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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 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 OSTI2026

Combining Generative Modeling and Advanced Control for Building Scenario Generation

Buildings make up a large portion of energy consumption in the U.S. today. Understanding their energy consumption patterns can improve their efficiency, but requires detailed models that rely on incomplete or unknown information. Previous work has shown that artificial intelligence (AI) can be used to predict missing information and even suggest upgrades to improve building efficiency. However, building upgrades may require undesirable upfront costs. Oppositely, advanced control could improve building efficiency with negligible upfront cost. To explore the tradeoffs between these two approaches, in this work we propose a workflow to compute optimal temperature setpoint schedules to minimize energy consumption and operational cost. Results show that modifying the temperature setpoints in a building using model predictive control (MPC) can effectively reduce its energy consumption and operational cost. This optimal operation cannot fully meet a desired goal. However, we show that by considering MPC in addition to component upgrades, a desired goal can be met with significantly less upfront costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Identification of Intraday False Data Injection Attack on DER Dispatch Signals

The urgent need for the decarbonization of power grids has accelerated the integration of renewable energy. Concurrently, increasing distributed energy resources (DER) and advanced metering infrastructure (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operation al challenges. This paper investigates the vulnerabilit y of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.

Kim, Jip↗
DOE OSTI2023

Net-zero CO 2 by 2050 scenarios for the United States in the Energy Modeling Forum 37 study

The Energy Modeling Forum (EMF) 37 study on deep decarbonization and high electrification analyzed a set of scenarios that achieve economy-wide net-zero carbon dioxide (CO 2 ) emissions in North America by mid-century, exploring the implications of different technology evolutions, policies, and behavioral assumptions affecting energy supply and demand. Here, for this paper, 16 modeling teams reported resulting emissions projections, energy system evolution, and economic activity. This paper provides an overview of the study, documents the scenario design, provides a roadmap for complementary forthcoming papers from this study, and offers an initial summary and comparison of results for net-zero CO 2 by 2050 scenarios in the United States. We compare various outcomes across models and scenarios, such as emissions, energy use, fuel mix evolution, and technology adoption. Despite disparate model structure and sources for input assumptions, there is broad agreement in energy system trends across models towards deep decarbonization of the electricity sector coupled with increased end-use electrification of buildings, transportation, and to a lesser extent industry. All models deploy negative emissions technologies (e.g., direct air capture and bioenergy with carbon capture and storage) in addition to land sinks to achieve net-zero CO 2 emissions. Important differences emerged in the results, showing divergent pathways among end-use sectors with deep electrification and grid decarbonization as necessary but not sufficient conditions to achieve net zero. These differences will be explored in the papers complementing this study to inform efforts to reach net-zero emissions and future research needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Scale-Up and Modeling Efforts Using an Omniphobic Surface Treatment for Mitigating Solids Deposition

Abstract Gas hydrates, waxes and asphaltenes represent some of the most significant flow assurance challenges, especially in subsea lines, where treatment options can be limited. Currently, complete avoidance is the primary strategy for hydrate management, while chemical or mechanical flushing/pigging may be utilized for other solids. Each system must also be treated with individualized solutions, as there have been no proved one-size-fits-all technologies demonstrated to date. As an alternative to constant chemical injection or thermodynamic controls such as insulation or heating, a robust omniphobic surface treatment material has been developed which has been shown in previous studies to significantly reduce the adhesion of flow assurance solids, resulting in lower risk for deposition and plugging of gas hydrates, waxes, and asphaltenes. As part of a Department of Energy study, laboratory scale tests were performed on a variety of apparatuses ranging from micromechanical force (micron scale) to laboratory flow loop testing (meter scale). While the results from these tests have been promising, there can be some disconnects between lab-scale observations and field-scale testing. Several factors can result in discrepancies between small-scale bench-top studies and deployment-scale efforts. These include flow conditions (Reynolds number, flow regime), chemistry (synthetic vs. crude oils, gas composition, and additives), physical differences (pipeline material, size, and geometry), etc. This work will describe the actions taken to better understand the deployment and performance of a material such as this omniphobic surface treatment in field scenarios, and to build confidence for deployment in a production scenario. These efforts include modeling field production scenarios using models developed from lab observations, application of the material to field-scale equipment to better understand application challenges, and expanded survivability and longevity testing. Understanding the bridge between laboratory testing and full-scale deployment allows for better technique and risk mitigation for field scale testing.

03 NATURAL GAS↗
DOE OSTI2022

Win-win transportation strategies for India: Linking air pollution and climate mitigation

Here, this article analyzes road transport in India to explore linkages between air pollution and climate change policies in the transportation sector. Five teams modeled five policy scenarios – fuel efficiency, electrification, alternative fuels, modal shifts, and moderation in transport demand – to explore which policy brings the largest synergetic effects in reducing carbon dioxide (CO 2 ) and particulate matter (PM 2.5 ) emissions. The teams also modeled the comprehensive scenario which included policy measures from individual scenarios. The paper concludes that all of the measures provide strong co-benefits in reducing air pollutant and CO 2 emissions. The modeling results show that increased energy efficiency of passenger and freight vehicles has the largest protentional for reducing both CO 2 and PM 2.5 emissions. It is possible to reach even a larger reduction of air pollutants and CO 2 emissions by combining several policy measures in the comprehensive scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AI-Driven Frameworks for Characterizing Urban Energy Systems

We develop AI-driven frameworks to characterize urban energy systems with the goal of transforming planning by reducing the labor of model generation, scaling scenario exploration, and improving accuracy for localized analysis. The approach integrates top-down and bottom-up data to train different AI models that predict missing information and generate inputs and targeted scenarios for district-scale models. The result is a scalable framework that provides actionable insights for reliable and efficient planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

The Kimberlina synthetic multiphysics dataset for CO 2 monitoring investigations

Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO 2 reservoir model based on a potential CO 2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO 2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO 2 , and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO 2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO 2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

58 GEOSCIENCES↗
DOE OSTI2024

Integrated modeling for assessing climate change impacts on water resources and hydropower potential in the Himalayas

Regional hydroclimatic variability and change can affect water resources and hydropower generation. It is essential to assess hydropower potential under current and future climatic conditions to inform the design and operation of hydropower infrastructures. Here, we employ an integrated modeling framework to assess the impact of projected hydroclimatic conditions on water resource systems and hydropower generation. The integrated framework samples climate model outputs under different scenarios to force a hydrologic model and produces streamflow projections. The projected streamflows are inputs for the future hydropower potential assessment. We implement the framework in the central Himalayan river basin. Our results demonstrate substantial spatiotemporal variability in different water balance components (precipitation, evapotranspiration, and water yield) under current and future climatic conditions. For the Himalayan Tila river basin, the annual average energy production is expected to increase under future hydroclimatic conditions (up to 39% in Tila-2 hydropower project, suggested by ensemble mean). Furthermore, this increase in energy is driven mainly by the increased streamflow projections, particularly during the dry season and in the late century. Our results highlight the impacts of hydroclimatic variability in hydropower productions and are of practical use to provide decision-relevant information for designing and operating hydropower infrastructures. The integrated modeling framework presented here is region-specific; however, the approach is reproducible, and the overall insights are generalizable across the Himalayan region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Grid Cost and Total Emissions Reductions Through Mass Deployment of Geothermal Heat Pumps for Building Heating and Cooling Electrification in the United States

This report presents the results of a study on the potential grid impacts of national-scale mass deployment of geothermal heat pumps (GHPs) coupled with weatherization in single-family homes (SFHs) from 2022 to 2050. GHPs are a technology readiness level 10, commercially available technology across the United States. This study is an impact analysis only; installed costs and available land areas for installing GHPs are not accounted for in determining their estimated deployment. The three scenarios studied were (1) continuing to operate the grid as it is today (the Base scenario), (2) a scenario to reach 95% grid emissions reductions by 2035 and 100% clean electricity by 2050 (the Grid Decarbonization scenario), and (3) a scenario in which the Grid Decarbonization scenario is expanded to include the electrification of wide portions of the economy, including building heating (the Electrification Futures Study or EFS scenario). The analysis team modeled each of these three scenarios with and without GHP deployment to a large percentage of US building floor space. In all cases, deployment of approximately 5 million GHPs per year demonstrated system cost savings on the grid, consumer fuel cost savings through eliminated fuel combustion for space heating, and CO 2 emission reductions from avoided on-site fuel combustion—and, in the case of the Base scenario, CO 2 emissions reductions from the electric power sector. GHPs have traditionally been viewed as a building energy technology. The most notable result of this study, however, is the demonstration that GHPs coupled with weatherization in SFHs are primarily a grid cost reduction tool and technology that, when deployed at a national scale, also substantially reduces CO 2 emissions, even in the absence of any other decarbonization policy.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Explaining Nonmerger Gamma-Ray Bursts and Broad-lined Supernovae with Close Binary Progenitors with Black Hole Central Engines

For over 25 yr, the origin of long-duration gamma-ray bursts (lGRBs) has been linked to the collapse of rotating massive stars. However, we have yet to pinpoint the stellar progenitor powering these transients. Moreover, the dominant engine powering the explosions remains open to debate. Observations of both lGRBs, supernovae associated with these GRBs, such as broad-line (BL) stripped-envelope (type Ic) supernovae (hereafter, Ic-BL), supernovae (SNe), and perhaps superluminous SNe, fast blue optical transients, and fast x-ray transients, may provide clues to both engines and progenitors. In this paper, we conduct a detailed study of the tight-binary formation scenario for lGRBs, comparing this scenario to other leading progenitor models. Combining this progenitor scenario with different lGRB engines, we can compare to existing data and make predictions for future observational tests. We find that the combination of the tight-binary progenitor scenario with the black hole accretion disk engine can explain lGRBs, low-luminosity GRBs, ultra-long GRBs, and Ic-BL. We discuss the various progenitor properties required for these different subclasses and note such systems would be future gravitational-wave merger sources. We show that the current literature on other progenitor-engine scenarios cannot explain all of these transient classes with a single origin, motivating additional work. We find that the tight-binary progenitor with a magnetar engine is excluded by existing observations. The observations can be used to constrain the properties of stellar evolution, the nature of the GRB, and the associated SN engines in lGRBs and Ic-BL. We discuss the future observations needed to constrain our understanding of these rare, but powerful, explosions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Exploiting SMILEs and the CMIP5 Archive to Understand Arctic Climate Change Seasonality and Uncertainty

Arctic Amplification (AA) exhibits a distinct seasonal dependence; it is weakest in boreal summer and strongest in winter. Here, we analyze simulations from single-model initial-condition large ensembles and Coupled Model Intercomparison Project Phase 5 to decipher the seasonal evolution of Arctic climate change. Models agree that the annual maximum AA shifts from autumn into winter over the 21st century, accompanied by similar shifts in sea-ice loss and surface turbulent heat fluxes, whereas the maximum precipitation shifts only into late autumn. However, the exact seasonal timing and magnitude of these shifts are highly uncertain. Decomposing the uncertainty into model structural differences, emission scenarios, and internal variability reveals that model differences dominate the total uncertainty, which also undergo autumn-to-winter shifts. We also find that the scenario uncertainty is unimportant for projections of AA. These results highlight that understanding model differences is critical to reducing uncertainty in projected Arctic climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

BETO 2021 Peer Review - WBS 4.1.2.32: Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project uses systems thinking and analysis to assess current and/or prospective techno-economics, research and development, deployment strategies, policy, and market conditions and their impact on the potential development trajectories of the bioenergy industry over time. Results from this project include identification of opportunities and constraints to industrial development, quantification of multiple metrics (energy, economic, environmental) and informing researchers, decision makers, and industry of the steps needed for a sustainable, nationwide biofuels industry. Analyses from this project enable the creation of a bioenergy industry by (1) inciting policy-makers to explore scenarios for nationwide biofuels production, identifying policy actions consistent with pathways for growth; (2) improving industry’s understanding of industry growth potential under different technology and investment conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with tools and analyses that can be adapted to meet research and teaching objectives, connecting students with careers that build the industry. One of the many modeling tools used in this project, the Biomass Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. We use models like the BSM to examine the implications of policies and incentives as well as their potential side-effects. The BSM uses a system-dynamics simulation to model dynamic interactions and transitions across the supply chain; it tracks the deployment of biofuels given industrial learning and the reaction of the investment community in the context of land availability, projected oil markets, consumer demand for biofuels, and government policies over time. Under expected market conditions, analyses using the BSM suggest that the biofuels industry may require significant external actions in the early years to thrive. Interventions that accelerate the industrial learning process (e.g. operation of pre-commercial and commercial facilities) have been identified as having strong influence in starting the growth of a commercial biofuel industry. Policies which are coordinated across the whole supply chain in BSM foster the growth of the biofuels industry and production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Modeling the Air Quality Impacts of Future Energy Scenarios

Many greenhouse gas (GHG) emission reduction measures achieve simultaneous reductions in air pollutants. Human-Earth system models can estimate such emission changes in the energy system but using them in chemistry-transport models (CTMs) to study their air quality impacts involves resource-intensive emissions processing. This is greatly simplified by an emissions scaling approach linking state-level emissions estimated by a human-Earth system model to a CTM. A scenario continuing pre-2022 energy policy in the U.S. to 2050 shows widespread air quality improvements over the 2015 baseline from SO 2 and NO x emission reductions of 50–80% from electricity generation and light-duty vehicles. Scenarios of GHG mitigation and vehicle electrification at the state and national level add further benefits. However, PM 2.5 increases from increased use of wood heating and bioenergy suggest that additional PM 2.5 management may be needed when using biofuels. In conclusion, this approach helps assess multiple future energy scenarios efficiently without sacrificing chemical detail in the air quality simulations.

air quality↗
DOE OSTI2021

Identification and Resolution of Gaps in Mechanistic Source Term and Consequence Analysis Modeling for Molten Salt Reactors Salt Spill Scenarios

This report represents an assessment of the gaps in Mechanistic Source Term (MST) and consequence assessment modeling for Molten Salt Reactors (MSRs). The current capabilities for MELCOR and the MELCOR Accident Code System (MACCS) are discussed, along with updates needed in order to address specific needs for MSRs. A test plan developed by Argonne National Laboratories is discussed as addressing some of these gaps, while some will require additional attention. Further recommendations are made on addressing these gaps. This report satisfies the DOE NE Milestone M2RD-21SN0601061 to leverage MELCOR and MACCS to identify parameters of importance for source term assessments for salt spill experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2022

Runaway electron deconfinement in SPARC and DIII-D by a passive 3D coil

The operation of a 3D coil—passively driven by the current quench (CQ) loop voltage—for the deconfinement of runaway electrons (REs) is modeled for disruption scenarios in the SPARC and DIII-D tokamaks. Nonlinear magnetohydrodynamic (MHD) modeling is carried out with the NIMROD code including time-dependent magnetic field boundary conditions to simulate the effect of the coil. Further modeling in some cases uses the ASCOT5 code to calculate advection and diffusion coefficients for REs based on the NIMROD-calculated fields, and the DREAM code to compute the runaway evolution in the presence of these transport coefficients. Compared with similar modeling in Tinguely et al (2021 Nucl. Fusion 61 124003), considerably more conservative assumptions are made with the ASCOT5 results, zeroing low levels of transport, particularly in regions in which closed flux surfaces have reformed. Of three coil geometries considered in SPARC, only the n = 1 coil is found to have sufficient resonant components to suppress the runaway current growth. Without the new conservative transport assumptions, full suppression of the RE current is maintained when the thermal quench MHD is included in the simulation or when the RE current is limited to 250kA, but when transport in closed flux regions is fully suppressed, these scenarios allow RE beams on the order of 1–2 MA to appear. Additional modeling is performed to consider the effects of the close ideal wall. In DIII-D, the CQ is modeled for both limited and diverted equilibrium shapes. In the limited shape, the onset of stochasticity is found to be insensitive to the coil current amplitude and governed largely by the evolution of the safety-factor profile. In both devices, prediction of the q-profile evolution is seen to be critical to predicting the later time effects of the coil.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗