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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data and Scripts Associated with "Modeling Ecohydrological Responses of Vegetation to Urban Microclimates Using the E3SM Land Model"

This dataset supports the study of vegetation ecohydrological responses to urban microclimates using the land component of the Energy Exascale Earth System Model (ELM) at four urban sites in Knoxville, Tennessee, USA. It includes the model inputs, simulation outputs, and associated scripts for running ELM simulations and analyzing the resulting data. The Model_Inputs folder includes static surface data, satellite-derived phenology (i.e., leaf area index), and atmospheric forcing data used to drive ELM simulations. Detailed descriptions of these datasets are provided in Section 2.3.2 of the associated manuscript. The Model_Outputs folder contains simulation results for the baseline, treatment, and ensemble experiments. Outputs from the baseline and treatment simulations are provided as raw ELM NetCDF files. Because the raw outputs from the 4,000-member ensemble are prohibitively large, the ensemble results are provided as summarized CSV files, which also serve as the source data for Figure 5 of the associated manuscript. The Scripts folder contains three components: E3SM, the core codebase of the Energy Exascale Earth System Model (E3SM); elm-olmt, the Offline Land Model Testbed (OLMT) used to perform the simulations; and knoxville_elm, which contains the analysis scripts used to process model outputs and generate the figures and results presented in the associated manuscript. Additional information is provided in Scripts_readme.txt within the Scripts directory.

Lu, Xiaoman [ORNL] (ORCID:0000000306698780)↗
DOE OSTI2024

Urban morphology and urban water demand evolution in the Los Angeles region

Detailed description of the dataset sources used in this study, the experimental workflow, and plotting for the paper figures provided at the associated GitHub Meta Repo: https://github.com/IMMM-SFA/Ferencz_et_al_2024_ERL The future water demand projections from this study are hypothetical future water demands that reflect the population and urban land cover changes represented by the scenarios considered. The intent and emphasis of this work is investigating the interactions between population change, evolution of urban morphology, and water demand. These projections are not meant to be likely future demands for specific water providers or the LA region and should not be interpreted as such. The folders contain input and output data for each step of the "Recreate my Experiment" workflow described in the associated GitHub meta-repository as well as data used for plotting Figures for the paper that this dataset supports. Description of each folder's contents and use: Step_1a: All necessary inputs to the associated python script provided on the GitHub repo. Step_1b: All necessary inputs (downscaled population rasters) used by the associated python script provided on the GitHub repo. Original 1-km squared rasters that were downscaled also provided. Step_1c: Urban growth projection rasters corresponding to SSP3 and SSP5 population scenarios are provided in separate subfolders as well as the water provider boundaries used for analysis. Outputs of data processing also provided. Associated python script provided on GitHub. Step_1d: Description of Inputs used by the QGIS Model Builder GUI that automates geospatial processing and clipping the of the high resolution land cover data for each urban land class footprint within a defined polygon boundary. The Model Builder is provided on the GitHub repo and can be used by QGIS. The outputs of this step are in "Clipped Provider Hi Res Landcover". If the user wants to use The Model Builder for different regions of LA or two test our outputs, they will need to download the hi resolution landcover raster listed in the Readme and in Ref [2] of the GitHub Page. Step_1e: All necessary inputs to generate average monthly demand for each water provider. Associated python script on GitHub. Step 2: Output data about land cover metrics (areas and fractions) for each urban land class for each water provider. Associated python script on GitHub. Uses outputs from Step 1d "Clipped Provider Hi Res Landcover" Step 3: Inputs for and Outputs from the urban projection raster analysis Python script on GitHub. The outputs are rasters of urban pixels that were converted to a higher land class and the number of land class units that changed (Values of 1, 2, or 3). For example, a value of 2 could be LC 21 -> 23 or LC 22 -> 24. These maps are label "intensification." The other outputs are "urban growth" rasters showing the conversion of non urban to urban land, which are indicated by pixel values of 1. Step 4: Output projections of indoor and outdoor annual and monthly demands for each water provider. These are used for Figures 4 - 7 of the paper. Figures: This folder has data used for plotting Figures 1 through 5. Data for Figures 6 and 7 are sourced directly from folders associated with the Processing and Analysis Steps 1 - 4 and the plotting scripts for Figures 6 and 7 are commented with what folder paths are needed to generate the figures. The GitHub page provides descriptions of how each figure was made and the associated plotting scripts used.

Los Angeles↗
DOE OSTI2020

Matrix converter operating in current control mode using feed forward signals

A matrix converter system having a current control mode operation is provided. The system includes a matrix converter having a switching matrix. The matrix converter is coupled at its low-voltage side to a generator and at its output load side to a load. A controller having a pulse width modulation (PWM) control circuit is configured to control the matrix converter via its switching matrix to increase energy within the internal inductances of the generator when the switching matrix causes a short circuit. A feed forward calculator is configured to calculate a feed forward output phase angle. The feed forward output phase angle is an estimation of an angle between an output current vector and an output voltage vector that represent feedback signals of current and voltage output by the matrix converter. The angular position of the voltage output vector is adjusted as a function of the feed forward output phase angle to align angular position or phase angle of the voltage output vector that represents the voltage output with a selected angular position or phase angle.

42 ENGINEERING↗
DOE OSTI2024

Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets

This repository contains simulated outputs from superwell – a hydro-economic tool for long-term assessment of groundwater cost and supply – providing globally gridded groundwater extractable volumes and associated unit costs ($/km³) for accessible groundwater production, based on a variety of user-defined depletion and ponded depth scenarios. Key model documentation: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 Find the source code of the superwell model on GitHub: https://github.com/JGCRI/superwell Repository Overview Main output: superwell_outputs.7z contains 6 files (4.5 GB) named as superwell_py_deep_all_0.*PD_0.*DL.csv. These files present superwell outputs of global groundwater extraction volumes and cost estimates on a 0.5° scale for six scenarios with different Ponded Depth (PD; 0.3 and 0.6 m) and Depletion Limit (DL; 5%, 25%, and 40% of available volume) targets over the entire pumping lifetime of a grid cell superwell_py_deep_all_0.3PD_0.25DL_sample_100.csv contains superwell outputs for 100 data points sampled to match the global inputs' distribution superwell_py_deep_all_0.3PD_0.25DL_Grid_72548.csv contains superwell output for a single grid cell concept_v5.png provides an overview of the superwell workflow Outputs Description year_number: year of pumping depletion_limit: set depletion limit (DL) as a volume fraction of total available groundwater Mappings: continent, country, gcam_basin_id, Basin_long_name, grid_id: geographic identifiers and basin information Inputs: grid_area (km²): area of the grid cell whyclass: hydrogeological classification of the aquifer permeability (m/day), porosity (%), total_thickness (m), depth_to_water (m): aquifer properties. The geo-processed input data has been published separately: https://doi.org/10.57931/2307831 Model outputs: orig_aqfr_sat_thickness (m), aqfr_sat_thickness (m): original and remaining/instantaneous saturated thickness of the aquifer hydraulic_conductivity (m/day), transmissivity (m²/day): hydraulic properties of the aquifer radius_of_influence (m), areal_extent (km²): well radius and area of influence from the center of the well number_of_wells (-): number of wells in a grid cell determined by a ratio of well area and grid area max_drawdown (m), drawdown (m), drawdown_interference (m): well and aquifer drawdown during extraction total_head (m): total lift for the groundwater (depth to water plus drawdown) total_well_length (m): total depth of wells drilled well_yield (m³/day): pumping rate or well yield power (kW), energy (kWh): power and energy required for pumping groundwater Volume Outputs: volume_produced_perwell (m³), cumulative_vol_produced_perwell (m³): production volume metrics per well volume_produced_allwells (m³), cumulative_vol_produced_allwells (m³): aggregate extraction volumes for all wells in a grid cell available_volume (m³): available groundwater in storage for the grid cell as determined by aquifer properties depleted_vol_fraction: fraction of total volume pumped over available volumes in a grid cell (same as depletion limit) Cost Outputs: well_installation_cost ($): well installation cost based on the hydrogeological complexity of the aquifer annual_capital_cost, maintenance_cost, nonenergy_cost ($): nonenergy costs energy_cost_rate ($/kWh): electricity rate energy_cost ($): energy cost of pumping groundwater total_cost_perwell ($), total_cost_allwells ($): total annual energy and non-energy cost for each and all wells in a grid cell a unit_cost ($/m³), unit_cost_per_km3 ($/km³), unit_cost_per_acreft ($/acre-ft): total cost of pumping a unit of groundwater, indicated for different spatial units Key Resources Model documentation: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. Input data: Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data repository. https://doi.org/10.57931/2307831 superwell source code: https://github.com/JGCRI/superwell Cite as Niazi, H., Ferencz, S., Yoon, J., Graham, N., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024). Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets. MSD-LIVE Data repository. https://doi.org/10.57931/2307832 Contact Reach out to Hassan Niazi or Stephen Ferencz or open an issue in the superwell repository for questions or suggestions.

Earth Systems↗
DOE OSTI2023

System and method for micromechanical logical and gate

The present disclosure relates to a mechanical AND-gate logic system that may make use of a first and second lever arms, a first pivot linkage coupling the lever arms, an output member, and a second pivot linkage coupling the second lever arm to the output member. The first lever arm has first and second spaced apart ends for independently receiving separate logic level 1 or logic level 0 input signals thereon, and an output end spaced apart from the first and second input ends. The output end is disposed generally equidistant from the first and second spaced apart ends. The second lever arm has an input end and an output end and can move in both pivoting and translating movements. The first pivot linkage couples the output end of the first lever arm to the input end of the second lever arm such that both the input end and the output end of the second lever arm are able to either pivot and or to translate. The second lever arm is only able to translate and apply a logic 1 level input signal to the output member, to thus generate a logic 1 level output signal, when a logic level 1 input signal is applied simultaneously to both of the first and second input ends of the first lever arm.

Panas, Robert Matthew↗
DOE OSTI2024

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Hybrid Solar Thermal Energy System for District Heating Application

Solar district heating (SDH) systems can be good alternatives to conventional systems when they are optimized with hybrid configurations and thermal energy storage (TES). In this scope, a hybrid renewable thermal energy system (RTES) model has been built combining flat plate collector (FPC) solar system with parabolic trough collector (PTC) system via a heat exchanger and coupled with TES. To undertake the hybridization of the system, System Advisor Model (SAM) software was modified, which allowed control over configurations and more accurate modelling of heat transfer between the collectors. The model is first compared to an existing hybrid solar district heating systems (DHS) system in Taars, Denmark. The results showed a good correlation with an overestimation of only 6.4% compared to most recent heat output. Then the same system configuration was modeled in different geographic locations to investigate the impact of changes in direct normal irradiance (DNI) to the heat sink thermal output of the hybrid system. The results showed that the annual net thermal power output in California, USA can be three times more than the annual net thermal power output in Taars, Denmark. Finally, multiple hybrid configurations with varying solar field sizes were simulated based on the heat demand of two different university campuses DHS. The results showed that, retrofit applications of this hybrid DHS system coupled with TES could reduce the natural gas consumption of the existing systems between 25% and 41%. The use of hybrid RTES highlighted in this paper can be extended to many more opportunities.

district heating↗
DOE OSTI2024

Data for Grogan et al. "Bringing Hydrologic Realism to Water Markets"

This data set provides model output and post-processing files required to reproduce the results, tables, and figures in the paper "Bringing Hydrologic Realism to Water Markets" by Grogan et al. (in review). Other input data used in this study includes: Lisk, M., Grogan, D., Zuidema, S., Caccese, R., Peklak, D., Zheng, J., Fisher-Vanden, K., Lammers, R., Olmstead, S., & Fowler, L. (2023). Harmonized Database of Western U.S. Water Rights (HarDWR) (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2205619 Two models were used in this study: (1) The University of New Hampshire Water Balance Model WBM, and (2) a Water Market Model. Market model code and model output post-processing code that make use of these data can be found here Model output files are: 1. WBM output files: scenario[x]_wbm_output.zip Where [x] is one of 1, 2, 2a, 3, and 3a Each zipped directory contains 7 gridded NetCDF files, each reporting the 10-year annual average value of a given variable, in units of average mm/day: File Name: wbm_indUseGross_yc.nc; Description: Water withdrawals by industry (part of the urban sector) File Name: wbm_domUseGross_yc.nc; Description: Water withdrawals by the domestic sector (part of the urban sector) File Name: wbm_irrigationGross_yc.nc; Description: Water withdrawals for agriculture File Name: wbm_irrigationExtra_yc.nc; Description: Water withdrawals from unsustainable groundwater for agriculture File Name: wbm_indUseEvap_yc.nc; Description: Consumptive water use by industry File Name: wbm_domUseEvap_yc.nc; Description: Consumptive water use by the domestic sector File Name: wbm_irrigationNet_yc.nc; Description: Consumptive water use by agriculture The file full_cell_area.nc gives the area of each grid cell in km2, which is used for converting water depth to water volume. 2. Water market model output & post processing output Folder: marketTrdSummaries/ Description: Files in this folder are used as input to code 1_WelfareCalculation_actual_trades.R. They summarize historical water right trade transactions in each state. File Name: welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: welfare_data_actual.rdata; Description: welfare gains by WMA from actual historical trades, as shown in Figure 3A. This data is the output of code 1_WelfareCalculation_actual_trades.R File Name: welfare_summary_simulated.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 1. Produced by code 2_DemandCurves_simulated_trades.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs_SGMS.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2a. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_data_actual.rdata; Description: Spatial data, actual historical welfare gains by WMA as shown in Figure 3A. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated.rdata; Description: Spatial data, simulated Scenario 1 welfare gains by WMA as shown in Figure 3B. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs.rdata; Description: Spatial data, simulated Scenario 2 welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs_SGMA.rdata; Description: Spatial data, simulated Scenario 2a welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. Additional files are provided for efficient reproduction of tables and figures. These include: File Name: wma_thresold_dates_Scenario2(a).csv; Description: Wet vs. paper right threshold dates for each WMA. Shown in Figure 2A,B. Produced and used by code calculate_thresolds_Figure2.R File Name: WWRTradeBounds (directory); Description: Trade boundary shapefile required to reproduce Figure 3A-D. Used in code Figure3.R File Name: welfare_region_totals.csv; Description: Welfare gains for the entire study region, as shown in Figure 3E. Used in code Figure3.R File Name: Welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: WECC_MERIT_5min_v3b_mask.nc; Description: Gridded file that identified which land grid cells are in the WBM model domain, used for processing in code Figure4.py File Name: Table_1.csv; Description: All data in Table 1, reproducible from WBM output files using code table_1.R

Economics↗
DOE OSTI2025

IM3 Phase 2 Official Simulations: GCAM-Demeter-SELECT Annualized Land Use and Land Cover, Wood Harvest and Fertilization Data with Dynamic Urbanization Harmonized to CLM Land Definitions at 0.125 Degrees

Annualized land use land cover data, including wood harvest and fertilizer use data from the Global Change Analysis Model (GCAM) downscaled to 0.125 degrees for couping with the Community Land Model (CLM). GCAM here refers to GCAM-USA v5.3.im3 which has an enhanced electricity sector and an updated data system needed to represent regional to local scale dynamics. Data is also harmonized with future urbanization projections from the Spatially-Explicit, Long-term, Empirical City developmenT (SELECT) model. Projections/Data are generated using the demeter land use and land cover downscaling model. Original projections were generated at 0.05 degrees before being aggregared to 0.125 degrees. Projections are available for 8 alternative scenarios. Two versions of final data are included- one with managed forests or harvested forest area per pixel broken out and one with the same aggregated into total forests. Following folders are included: demeter_78_PFT_output:This is the final output of dynamic land use land cover change for 78 PFTs as required by CLM raw_outputs_incl_managed_forest: This is the final output but with managed forests broken out as a different PFT. Essentially a 79th PFT is added. wood_harvest_outputs: Wood harvest output per pixel in gC/m2 fertilization_outputs: Fertilizer use per pixel in gN/m2 Each NetCDF file in each folder represents a projection for a separate year, scenario. Land use outputs are organized as PFT level data saved as subdata. Link to GCAM version used- https://data.msdlive.org/records/yb23g-44274 Link to SELECT documentation -https://www.sciencedirect.com/science/article/pii/S1364815219301707 Link to CLM documentation- - https://www.cesm.ucar.edu/models/clm In case of questions contact- kanishka.narayan@pnnl.gov

GCAM-USA↗
DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗
DOE OSTI2022

Magnetic cores with high reluctance differences in flux paths

Embodiment of the present invention includes a magnetic structure and a magnetic structure used in a direct current (DC) to DC energy converter. The magnetic structure has an E-core and a plate, with the plate positioned in contact or in near contact with the post surfaces of the E-core. The E-core has a base, a no-winding leg, a transformer leg, and an inductor leg. The no-winding leg, the transformer leg, and the inductor leg are perpendicular and magnetically in contact with the base. The plate is a flat slab with lateral dimensions generally larger than its thickness. The plate has a plate nose that overlaps a top no-winding leg surface of the no-winding leg with a no-winding gap area to form a no-winding gap with a no-winding gap reluctance. The plate also has a plate end that overlaps a top inductor leg surface of the inductor leg with an inductor gap area to form an inductor gap with an inductor gap reluctance. In some embodiments, e.g., where the duty cycle is less than 50 percent, the inductor gap reluctance will be designed to be less than the no-winding gap reluctance. In these cases, the majority of the magnetic flux that passes through the transformer leg will return through the inductor leg, instead of through the no-winding leg. The inductor and no-winding gap reluctances can he adjusted, so that the electromotive force applied to a charge passing through the inductor will partially cancel the electromotive force applied by the transformer secondary. The gap reluctance ratio can be defined, so that the difference in secondary and inductor electromotive forces is equal to the output voltage defined by an optimal no-ripple duty cycle. In this way no changing current is required through the inductor to create a dI/dt inductive voltage drop across the output inductor. Zero output current ripple is achieved.Various embodiments of the plate, plate shape, and no-winding leg are disclosed. These embodiments allow achieving a high ratio of no-winding gap reluctance to inductor gap reluctance, for practical, affordable magnetic material structures and aspect ratios. A high gap reluctance ratio enables zero output current ripple for the high transformer turns ratios that are needed to achieve high input to output voltage ratios. The embodiments therefore allow achieving low output current ripple for 48 V or higher input voltages, 1 V or lower output voltages, and high output currents.

Yao, Yuan↗
DOE OSTI2023

Unfolder-based single-stage AC-AC conversion system

A power converter includes an unfolder connected to a three-phase source and has an output connection with three output terminals. A three-input converter connected to the unfolder produces a quasi-sinusoidal output voltage across converter output terminals. Switches of the converter selectively connect each of the three output terminals across the converter output terminals. A pulse-width modulation controller controls a first duty ratio and a second duty ratio for the converter based on a phase angle of the source and a modulation index generated from an error signal related to a control variable. The duty ratios are time varying at a rate related to a fundamental frequency of the source. The modulation index relates to output voltage of the converter, peak voltage or current of the source and/or peak current at the output terminals.

Teeneti, Chakridhar Reddy↗
DOE OSTI2021

Bioresorbable Primary Battery Anodes Built on Core–Double-Shell Zinc Microparticle Networks

Bioresorbable implantable electronics require power sources that are also bioresorbable with controllable electrical output and lifetime. In this paper, we report a bioresorbable zinc primary battery anode filament based on a zinc microparticle (MP) network coated with chitosan and Al 2 O 3 double shells. When discharged in 0.9% NaCl saline, a Zn MP filament with a 0.17 × 2 mm 2 cross-sectional area exhibited a stable voltage output of 0.55 V at a current of 0.01 mA. Covered by chitosan and Al 2 O 3 double shells, the zinc MP filament exhibited a directional dissolution behavior with a tunable lifetime approximately linear to its length. A stable 200 h discharging time was achieved with a 15 mm Zn MP filament. The maximum output power was found to be 12 μW at 0.03 mA for one filament. The linearity relationship between the current output and the filament cross-sectional area suggested a facile strategy to raise the power output at constant discharging voltage. The filaments could also be connected in series and in parallel to boost its overall voltage and current output, demonstrating their excellent integration capability. Furthermore, this work presents a promising pathway toward bioresorbable transient batteries with controllable lifetime and power output, demonstrating a great potential for powering transient implantable biomedical devices.

25 ENERGY STORAGE↗
DOE OSTI2021

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

NbTaV_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution ternary alloys niobium-tantalum-vanadium (Nb-Ta-V). The first-principle code that has been used to run the calculations is the closed-source Vienna Ab-Initio Simulation Package (VASP). The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 8. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 10,500 randomized atomic structures over 105 chemical compositions. The calculations have been collected on NERSC-Perlmutter using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for ternary alloys is AXBYCZ, where A, B, and C refer to the constituents, and X, Y, and Z are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. 0.POSCAR: input file that defines the atomic structure of a system 3. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 4. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 6. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 7. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 8. OUTCARL: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 10. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 11. vasprun.xml: contains similar information to OUTCAR, but in an xml format. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216.

36 MATERIALS SCIENCE↗
DOE OSTI2023

NbTa_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-tantalum (Nb-Ta). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb. For Ta, p semi-core states as valence were chosen. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for ternary alloys is AXBYCZ, where A, B, and C refer to the constituents, and X, Y, and Z are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2023

TaV_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-vanadium (Tanu-V). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the element V. For Ta, p semi-core states as valence were chosen. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmuller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmuller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗