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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

N-body simulations of terrestrial planet formation under the influence of a hot Jupiter

We investigate the formation of multiple-planet systems in the presence of a hot Jupiter (HJ) using extended N-body simulations that are performed simultaneously with semianalytic calculations. Our primary aims are to describe the planet formation process starting from planetesimals using high-resolution simulations, and to examine the dependences of the architecture of planetary systems on input parameters (e.g., disk mass, disk viscosity). We observe that protoplanets that arise from oligarchic growth and undergo type I migration stop migrating when they join a chain of resonant planets outside the orbit of an HJ. The formation of a resonant chain is almost independent of our model parameters, and is thus a robust process. At the end of our simulations, several terrestrial planets remain at around 0.1 AU. The formed planets are not equal mass; the largest planet constitutes more than 50% of the total mass in the close-in region, which is also less dependent on parameters. In the previous work of this paper, we have found a new physical mechanism of induced migration of the HJ, which is called a crowding-out. If the HJ opens up a wide gap in the disk (e.g., owing to low disk viscosity), crowding-out becomes less efficient and the HJ remains. We also discuss angular momentum transfer between the planets and disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

JGCRI/metis

Sub-Regional Nexus Modeling Metis provides a framework allowing analysis of dynamics across multiple sectors including energy, water, land and socioeconomics at variable spatial and temporal scales. It is designed to be a flexible tool which can take inputs from both global integrated assessment models (IAMs) which capture global dynamics as well as more detailed models which capture specific sub-regional and sectoral details at finer resolutions. Metis integrates across tools by sharing data in standardized formats.

Khan, Zarrar↗
DOE OSTI2024

FY24 Report on Water NSTF Testing: Lower Tank Inlet Piping Configuration

The following report serves as a summary of the accomplishments and testing results by the Natural convection Shutdown heat removal Test Facility (NSTF) experimental program over the past 12-month period. A major activity included reconfiguration of the chimney piping geometry which altered the discharge position into the tank from the original 50% tank height to a new lower position at a 10.9% tank height. This modification increases the volume of available coolant thus extending long-term operating capacity, however also results in a decreased liquid driving head which has the potential to reduce the natural circulation efficiency and decrease overall performance. Examination of the tradeoffs for these two configurations is an area of interest for RCCS designers and drove planned test activities this year. Ten matrix tests were performed in FY24, totaling 195 hours of heated operations and 12.9 MWh of electrical heating throughout the year, with eight classified as Accepted per NQA-1 and two classified as Trending. Testing prior to the facility reconfiguration examined the facility response to throttling at the tank inlet, demonstrating increased sensitivity of flow instabilities to throttling within the two-phase region when compared to throttling at the lower sensitivity single-phase inlet region. Testing after the chimney reconfiguration began with a baseline test at conditions of 80% inventory fill and prototypic thermal power input of 2.1 MW t . In addition to establishing a reference for nominal system behavior and performance, repeat testing with multiple subsequent runs demonstrated strong repeatability between tests performed at the same conditions in this new configuration. These tests also examined if influences would occur to system behavior with installation of new higher-resolution instrumentation within the upper chimney. In this critical region where boiling and flashing phenomena dominate, the test results provided confidence that the new instrumentation does not uncharacteristically influence the observed behaviors. An inventory parametric series was then initiated to examine system behavior with the new chimney configuration at six varying initial inventory levels, ranging from high 80% to low 20% fill. Two-phase oscillations with similar peak and mean flow rates were observed when comparing to the previous mid tank configuration. Generally, similar system response trends with inventory were also observed, such as two phase oscillations suppressing as inventories were lowered. However, in one absolute inventory comparison at the highest fill of 80%, flow oscillations saw a gradual growth over the 4 hours of two-phase operation in the new lower tank inlet configuration, opposite of the gradual dampening observed in the mid tank inlet configuration. This can be attributed to, in part, a greater hydrostatic head pressure above the two-phase discharge region where the boiling front is developed. Furthermore, the change resulted in greater sensitivity to liquid degassing phenomena during single-phase heating, causing loop instabilities to form which trigged moderate flow degradation during the period approaching saturation and boiling conditions. This behavior had been observed previously under some conditions but was a common occurrence in recent testing with the newer lower tank inlet configuration. Initial observations from these first data sets suggest an overall larger window of stability for the mid tank inlet configuration compared to the lower tank inlet. Lastly, the lowest inventory fill test was repeated over an extended testing window to examine depletion behavior. Natural circulation flow and effective heat removal performance were observed during most of the testing period; only after the tank became fully drained (0% fill) did flow stagnate and violent geysering events occur. This early observation confirms one relative advantage over the mid tank inlet configuration, which stagnated under comparable conditions at ~20% inventory remaining in the tank.

42 ENGINEERING↗
DOE OSTI2024

Atomic view of photosynthetic metabolite permeability pathways and confinement in synthetic carboxysome shells

Carboxysomes are protein microcompartments found in cyanobacteria, whose shell encapsulates rubisco at the heart of carbon fixation in the Calvin cycle. Carboxysomes are thought to locally concentrate CO 2 in the shell interior to improve rubisco efficiency through selective metabolite permeability, creating a concentrated catalytic center. However, permeability coefficients have not previously been determined for these gases, or for Calvin-cycle intermediates such as bicarbonate (HCO$^–_3$), 3-phosphoglycerate, or ribulose-1,5-bisphosphate. Starting from a high-resolution cryogenic electron microscopy structure of a synthetic β-carboxysome shell, we perform unbiased all-atom molecular dynamics to track metabolite permeability across the shell. The synthetic carboxysome shell structure, lacking the bacterial microcompartment trimer proteins and encapsulation peptides, is found to have similar permeability coefficients for multiple metabolites, and is not selectively permeable to HCO$^–_3$ relative to CO 2 . To resolve how these comparable permeabilities can be reconciled with the clear role of the carboxysome in the CO 2 -concentrating mechanism in cyanobacteria, complementary atomic-resolution Brownian Dynamics simulations estimate the mean first passage time for CO 2 assimilation in a crowded model carboxysome. Despite a relatively high CO 2 permeability of approximately 10 -2 cm/s across the carboxysome shell, the shell proteins reflect enough CO 2 back toward rubisco that 2,650 CO 2 molecules can be fixed by rubisco for every 1 CO 2 molecule that escapes under typical conditions. The permeabilities determined from all-atom molecular simulation are key inputs into flux modeling, and the insight gained into carbon fixation can facilitate the engineering of carboxysomes and other bacterial microcompartments for multiple applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Abstract. Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super-resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super-resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8×10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid-cell areas, super-resolve lognormally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high-resolution simulations and ensembles at a fraction of the computational cost.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM 2.5 concentrations at 1 km resolution in the Eastern United States

To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM 2.5 concentrations. We develop a daily PM 2.5 product at 1 × 1 km 2 spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km 2 MAIAC aerosol optical depth (AOD) data, 36 × 36 km 2 WRF-Chem output, 1 × 1 km 2 land-use type from the National Land Cover Database, and 0.125° × 0.125° ERA-Interim re-analysis meteorology. A gap-filling technique is applied to MAIAC AOD data to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed by clouds or snow cover). The input data are incorporated into a multiple-linear regression model trained to surface observations of PM 2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r 2 = 0.75 using a 10-fold random cross-validation) of daily PM 2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r 2 = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r 2 = 0.52). Daily PM 2.5 output from our statistical model can be easily integrated into county-level epidemiological studies. The novelty of this project is that we are able to simulate PM 2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 × 1 km 2 ) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km 2 ).

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

India National Gas Hydrate Program Expedition 02 Summary of Scientific Results: Numerical Simulation of Reservoir Response to Depressurization

The India National Gas Hydrate Program Expedition 02 (NGHP-02) discovered gas hydrate at high saturation in sand reservoirs at several sites in the deepwater Bay of Bengal. To assess the potential response of those deposits to scientific depressurization experiments, comprehensive geologic models were constructed to enable numerical simulation for two sites. Both sites (NGHP-02-09 and NGHP-02-16) feature thick sequences of thinly-interbedded reservoir and non-reservoir facies at sub-seafloor depths less than 300m and sub-sea depths of 2400m or more. These settings pose significant challenges to current modeling capabilities. First, the thinly-interbedded reservoir architecture complicates the determination of basic reservoir parameters from both log and core data due to measurement resolution issues. Secondly, the fine scale variation in sediment properties imparts great contrasts in key parameters over very short distances, creating high gradients at multiple scales and varying orientations that necessitate careful design of high-definition simulation grids. Thirdly, the deposits include internal sources of water, as well as a range of complex boundary conditions, including variable permeability within the overlying mud-rich “seals,” that complicate reservoir depressurization. Lastly, because of the unique combination of great water depth and relatively shallow sub-seafloor depth: models designed to maximize the dissociation rate impose large pressure drawdowns on relatively low-strength sediments. This condition renders the proper evaluation and integration of the geomechanical response to hydrate dissociation critical. In this report, we review the history of gas hydrate reservoir simulation, discuss methods for creating geologic input models, and summarize the key findings and implications of the collaborative NGHP-02 numerical simulation effort. Together, the studies confirm the viability of the modeled accumulations for scientific testing and identify key challenges related to the selection of specific test sites and the design of test wells.

Boswell, Ray↗
DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗
DOE OSTI2016

Estimating Biofuel Feedstock Water Footprints Using System Dynamics

Increased biofuel production has prompted concerns about the environmental tradeoffs of biofuels compared to petroleum-based fuels. Biofuel production in general, and feedstock production in particular, is under increased scrutiny. Water footprinting (measuring direct and indirect water use) has been proposed as one measure to evaluate water use in the context of concerns about depleting rural water supplies through activities such as irrigation for large-scale agriculture. Water footprinting literature has often been limited in one or more key aspects: complete assessment across multiple water stocks (e.g., vadose zone, surface, and ground water stocks), geographical resolution of data, consistent representation of many feedstocks, and flexibility to perform scenario analysis. We developed a model called BioSpatial H2O using a system dynamics modeling and database framework. BioSpatial H2O could be used to consistently evaluate the complete water footprints of multiple biomass feedstocks at high geospatial resolutions. BioSpatial H2O has the flexibility to perform simultaneous scenario analysis of current and potential future crops under alternative yield and climate conditions. In this proof-of-concept paper, we modeled corn grain (Zea mays L.) and soybeans (Glycine max) under current conditions as illustrative results. BioSpatial H2O links to a unique database that houses annual spatially explicit climate, soil, and plant physiological data. Parameters from the database are used as inputs to our system dynamics model for estimating annual crop water requirements using daily time steps. Based on our review of the literature, estimated green water footprints are comparable to other modeled results, suggesting that BioSpatial H2O is computationally sound for future scenario analysis. Our modeling framework builds on previous water use analyses to provide a platform for scenario-based assessment. BioSpatial H2O's system dynamics is a flexible and user-friendly interface for on-demand, spatially explicit, water use scenario analysis for many US agricultural crops. Built-in controls permit users to quickly make modifications to the model assumptions, such as those affecting yield, and to see the implications of those results in real time. BioSpatial H2O's dynamic capabilities and adjustable climate data allow for analyses of water use and management scenarios to inform current and potential future bioenergy policies. The model could also be adapted for scenario analysis of alternative climatic conditions and comparison of multiple crops. The results of such an analysis would help identify risks associated with water use competition among feedstocks in certain regions. Results could also inform research and development efforts that seek to reduce water-related risks of biofuel pathways.

09 BIOMASS FUELS↗
DOE OSTI2023

Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

Jeske, Torri↗
DOE OSTI2025

HYBRD (High Resolution HYBrid Regional Downscaling) Model: Input data and Code

The HYBRD (HYBrid Regional Downscaling) model is a high-resolution urban land downscaling model that can be used to downscale intermediate urban land use and land cover (LULC) products into a high-resolution (30-meters). HYBRD uses a sequential hybrid process, combining statistical models with cellular-automata-based spatial algorithms. This repository contains all the necessary model code and inputs needed to successfully run HYBRD for Los Angeles, California. The repo also contains example outputs of each model step, except the final simulated raster outputs. Examples of simulated raster outputs for multiple scenarios for Los Angeles are available at DOI: 10.57931/2575233. Please refer to Related Works below.

Land↗
DOE OSTI2018

Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria

Buildings in the developing world are inadequately mapped. Lack of such critical geospatial data adds unnecessary challenges to locating and reaching a large segment of the world’s most vulnerable population, impeding sustainability goals ranging from disaster relief to poverty reduction. Use of volunteered geographic information (VGI) has emerged as a widely accepted source to fill such voids. Despite its promise, availability of building maps for developing countries significantly lags behind demand. We present a new approach, coupling deep convolutional neural networks (CNNs) with VGI for automating building map generation from high-resolution satellite images for Kano state, Nigeria. Specifically, we trained a CNN with VGI building outlines of limited quality and quantity and generated building maps for a 50,000 km 2 area. Resulting maps are in strong agreement with existing settlement maps and require a fraction of the manual input needed for the latter. The VGI-based maps will provide support across multiple facets of socioeconomic development in Kano state, and demonstrates potential advancements in current mapping capabilities in resource constrained countries.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Next Generation Qualification: Guralp Systems Affinity Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a new digitizer, the Affinity, manufactured by Guralp Systems Ltd. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The Affinity digitizer is Guralp's latest release in their digitizer product line. The Affinity is available with either 4 or 8 channels at 24 bit resolution. In addition to the 24 bit channels, 16 multiplexed low resolution channels are provided. Other features include the means to accept multiple types of timing sources (e.g. GPS, NTP and PTP) and a web page interface for command and control of the unit.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach

In this work we develop and present a machine learning based downscaling approach using generative adversarial networks (GANs). GANs learn to distinguish the relationships between low-resolution and high-resolution simulations and generate accurate high-resolution output from low-resolution input (Stengel, Glaws, Hettinger, & King, 2020). Low-resolution numerical weather prediction (NWP) simulations at 9-km spatial and 60-minute temporal resolution were executed over Southeast Asia to provide input to the GANs model. GANs for wind, temperature, and pressure were trained on coarsened WIND Toolkit data with a diverse sampling of terrain and meteorological conditions. After training, the NWP simulations over Southeast Asia were enhanced by 3x along each horizontal spatial dimension and 4x along the temporal dimension. This novel downscaling approach generated 15-year high-resolution wind, temperature, and pressure data from January 2007 through December 2021 at multiple hub heights over Southeast Asia at 3-km spatial and 15-minute temporal resolution with a 16x reduction in compute time over standard dynamical downscaling.

17 WIND ENERGY↗
DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2017

Data-Driven Residential Load Modeling and Validation in GridLAB-D

Accurately characterizing the impacts of high penetrations of distributed energy resources (DER) on the electric distribution system has driven modeling methods from traditional static snap shots, often representing a critical point in time (e.g., summer peak load), to quasi-static time series (QSTS) simulations capturing all the effects of variable DER, associated controls and hence, impacts on the distribution system over a given time period. Unfortunately, the high time resolution DER source and load data required for model inputs is often scarce or non-existent. This paper presents work performed within the GridLAB-D model environment to synthesize, calibrate, and validate 1-second residential load models based on measured transformer loads and physics-based models suitable for QSTS electric distribution system modeling. The modeling and validation approach taken was to create a typical GridLAB-D model home that, when replicated to represent multiple diverse houses on a single transformer, creates a statistically similar load to a measured load for a given weather input. The model homes are constructed to represent the range of actual homes on an instrumented transformer: square footage, thermal integrity, heating and cooling system definition as well as realistic occupancy schedules. House model calibration and validation was performed using the distribution transformer load data and corresponding weather. The modeled loads were found to be similar to the measured loads for four evaluation metrics: 1) daily average energy, 2) daily average and standard deviation of power, 3) power spectral density, and 4) load shape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

The additive volume density law implemented in the Latec workbench to deal with fissile media that contain moderator elements - 14342

The moderation ratio is one of the key parameters for safety assessment in criticality-safety studies. It can be expressed as a moderator to fissile atoms ratio and is related to the concentration of fissile isotopes. The relationship linking these parameters illustrates a physical behaviour of the mixture, which could be very complex depending on the different kinds of media and their associated physical states encountered in the nuclear fuel cycle. To guarantee the safety when modelling a given mixture, criticality-safety practitioners consider either a best estimate approach by fitting experimental data, or, a bounding approach with a fictitious fissile mixture. This theoretical model is generally more penalizing in terms of k{sub eff} and does not involve numeric algorithms for the resolution reducing the overall uncertainty. This is the case for the additive volume density law, which is based on the assumption that the volume of a mixture is the sum of the volumes of all its compounds. This naturally leads to differentiate all the compounds in the mixture and, in particular, the fissile compound and the moderator. A first algorithm has been derived from this approach and implemented in the LATEC criticality workbench developed at IRSN, but a second assumption is required to solve the problem encountered when moderator's elements have also to be used to define the fissile compound. A typical example of this major issue is an UZrH fissile compound moderated by water. The reference moderator element of the water is obviously hydrogen that belongs to the fissile compound. This paper is dedicated to the implementation in the LATEC criticality workbench of an algorithm based on the additive volume density law that is able to deal with such a configuration. The major advantage of this new algorithm is the versatility in terms of input data. The mixture compounds and the moderation can be expressed in multiple ways still using the same algorithm. A validation work has been performed on different UZrH compounds and for different expressions of moderation. The calculated isotopic concentrations have been compared to theoretical calculations using a more straightforward algorithm but that should be adapted for every change in terms of moderation expression. The results obtained are in good agreement with validation results. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗