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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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243 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transformation rate maps of dissolved organic carbon in the contiguous US

Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and Earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux (P r ), accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive high-resolution P r maps over the contiguous US (CONUS) using SOC data from two different sources: the Harmonized World Soil Database v1.2 (HWSD) and SoilGrids 2.0. Both maps are developed following the same five major steps: (1) selecting independent catchments where observed riverine DOC data are available with reasonable quality; (2) estimating catchment-average SOC for the independent catchments; (3) estimating the P r values for these catchments based on the generic formula and catchment-average SOC; (4) developing a predictive model of P r with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and (5) deriving a national map of P r based on the ML model. For evaluation, we compare the DOC concentration derived using the P r map and the observed DOC concentration values at evaluation catchments. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47 for the HWSD-based model and 0.58 and 0.72 for the SoilGrids-based model, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate sensitivity of P r to different factors are discussed. To illustrate the use of such maps, we derive a riverine DOC concentration reanalysis dataset over CONUS. The two P r maps, robustly derived and empirically validated, lay a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and Earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales, but also hold promises for informing policy decisions related to decarbonization and climate change mitigation. The data presented in this study are publicly available at https://doi.org/10.5281/zenodo.14563816 (Li et al., 2024).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulation of triggering and evolution of ELM by pellet injection in EAST under BOUT++ framework

A BOUT ++ three-field magnetohydrodynamic model is employed to study the triggering and evolution of edge localized mode (ELM) by Li pellets injected along the outer mid-plane in the EAST configuration. The linear simulation shows that compared with a large deposition on the pedestal top (scenario I), a smaller deposition within the steep-gradient pedestal region (scenario II) can stimulate much larger linear growth rates of all-n peeling-ballooning modes (PBMs). The nonlinear simulation shows that there exists a pellet size threshold for ELM triggering for two deposition locations; the threshold for scenario I predicted in the present study matches the EAST observation well. Comparison of the two scenarios reveals that a smaller deposition is sufficient to trigger an ELM in a much shorter time in scenario II, whose ELM size is comparable to that in scenario I. This conclusion confirms previous DIII-D and ASDEX-Upgrade observations, suggesting that the steep-gradient pedestal region is a favorable deposition location for ELM triggering with minimum pellet size. Simulation analyses also find that the positive radial gradient of the hump-like pressure profile in the outer mid-plane induced by the pellet deposition plays a different role in the two scenarios. In scenario I, the force resulting from the gradient hinders the outflow of core plasmas and in return, the perturbation is suppressed from spreading inwards after ELM crashes. In scenario II, with a sizable deposition, the gradient results in another competitive perturbation growth region during the linear phase, thus dispersing the free energy and reducing the efficiency of destabilizing PBMs by pellet injection. The suppressing effect of saturated zonal flow on other modes, the short ELM fast crash phase, and the restricting transport effect of the positive radial pressure gradient work together to constrain the pedestal energy loss, especially when the pellet deposition amount is high.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Overview of stellar nucleosynthesis in explosive environments and recent experimental highlights

Explosive stellar environments such as neutron star mergers, supernovae and X-ray bursts contribute significantly to the synthesis of many chemical elements known in the universe. Understanding the underlying explosion mechanisms and stellar conditions, as well as the observed signatures of chemical elements at a variety of these sites require a considerable effort from the nuclear physics community. An overview of several explosive stellar environments have been summarized, along with brief highlights of recent experimental efforts to better constrain the nucleosynthesis from these environments using recent advances of rare isotope beam facilities and measurement techniques.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Thermal and Well Flow Performance of Closed-Loop Geothermal in the Wattenberg Area: Preprint

Closed-loop geothermal systems provide an alternative to resource-constrained hydrothermal systems and stimulation-intensive enhanced geothermal systems. In this work, we apply the slender-body theory (SBT) model, to simulate the well flow and heat transfer performance of U-loop well designs drilled in the Wattenberg area of the Denver-Julesburg Basin. Three U-loop well patterns are investigated including a single, double, and multi-lateral design. The subsurface within area is characterized by deep, hot (> 200degreesC) igneous/metamorphic basement rock underlying multiple sedimentary formations. The lateral section(s) of the U-loop lies within a target depth of 6 km where temperatures are estimated to approach 300degreesC. As a base case, conduction-only heat transfer is investigated through simulations with the SBT model within U-loops with open-hole laterals that exchange heat directly with the hot dry rock using water as a working fluid. The utilization of supercritical CO2 as a heat transfer fluid is also considered. For each scenario, the system performance in terms of annual heat production and temperature profile over a 20-year project lifetime are assessed. Also, the levelized costs of heat and electricity (LCOH and LCOE) are determined using a top-down technoeconomic analysis model. The results show that the performance and cost optimized U-loop design is one having an injection-production well spacing of 1,000 meters with ten 50-meter-spaced laterals that traverse a subsurface system with a temperature gradient of 60degreesC/km. By injecting 20 degreesC-water at a rate of 60 kg/s through this loop, an average heat production of 19 MWth can be achieved, resulting in an LCOE and LCOH of $136/MWh and $1.53/GJ, respectively, over a 20-year project life.

closed-loop geothermal↗
DOE OSTI2025

Thermal and Well Flow Performance of Closed-Loop Geothermal in the Wattenberg Area

Closed-loop geothermal systems provide an alternative to resource-constrained hydrothermal systems and stimulation-intensive enhanced geothermal systems. In this work, we apply the slender-body theory (SBT) model, to simulate the well flow and heat transfer performance of U-loop well designs in the Wattenberg area of the Denver-Julesburg Basin. Three U-loop well patterns are investigated, including a single-, double-, and multi-lateral design. The subsurface within the area of interest is characterized by deep, hot (> 200 degrees C) igneous/metamorphic basement rock underlying multiple sedimentary formations. The lateral section(s) of the U-loop lie(s) within a target depth of 6 km, where temperatures are estimated to approach 300 degrees C. As a base case, conduction-only heat transfer is investigated through simulations with the SBT model within U-loops with open-hole laterals that exchange heat directly with the hot, dry rock using water as a working fluid. The utilization of supercritical CO2 as a heat transfer fluid is also considered. For each scenario, the system performance in terms of annual heat production and temperature profile over a 20-year project lifetime are assessed. Also, the levelized costs of heat and electricity (LCOH and LCOE) are determined using a top-down techno-economic analysis model. The results show that the performance- and cost-optimized U-loop design is one having an injection-production well spacing of 1,000 meters with ten 50-meter-spaced laterals that traverse a subsurface system with a temperature gradient of 60 degrees C/km. By injecting 20 degrees C-water at a rate of 60 kg/s through this loop, an average heat production of 19 MWth (i.e., 2.2 MWe net plant output) can be achieved, resulting in an LCOE and LCOH of $136/MWhe and $1.53/GJ, respectively, over a 20-year project life.

closed-loop geothermal↗
DOE OSTI2026

TDCOSMO XXIII. Measurement of the Hubble constant from the doubly lensed quasar HE 1104−1805

Time-delay cosmography leverages strongly lensed quasars to measure the Universe’s current expansion rate, H 0 , independently from other methods. The latest TDCOSMO milestone measurement primarily used quadruply lensed quasars for their mass profile constraints. However, doubly lensed quasars, being more abundant and offering precise time delays, could expand the sample by a factor of 5, significantly advancing towards a 1% precision measurement of H 0 . We present the first TDCOSMO analysis of a doubly imaged source, HE 1104−1805, including the measurement of the four necessary ingredients. First, by combining 17 years of data from the SMARTS, Euler, and WFI telescopes, we measured a time delay of 176.3 +11.4 −10.3 days. Second, using MUSE data, we extracted stellar velocity dispersion measurements in three radial bins with 5% to 13% precision. Third, employing F160W HST imaging for lens modelling and marginalising over various modelling choices, we measured the Fermat potential difference between the images. Fourth, using wide-field imaging, we measured the convergence added by objects not included in the lens modelling. By combining these four ingredients, we measured the time delay distance and the angular diameter distance to the deflector, favouring a power-law mass model over a baryonic and dark matter composite model. The measurement was performed blindly to prevent experimenter bias and resulted in a Hubble constant of H 0 = +5.8 −5.0 × λ int km s −1 Mpc −1 , where λ int is the internal mass sheet degeneracy parameter. This is in agreement with the TDCOSMO-2025 milestone and its precision for λ int = 1 is comparable to that obtained with the best-observed quadruply lensed quasars (4–6%). This work is a stepping stone towards a precise measurement of H 0 using a large sample of doubly lensed quasars, supplementing the current sample. The next TDCOSMO milestone paper will include this system in its hierarchical analysis, constraining λ int and H 0 jointly with multiple lenses.

cosmological parameters↗
DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗
DOE OSTI2025

Advanced Fuels Campaign Time-at-Temperature Irradiation Campaign and Post-Irradiation Examination Plan

With increasing per capita energy consumption and rapidly growing energy consumption predicted for data centers, the US faces challenges in meeting projected energy demands. The current administration is attempting to accelerate the deployment of new nuclear construction by providing significant investments in advanced nuclear and by modernizing regulatory approaches for the licensing new reactors. However, enabling extended power uprates (EPUs) for the current nuclear reactor fleet would provide a shorter-term solution to better calibrate near-term energy generation capacity to the ever-growing energy demands of the modern era. One pathway to achieve power uprates is to increase the operational window by reassessing fuel safety limits around anticipated transients. Existing light-water reactors (LWRs) utilize a targeted operational window with well-defined operational efficiencies, yet this window is also often bound by conservative safety limits that result in suboptimal operational performance. For example, the operation of the existing fleet is highly constrained by safeguards to operation related to anticipated operational occurrences (AOOs), which are moderate-frequency transients expected with a frequency greater than 0.01 per reactor-year. These AOOs include temperature excursions where the critical heat flux for the system is exceeded, resulting in a departure from nucleate boiling (DNB) at the cladding surface. This DNB event is a thermohydraulic condition that, with current conservative fuel safety limits, results in the fuel rods being classified as “failed,” meaning that the cladding may not be returned to service.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Global Methane Budget 2000–2020

Abstract. Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget. We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024).

54 ENVIRONMENTAL SCIENCES↗