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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 OSTI2016

Integral Reactor Containment Condensation Model and Experimental Validation

This NEUP funded project, NEUP 12-3630, is for experimental, numerical and analytical studies on high-pressure steam condensation phenomena in a steel containment vessel connected to a water cooling tank, carried out at Oregon State University (OrSU) and the University of Wisconsin at Madison (UW-Madison). In the three years of investigation duration, following the original proposal, the planned tasks have been completed: (1) Performed a scaling study for the full pressure test facility applicable to the reference design for the condensation heat transfer process during design basis accidents (DBAs), modified the existing test facility to route the steady-state secondary steam flow into the high pressure containment for controllable condensation tests, and extended the operations at negative gage pressure conditions (OrSU). (2) Conducted a series of DBA and quasi-steady experiments using the full pressure test facility to provide a reliable high pressure condensation database (OrSU). (3) Analyzed experimental data and evaluated condensation model for the experimental conditions, and predicted the prototypic containment performance under accidental conditions (UW-Madison). A film flow model was developed for the scaling analysis, and the results suggest that the 1/3 scaled test facility covers large portion of laminar film flow, leading to a lower average heat transfer coefficient comparing to the prototypic value. Although it is conservative in reactor safety analysis, the significant reduction of heat transfer coefficient (50%) could under estimate the prototypic condensation heat transfer rate, resulting in inaccurate prediction of the decay heat removal capability. Further investigation is thus needed to quantify the scaling distortion for safety analysis code validation. Experimental investigations were performed in the existing MASLWR test facility at OrST with minor modifications. A total of 13 containment condensation tests were conducted for pressure ranging from 4 to 21 bar with three different static inventories of non-condensable gas. Condensation and heat transfer rates were evaluated employing several methods, notably from measured temperature gradients in the HTP as well as measured condensate formation rates. A detailed mass and energy accounting was used to assess the various measurement methods and to support simplifying assumptions required for the analysis. Condensation heat fluxes and heat transfer coefficients are calculated and presented as a function of pressure to satisfy the objectives of this investigation. The major conclusions for those tests are summarized below: (1) In the steam blow-down tests, the initial condensation heat transfer process involves the heating-up of the containment heat transfer plate. An inverse heat conduction model was developed to capture the rapid transient transfer characteristics, and the analysis method is applicable to SMR safety analysis. (2) The average condensation heat transfer coefficients for different pressure conditions and non-condensable gas mass fractions were obtained from the integral test facility, through the measurements of the heat conduction rate across the containment heat transfer plate, and from the water condensation rates measurement based on the total energy balance equation. 15 (3) The test results using the measured HTP wall temperatures are considerably lower than popular condensation models would predict mainly due to the side wall conduction effects in the existing MASLWR integral test facility. The data revealed the detailed heat transfer characteristics of the model containment, important to the SMR safety analysis and the validation of associated evaluation model. However this approach, unlike separate effect tests, cannot isolate the condensation heat transfer coefficient over the containment wall, and therefore is not suitable for the assessment of the condensation heat transfer coefficient against system pressure and noncondensable gas mass fraction. (4) The average condensation heat transfer coefficients measured from the water condensation rates through energy balance analysis are appropriate, however, with considerable uncertainties due to the heat loss and temperature distribution on the containment wall. With the consideration of the side wall conduction effects, the results indicate that the measured heat transfer coefficients in the tests is about 20% lower than the prediction of Dehbi’s correlation, mainly due to the side wall conduction effects. The investigation also indicates an increase in the condensation heat transfer coefficient at high containment pressure conditions, but the uncertainties invoked with this method appear to be substantial. (5) Non-condensable gas in the tests has little effects on the condensation heat transfer at high elevation measurement ports. It does affect the bottom measurements near the water level position. The results suggest that the heavier non-condensable gas is accumulated in the lower portion of the containment due to stratification in the narrow containment space. The overall effects of the non-condensable gas on the heat transfer process should thus be negligible for tall containments of narrow condensation spaces in most SMR designs. Therefore, the previous correlations with noncondensable gas effects are not appropriate to those small SMR containments due to the very poor mixing of steam and non-condensable gas. The MELCOR simulation results agree with the experimental data reasonably well. However, it is observed that the MELCOR overpredicts the heat flux for all analyzed tests. The MELCOR predicts that the heat fluxes for CCT’s approximately range from 30 to 45 kW/m2 whereas the experimental data (averaged) ranges from about 25 to 40 kW/m2. This may be due to the limited availability of liquid film models included in MELCOR. Also, it is believed that due to complex test geometry, measured temperature gradients across the heat transfer plate may have been underestimated and thus the heat flux had been underestimated. The MELCOR model predicts a film thickness on the order of 100 microns, which agrees very well with film flow model developed in this study for scaling analysis. However, the expected differences in film thicknesses for near vacuum and near atmospheric test conditions are not significant. Further study on the behavior of condensate film is expected to refine the simulation results. Possible refinements include but are not limited to, the followings: CFD simulation focusing on the liquid film behavior and benchmarking with experimental analyses for simpler geometries. 16 1 INTRODUCTION This NEUP funded project, NEUP 12-3630, is for experimental, numerical and analytical studies on high-pressure steam condensation phenomena in a steel containment vessel connected to a water cooling tank, carried out at Oregon State University (OrSU) and the University of Wisconsin at Madison (UW-Madison). The experimental results are employed to validate the containment condensation model in reactor containment system safety analysis code for integral SMRs. Such a containment condensation model is important to demonstrate the adequate cooling. In the three years of investigation, following the original proposal, the following planned tasks have been completed: (1) Performed a scaling study for the full pressure test facility applicable to the reference design for the condensation heat transfer process during design basis accidents (DBAs), modified the existing test facility to route the steady-state secondary steam flow into the high pressure containment for controllable condensation tests, and extended the operations at negative gage pressure conditions (OrSU). (2) Conducted a series of DBA and quasi-steady experiments using the full pressure test facility to provide a reliable high pressure condensation database (OrSU). (3) Analyzed experimental data and evaluated condensation model for the experimental conditions, and predicted the prototypic containment performance under accidental conditions (UW-Madison). The results are applicable to integral Small Modular Reactor (SMR) designs, including NuScale, mPower, Westinghouse SMR, Holtec-160 and other integral reactors with small containments of relatively high pressures under accidental conditions. Testing has been conducted at the OrSU laboratory in the existing MASLWR (Multi-Application Small Light Water Reactor) integral test facility sponsored by the US Department of Energy. Its highpressure stainless steel containment model (~2 MPa) is scaled to the NuScale SMR currently under development at NuScale Power, Inc.. Minor modifications to the model containment have been made to control the non-condensable gas fraction and to utilize the secondary loop stable steam flow for condensation testing. UW-Madison has developed a containment condensation model, which leveraged previous validated containment heat transfer work carried out at UW-Madison, and extended the range of applicability of the model to integral SMR designs that utilize containment vessels of high heat transfer efficiencies. In this final report, the research background and literature survey are presented in Chapter 2 and 3, respectively. The test facility description and modifications are summarized in Chapter 4, and the scaling analysis is introduced in Chapter 5. The tests description, procedures, and data analysis are presented in Chapter 6, while the numerical modeling is presented in Chapter 7, followed by a conclusion section in Chapter 8.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Predicting Ice Sheet and Climate Evolution at Extreme Scales

A main research objectives of PISCEES is the development of formal methods for quantifying uncertainties in ice sheet modeling. Uncertainties in simulating and projecting mass loss from the polar ice sheets arise primarily from initial conditions, surface and basal boundary conditions, and model parameters. In general terms, two main chains of uncertainty propagation may be identified: 1. inverse propagation of observation and/or prior onto posterior control variable uncertainties; 2. forward propagation of prior or posterior control variable uncertainties onto those of target output quantities of interest (e.g., climate indices or ice sheet mass loss). A related goal is the development of computationally efficient methods for producing initial conditions for an ice sheet that are close to available present-day observations and essentially free of artificial model drift, which is required in order to be useful for model projections (“initialization problem”). To be of maximum value, such optimal initial states should be accompanied by “useful” uncertainty estimates that account for the different sources of uncerainties, as well as the degree to which the optimum state is constrained by available observations. The PISCEES proposal outlined two approaches for quantifying uncertainties. The first targets the full exploration of the uncertainty in model projections with sampling-based methods and a workflow managed by DAKOTA (the main delivery vehicle for software developed under QUEST). This is feasible for low-dimensional problems, e.g., those with a handful of global parameters to be inferred. This approach can benefit from derivative/adjoint information, but it is not necessary, which is why it often referred to as “non-intrusive”. The second approach makes heavy use of derivative information from model adjoints to address quantifying uncertainty in high-dimensions (e.g., basal boundary conditions in ice sheet models). The use of local gradient, or Hessian information (i.e., second derivatives of the cost function), requires additional code development and implementation, and is thus often referred to as an “intrusive” approach. Within PISCEES, MIT has been tasked to develop methods for derivative-based UQ, the ”intrusive” approach discussed above. These methods rely on the availability of first (adjoint) and second (Hessian) derivative code, developed through intrusive methods such as algorithmic differentiation (AD). While representing a significant burden in terms of code development, derivative-baesd UQ is able to cope with very high-dimensional uncertainty spaces. That is, unlike sampling methods (all variations of Monte Carlo), calculational burden is independent of the dimension of the uncertainty space. This is a significant advantage for spatially distributed uncertainty fields, such as threedimensional initial conditions, three-dimensional parameter fields, or two-dimensional surface and basal boundary conditions. Importantly, uncertainty fields for ice sheet models generally fall into this category.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Mitigating energy demand sector emissions: The integrated modelling perspective

Mitigating carbon emissions in the current energy system will require fundamental changes of both the energy supply and the energy demand sectors. Previous global model-based analyses, however, have focused mostly on energy supply transformations, while the energy demand sector changes are less well understood. In this study, this knowledge gap is addressed by analysing in detail the projected future energy demand projections, and the required demand-side changes to reach stringent mitigation targets using a suite of integrated assessment models. We examine industry, transport and buildings sector pathways across four models and three different reference scenarios from the Shared-Socioeconomic Pathway framework which is used as a set of common future perspectives by the climate research community. The demand side mitigation efforts are compared to a more detailed, sector-specific, technology-oriented assessments of abatement potential based on a literature review for the year 2030. The results indicate that strong emission growth in the industry and transport sector can be attributed to increasing final energy per capita and population growth. In the stringent mitigation scenarios energy efficiency, electrification and switching to low carbon fuel are all required in the short term. In the green growth SSP1 scenario the required emission reduction is significantly less than other scenarios showing that the demand growth and the technology development largely affects the sectors’ mitigation challenge. The technology assessment estimates that in particular in the transport and buildings sector there is a higher potential to reduce demand-side emissions through energy efficiency improvements than currently envisioned in the integrated assessment models.

Edelenbosch, Oreane Y.↗
DOE OSTI2014

The simulation of 3D mass models in 2D digital mammography and breast tomosynthesis

Purpose: This work proposes a new method of building 3D breast mass models with different morphological shapes and describes the validation of the realism of their appearance after simulation into 2D digital mammograms and breast tomosynthesis images. Methods: Twenty-five contrast enhanced MRI breast lesions were collected and each mass was manually segmented in the three orthogonal views: sagittal, coronal, and transversal. The segmented models were combined, resampled to have isotropic voxel sizes, triangularly meshed, and scaled to different sizes. These masses were referred to as nonspiculated masses and were then used as nuclei onto which spicules were grown with an iterative branching algorithm forming a total of 30 spiculated masses. These 55 mass models were projected into 2D projection images to obtain mammograms after image processing and into tomographic sequences of projection images, which were then reconstructed to form 3D tomosynthesis datasets. The realism of the appearance of these mass models was assessed by five radiologists via receiver operating characteristic (ROC) analysis when compared to 54 real masses. All lesions were also given a breast imaging reporting and data system (BIRADS) score. The data sets of 2D mammography and tomosynthesis were read separately. The Kendall's coefficient of concordance was used for the interrater observer agreement assessment for the BIRADS scores per modality. Further paired analysis, using the Wilcoxon signed rank test, of the BIRADS assessment between 2D and tomosynthesis was separately performed for the real masses and for the simulated masses. Results: The area under the ROC curves, averaged over all observers, was 0.54 (95% confidence interval [0.50, 0.66]) for the 2D study, and 0.67 (95% confidence interval [0.55, 0.79]) for the tomosynthesis study. According to the BIRADS scores, the nonspiculated and the spiculated masses varied in their degrees of malignancy from normal (BIRADS 1) to highly suggestive for malignancy (BIRADS 5) indicating the required variety of shapes and margins of these models. The assessment of the BIRADS scores for all observers indicated good agreement based on Kendall's coefficient for both the 2D and the tomosynthesis evaluations. The paired analysis of the BIRADS scores between 2D and tomosynthesis for each observer revealed consistent behavior for the real and simulated masses. Conclusions: A database of 3D mass models, with variety of shapes and margins, was validated for the realism of their appearance for 2D digital mammography and for breast tomosynthesis. This database is suitable for use in future observer performance studies whether in virtual clinical trials or in patient images with simulated lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Model Structural Inference Using Local Dynamic Operators

This paper focuses on the problem of quantifying the effects of model-structure uncertainty in the context of time-evolving dynamical systems. This is motivated by multi-model uncertainty in computer physics simulations: developers often make different modeling choices in numerical approximations and process simplifications, leading to different numerical codes that ostensibly represent the same underlying dynamics. We consider model-structure inference as a two-step methodology: the first step is to perform system identification on numerical codes for which it is possible to observe the full state; the second step is structural uncertainty quantification, in which the goal is to search candidate models "close" to the numerical code surrogates for those that best match a quantity of interest (QOI) from some empirical data sets. Here, we (1) define a discrete, local representation of the structure of a partial differential equation, which we refer to as the "local dynamical operator" (LDO); (2) identify model structure nonintrusively from numerical code output; (3) nonintrusively construct a reduced-order model (ROM) of the numerical model through POD-DEIM-Galerkin projection; (4) perturb the ROM dynamics to approximate the behavior of alternate model structures; and (5) apply Bayesian inference and energy conservation laws to calibrate a LDO to a given QOI. We demonstrate these techniques using the two-dimensional rotating shallow water equations as an example system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q3

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VA Community Determinants of Health Data Curation Documentation FY25-Q4

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VA Community Determinants of Health Data Curation Documentation FY26-Q1

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies

Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

CONUS-wide Projected Flood Frequency and Uncertainty Estimates, Version 1.0

This dataset presents a large-ensemble of CONUS-wide projected flood frequency and uncertainty estimates across ~2.7 million NHDPlusV2 river reaches over the CONUS. The framework producing this dataset leverages a multi-model, uncertainty-aware modeling framework that allows evaluating shifts in flood frequences at the stream reach level across the CONUS. CONUS-wide ensemble streamflow projections generated from hydrologic simulations driven by downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs are used to derive these flood frequency and uncertainty estimates over the period 1980 - 2099. A spatially consistent regional L-moment algorithm is applied across clusters defined by the US Hydrologic Unit Code Subregions (HUC4s and HUC8s) and NHDPlusV2 stream orders to estimate flood frequencies. The dataset also includes at-site based flood estimates that allow for the comparison between local and regional approach-based estimates, assess projected changes, and characterize their uncertainties. For more reliable estimation of rare flood frequencies such as 500 and 1000-year return periods, super-ensemble based estimates are also included in the dataset. This dataset is derived to support the "Impact-Informed Dam Safety Risk Assessment for Securing Hydropower Assests" project for the US Department of Energy (DOE) Hydropower and Hydrokinetic Office (H2O). For further details, refer to Kao et al. (2022), Ghimire et al. (2023), Ghimire et al. (2025), and Hosking and Wallis (1997).

Ghimire, Ganesh [ORNL] (ORCID:0000000242843941)↗
DOE OSTI2015

Modeling and Analysis of Wholesale Electricity Market Design. Understanding the Missing Money Problem. December 2013 - January 2015

This project examined the impact of renewable energy sources, which have zero incremental energy costs, on the sustainability of conventional generation. This “missing money” problem refers to market outcomes in which infra-marginal energy revenues in excess of operations and maintenance (O&M) costs are systematically lower than the amortized costs of new entry for a marginal generator. The problem is caused by two related factors: (1) conventional generation is dispatched less, and (2) the price that conventional generation receives for its energy is lower. This lower revenue stream may not be sufficient to cover both the variable and fixed costs of conventional generation. In fact, this study showed that higher wind penetrations in the Electric Reliability Council of Texas (ERCOT) system could cause many conventional generators to become uneconomic.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Climate effects of future aerosol reductions for achieving carbon neutrality in China

To limit the global warming to 1.5 °C above pre-industrial levels, beyond which the most dangerous impacts of climate change will occur, achieving carbon neutrality by the mid-21st century is essential. As the largest developing country and a significant contributor to carbon dioxide (CO 2 ) emissions, China has announced its ambitious climate commitment to pass carbon peak before 2030 and to achieve carbon neutrality by 2060. Both climate policies and regional clean air actions have been implemented for reductions in fossil fuel emissions, including the emissions of short-lived aerosols and precursors. Furthermore, in the context of pursuing carbon neutrality in China, aerosol reductions due to clean air actions and pollution control policies are very likely to have a great impact on climate. In this study, climate effects of aerosol reductions due to China's clean air actions under localized future emission scenarios are investigated using the Community Earth System Model version 1 (CESM1). Fully coupled and atmosphere-only experiments in years of carbon peak (2030) and carbon neutrality (2060) are performed with anthropogenic emissions of aerosols and precursors under “Current-goals” (Current) and “Carbon-Neutral” (Neutral) scenarios from the Dynamic Projection for Emissions in China (DPEC) model that consider socioeconomic development, climate policy, and pollution control actions (Figs. S1–S4 online). In addition, a present-day emissions simulation (PD) is conducted as the reference case (Supplementary materials online). Another sensitivity simulation is also conducted, with black carbon (BC) emissions set to follow the Neutral experiment but other emissions kept at the present-day levels (Neutral_BC), to quantify the relative roles of reducing strongly absorbing BC and other aerosols on future climate towards carbon neutrality. Greenhouse gases concentrations are kept at the 2015 levels in all simulations (Text S1 online).

Yang, Yang↗
DOE OSTI2019

GridAPPS-D™ a distribution management platform to develop applications for rural electric utilities

Rural electric utilities, many of which operate with a co-op management approach, have unique operational challenges not shared with their municipal or IOU counterparts. Rural electric utilities typically have fewer customers per feeder mile, and are generally less able to afford the costs, or provide robust support for today’s Advanced Distribution Management System (ADMS) offerings. Additionally, co-ops typically do not need the full suite of ADMS offerings but would benefit from a subset of applications such as OMS, Unbalanced Power-Flow, Volt-VAR optimization and FLISR (Fault Location Isolation and Service Restoration). Their needs, even within these applications, are simpler and more standardized. Lastly, due to their size, their IT capabilities and resources are commonly limited; hence hardware and software needs to be standards-based and easy to maintain with only most expansions of current capabilities. GridAPPS-D™ is an open-source, application development environment that’s part of the Department of Energy’s Grid Modernization Efforts, being led by the Pacific Northwest National Laboratory. The GridAPPS-D project is developing a reference architecture and implementation with the goal of reducing the time and cost to integrate distribution applications for both planning and operations through the use of standardized interfaces and data models. In this paper, we present a view of the reference architecture for GridAPPS-D and highlight its applicability to the rural electric utilities’ needs, including descriptions of applications being developed using this platform, and how they can be applied to other rural electric utilities.

distribution management, GridAPPS-D, rural electri↗
DOE OSTI2026

VA Community Determinants of Health Data Curation Documentation FY26-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1 km grid) to another (e.g., U.S. Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, U.S. Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1 km grids. Some economic data may only be available at the ZIP code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., U.S. Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Basin and Range Investigation for Developing Geothermal Energy: Exploration Data

This data package includes exploration material from the Basin & Range Investigation for Developing Geothermal Energy [in Hidden Systems] project (BRIDGE), which is part of a broader initiative to advance the exploration of hidden geothermal resources in the Basin & Range Province of the western U.S. Data modalities include a helicopter-borne time-domain electromagnetic survey, magnetotellurics, 2-meter temperature measurements, ground-based gravity and legacy aeromagnetic surveys, geochemistry, geologic mapping, LiDAR analysis, 3D models, associated geospatial data, and a bibliography of existing data and references utilized in prospect characterization and conceptual modeling. Key files are in CSV, Geosoft, and Geotools formats. Please refer to READMEs for dataset-specific information. Where applicable, acquisition data and inversion models for a particular prospect or area of interest are organized separately. This BRIDGE data package is the product of a collaboration led by Sandia National Laboratories with partners from Geologica Geothermal Group, Inc., the U.S. Navy Geothermal Program Office, and consultants Steven Sewell (Australis Geoscience Ltd) and William Cumming (Cumming Geoscience). The project's areas of interest (AOIs) are based off priority areas of interest in the southwestern portion of the Nevada Play Fairway map, distribution across tectonic provinces, accessibility, and the project team's extensive experience in the region. AOIs cover about a dozen basins that include unexplored prospects, partially explored prospects, and some developed analogue resources that provide validation cases. Many unexplored and partially explored prospects are on U.S. Department of Defense (DoD) land, though adjacent lands are included as well.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Drying of tundra landscapes will limit subsidence-induced acceleration of permafrost thaw: Modeling Archive

This Modeling Archive is in support of a NGEE Arctic publication in review "Drying of tundra landscapes will limit subsidence-induced acceleration of permafrost thaw". The study used a cryohydrology model to assess the potential risk of abrupt permafrost thaw triggered by melting ground ice, a key open question associated with permafrost response to a warming Arctic. The spatially resolved simulations are for a small catchment 465 ice-wedge polygons in polygonal tundra near Utqiagvik, Alaska in the high-emissions RCP8.5 climate scenario. The simulations are compared to runoff, evapotranspiration and subsidence in the current climate and agree well. The study used the ATS code configured as an intermediate-scale cryohydrology model (Advanced Terrestrial Simulator, Version v2). The archive includes input files for spinup (1985 to 2005) and projections (2006 to 2100 - the manuscript reports 2006 to 2098). Three of the projections include the effects of subsidence and microtopography change with different depth profiles of ice content corresponding to the median, 20th percentile and 80th percentile. The fourth projection has subsidence turnoff and uses the reference case (median) ice content. Spatially averaged or aggregated output variables are included for the projections. Selected checkpoint files for the projections and postprocessing scripts are also included. Files included are *.xml; *.h5; *.exo; *.py; *.sh, *. nb, mesh files, and one file in the original Excel format plus *.csv and *.pdf to conserve formatting contained in the original file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A systems biology, whole-genome association analysis of the molecular regulation of biomass growth and composition in Populus deltoides

Poplars trees are well suited for biofuel production due to their fast growing habit, favorable wood composition and adaptation to a broad range of environments. The availability of a reference genome sequence, ease of vegetative propagation and availability of transformation methods also make poplar an ideal model for the study of wood formation and biomass growth in woody, perennial plants. The objective of this project was to conduct a genome-wide association genetics study to identify genes that regulate bioenergy traits in Populus deltoides (eastern cottonwood). Populus deltoides is a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits and common and low-frequency single-nucleotide polymorphisms (SNPs) detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. These polymorphism are critical tools for the development of specialized plant feedstocks for bioenergy.

09 BIOMASS FUELS↗