the Goddard Parameterization for Land Atmosphere-Cloud Exchange (PLACE). Initial soil moisture is provided from two sources: 1) NCEP reanalysis, and 2) Antecedent Precipitation Index (API) using NOAA rain gauge measurements as a proxy for soil moisture. NCEP reanalysis provides coarse resolution initial soil moisture (2.5 degree), while API provides high resolution initial soil moisture (10-200 km depending on NOAA rain gauge spacing). Initial results indicate that the distribution of soil moisture has a significant impact on the timing and location of heavy precipitation during this two-day flood event. Precipitation in simulations with high resolution initial soil moisture agrees more closely with observed precipitation. These results suggest that high resolution soil moisture observations are necessary to accurately predict severe storm development, heavy precipitation, and subsequent flooding.

Baker, R. David↗

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.

54 ENVIRONMENTAL SCIENCES↗

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗

Comparison of MeshGraphNet Techniques for Subsurface Behavior Prediction during CO2 Sequestration

Carbon sequestration is a vital part of the effort to mitigate anthropogenic climate change. Previously, we have shown that Graph Neural Networks (GNNs) provide the ability to extract meaningful insights during prediction of subsurface behavior in carbon storage projects. However, these models have struggled with long-term prediction accuracy due to error accumulation caused by autoregressive prediction. This research leverages the Illinois Basin – Decatur Project (IBDP) dataset to examine strategies for minimizing loss over time in a MeshGraphNet GNN model to improve reliability of predictions while minimizing inferencing time.

Holcomb, Paul↗

Evaluating the economic impact of water scarcity in a changing world

Abstract Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

54 ENVIRONMENTAL SCIENCES↗

Understanding the Flushing Capability of Bellingham Bay and Its Implication on Bottom Water Hypoxia

In this study, an unstructured-grid finite-volume coastal ocean model (FVCOM) was used to simulate hydrodynamic circulation and assess the flushing capability in Bellingham Bay, Washington, USA. The model was reasonably calibrated against field observations for water level, velocity and salinity, and was further used to calculate residence time distributions in the study site. The model results suggest that, despite the large tidal ranges (~4 m during spring tide), tidal currents are relatively weak in Bellingham Bay with surface currents generally below 0.5 m/s. The local residence time in Bellingham Bay varies from to near zero to as long as 15 days, depending on the location and river flow condition. In general, Bellingham Bay is a well-flushed coastal embayment affected by freshwater discharge, tides, wind, and density-driven circulation. The basin-wide global residence time ranges from 5-7 days. The model results also provide useful information on possible causes of the emerging summertime hypoxia problem in the north central region of Bellingham Bay. It was concluded that the formation of the bottom hypoxic water should result from the increased consumption rate of oxygen in the bottom oceanic inflow with low dissolved oxygen by organic matters accumulated at the regions characterized with relatively long residence time in summer months.

Bellingham Bay↗

Capacity assessment and co-optimization of CO 2 storage and enhanced oil recovery in residual oil zones

Residual oil zones (ROZs) are increasingly being commercially exploited using CO 2 -enhanced oil recovery (CO 2 -EOR) method. In this study, CO 2 storage potential, long-term CO 2 fate and oil recovery potential in ROZs are characterized based on a reservoir model for Goldsmith-Landreth San Andres Unit in the Permian Basin. The effects of CO 2 injection rates, well patterns (five-spot and line-drive), well spacings, injection modes (continuous CO 2 injection and water-alternatinggas injection) on the CO 2 retention in the reservoir and the oil production are investigated. After the preliminary assessment of CO 2 storage and EOR potentials in ROZs, we next develop a novel approach based on a newly developed optimization algorithm-Stochastic Simplex Approximate Gradient (StoSAG) and predictive empirical models constructed using machine learning technique to co-optimize CO 2 storage and oil recovery in ROZs. The performance of co-optimization of CO 2 storage and oil recovery is compared with the performance of optimization of only CO 2 storage.

58 GEOSCIENCES↗

USU Alternative and Unconventional Energy Research and Development

The purpose and rationale of this project has been to develop enduring research capabilities at Utah State University (USU) and the Utah State University Research Foundation (USURF) in a number of energy efficient and renewable energy areas including primarily a) algae energy systems, b) solar lighting, c) intuitive buildings, d) electric transportation, 3) unconventional energy environmental monitoring and beneficial reuse technologies (water and CO 2 ), f) wind energy profiling, and g) land use impacts. The long-term goal of this initiative has been to create high-wage jobs in Utah and a platform for sustained faculty and student engagement in energy research. The program’s objective has been to provide a balanced portfolio of R&D conducted by faculty, students, and permanent staff. This objective has been met. While some of the project’s tasks met with more success than others, as with any research project of this scope, overall the research has contributed valuable technical insight and broader understanding in key energy related areas. The algae energy systems research resulted in a highly productive workforce development enterprise as it graduated a large number of well prepared students entering alternative energy development fields and scholarship. Moreover, research in this area has demonstrated both the technological and economic limitations and tremendous potential of algae feedstock-based energy and co-products. Research conducted in electric transportation, specifically in both stationary and dynamic wireless inductive coupling charging technologies, has resulted in impactful advances. The project initiated the annual Conference on Electric Roads and Vehicles (http://www.cervconference.org/), which is growing and attracts more than 100 industry experts and scholars. As a direct result of the research, the USU/USURF spin-out startup, WAVE (Wireless Advanced Vehicle Electrification), continues work in wirelessly charged bus transit systems and is poised to quickly become a multi-million dollar company with clients around the globe. Moreover, USU students and researchers alike are now on the leading edge of the electrified transportation workforce. Finally, the legacy of this DOE investment in electric transportation is continuing at USU with the formation in progress of an industry sponsored research center built around the Electric Roadway and Vehicle (EVR) research facility and test track (http://evr.usu.edu). The research conducted in unconventional energy environmental monitoring and beneficial reuse technologies experienced broad success developing experimental and modeling tools and implementing those tools to better understand environmental impacts of industrial processes used in unconventional energy development in the Utah’s Uintah Basin. Before this project began the USU Uintah Basin branch campus had minimal capability to perform this regionally critical environmental research. This research investment enabled monitoring and modeling equipment and expertise to assess impacts of energy development to all aspects of environmental quality. Laboratory capability for environmental analysis has been developed and engaged along with field testing at multiple locations. Successful campaigns to measure greenhouse gas and hydrocarbon emissions from produced water surface impoundments and leakage from subsurface oil and gas infrastructure were executed. A computer model of meteorological conditions during winter inversion episodes was created, and commercialization efforts are underway for those models. Finally, in the past 24 months, nearly $2 million in non- DOE external funding from state, local, federal and private entities has been awarded to USU Uintah Basin to continue and add to work and capability established by this task. The preceding examples represent a few highlights resulting from the USU Alternative and Unconventional Energy Research and Development project. The following report provides further detail.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Data for Unraveling the Drivers of Water Shortage across Spatial Scales and Sectors in Colorado's West Slope River Basins

Results from Sunkara et al. submitted to Earth's Future. All code to reproduce the experiment and make the figures can be found here: https://github.com/ssaiveena/Sunkara-etal_2025_WestSlope For a detailed guide to data in this repository, see the README.txt file. Shortage output from .xdd files generated by StateMod was compressed into .parquet files in the Uncertain_scenarios folder. Reservoir output from .xre files can be found in the "Reservoir" directory within the Uncertain_scenarios folder. We adopted the CDSS naming convention: cm = Upper Colorado River Basin gm = Gunnison River Basin ym = Yampa River Basin wm = White River Basin sj = Southwest Basin

Climate Change↗

Massively Parallel Assimilation of TOGA/TAO and Topex/Poseidon Measurements into a Quasi Isopycnal Ocean General Circulation Model Using an Ensemble Kalman Filter

A massively parallel ensemble Kalman filter (EnKF)is used to assimilate temperature data from the TOGA/TAO array and altimetry from TOPEX/POSEIDON into a Pacific basin version of the NASA Seasonal to Interannual Prediction Project (NSIPP)ls quasi-isopycnal ocean general circulation model. The EnKF is an approximate Kalman filter in which the error-covariance propagation step is modeled by the integration of multiple instances of a numerical model. An estimate of the true error covariances is then inferred from the distribution of the ensemble of model state vectors. This inplementation of the filter takes advantage of the inherent parallelism in the EnKF algorithm by running all the model instances concurrently. The Kalman filter update step also occurs in parallel by having each processor process the observations that occur in the region of physical space for which it is responsible. The massively parallel data assimilation system is validated by withholding some of the data and then quantifying the extent to which the withheld information can be inferred from the assimilation of the remaining data. The distributions of the forecast and analysis error covariances predicted by the ENKF are also examined.

Keppenne, Christian L.↗

A Crustal Rock Clast in Magnesian Anorthositic Breccia, Dhofar 489 and Its Excavation from a Large Basin

We report the mineralogy and Ar-Ar age of a spinel troctolite clast with a granulitic texture found in the Dofar 489 lunar meteorite. This anothositic breccia contained magnesian mafic silicates not common in ferroan anorthosites (FAN) from the Feldspathic Highlands Terrane (FHT) of Joliff et al. The Ar-Ar ages of most FANs in the Apollo sample collection from the Procellarum KREEP Terrane (PKT) and FHT of the near-side of the Moon were reset at around 3.9 Gyr. by the basin forming event of Imbrium. From the older Ar-Ar age of Dho 489, we propose that a large basin formation other than the Imbrium basin may have mixed deep crustal rocks such as spinel troctolites with "pure" anrthosites to produce a magnesian anorthosite brecca. This model is in line with a proposal by Bussey and Spudis, who reported that inner rings of large basins display massifs of nearly pure anorthosites.

Takeda, Hiroshi↗