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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Modeling High-Energy Gamma-Rays from the Fermi Bubbles

In 2010, the Fermi Bubbles were discovered at the galactic center of the Milky Way. These giant gamma-ray structures, extending 55° in galactic latitude and 20°-30° in galactic longitude, were not predicted. We wish to develop a model for the gamma-ray emission of the Fermi Bubbles. To do so, we assume that second order Fermi acceleration is responsible for the high-energy emission of the bubbles. Second order Fermi acceleration requires charged particles and irregular magnetic fields{ both of which are present in the disk of the Milky Way galaxy. By solving the steady-state case of the transport equation, I compute the proton spectrum due to second order Fermi acceleration. I compare the analytical solutions of the proton spectrum to a numerical solution. I find that the numerical solution to the transport equation converges to the analytical solution in all cases. The gamma-ray spectrum due to proton-proton interaction is compared to Fermi Bubble data (from Ackermann et al. 2014), and I find that second order Fermi acceleration is a good fit for the gamma-ray spectrum of the Fermi Bubbles at low energies with an injection source term of S = 1.5 × 10 -10 GeV -1 cm -3 yr -1 . I find that a non-steady-state solution to the gamma-ray spectrum with an injection source term of S = 2 × 10 -10 GeV -1 cm -3 yr -1 matches the bubble data at high energies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Evaluating turbulence models at high energy densities

The RESHOCK campaign at Lawrence Livermore National Laboratory has been working on increasing our understanding of the evolution of turbulent, unstable plasma interfaces which are applicable in various HED applications including ICF implosions. A common approach to model such interfaces involves the use of Renolds-Averaged-Navier-Stokes (RANS) models whose parameters have been constrained by theory and experiments. These parameter sets are not unique and the applicability of one set of tuned model parameters to systems with differing shock strengths or material densities has not been well tested. Our new NIF experiments, along with related studies of turbulent shear flows, provide data and model validation for plasma interfaces in the HED regime. Our experiments measure mixing-layer width at an unstable interface in the plasma regime and are specifically designed to challenge mix models of the Reynolds-Averaged-Navier-Stokes (RANS) type where turbulence is assumed to be fully developed, i.e. the flow exhibits a broad spectrum of length scales without memory of the initial condition.4 We utilize precise control of the initial interface conditions (densities on both sides of the interface, an initial rippled perturbation pattern consisting of equally weighted wavelengths between 10 and 20 microns, and initial material compositions) along with a repeatable drive history

36 MATERIALS SCIENCE↗
DOE OSTI2015

Modeling High-Energy Gamma-Rays from the Fermi Bubbles - Oral Presentation [Slides]

In 2010, the Fermi Bubbles were discovered at the galactic center of the Milky Way. These giant gamma-ray structures, extending 50 degrees in galactic latitude and 20-30 degrees in galactic longitude, were not predicted. We wish to develop a model for the gamma-ray emission of the Fermi Bubbles. To do so, we assume that second order Fermi acceleration is responsible for the high-energy emission of the bubbles. Second order Fermi acceleration requires charged particles and irregular magnetic fields—both of which are present in the disk of the Milky Way galaxy. I use the assumption of second order Fermi acceleration in the transport equation, which describes the diffusion of particles. By solving the steady-state case of the transport equation, I compute the proton spectrum due to Fermi second order acceleration and compare this analytical solution to a numerical solution provided by Dr. P. Mertsch. Analytical solutions to the transport equation are taken from Becker, Le, & Dermer and are used to further test the numerical solution. I find that the numerical solution converges to the analytical solution in all cases. Thus, we know the numerical solution accurately calculates the proton spectrum. The gamma-ray spectrum follows the proton spectrum, and will be computed in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Hybrid Energy Resources Bidding Model (HERB) v1.0

The HERB Model provides a stochastic optimization framework for hybrid power plants (Storage plus Renewables) participating in day-ahead electricity markets. It considers uncertainty in market prices and renewable energy generation and generated risk-constrained price-quantity bid curves with limited, non-decreasing price steps.

Heleno, Miguel [Lawrence Berkeley National Laborat↗
DOE OSTI2019

Generic modelling and optimal day-ahead dispatch of micro-energy system considering the price-based integrated demand response

As an extension of micro-grid, micro-energy system (MES) is one of the important carriers for energy utilization in the future, and using energy prices as a controllable resource is conducive to improving the optimization potential of MES. Firstly, based on the energy hub model, a generic method for modelling the steady-state energy balance equation of MES is proposed. Then, considering the multi-energy substitution effect in the background of multi-energy coupling, the concept of price-based integrated demand response (P-IDR) is introduced. Meanwhile, based on the price elasticity theory and the discrete choice theory, the energy timing transfer characteristics and energy substitution characteristics in P-IDR is modelled. Furthermore, after taking into account the P-IDR, an MINLP model for day-ahead dispatch of MES is built, and a generalized benders decomposition method is used for solution. Case studies are conducted on an MES to verify the effectiveness of the proposed modelling method. Here, the result shows that it is beneficial to improve renewable energy accommodation and reduce the peak and off-peak difference of energy load when the P-IDR is deployed. In addition, consider the energy substitution characteristics can reduce the user’s cost on energy purchase and be more in line with the user’s rational consumption behaviour.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The Energy Exascale Earth System Model Version 3: 2. Overview of the Coupled System

The Energy Exascale Earth System Model version 3 (E3SMv3) represents the latest advancement in Earth system modeling developed by the U.S. Department of Energy (DOE). Building upon previous versions, E3SMv3 introduces significant updates across its coupled components to enhance capability and improve fidelity. The atmosphere component incorporates advancements in chemistry, aerosol-cloud interactions, convection, and microphysics. The ocean features a new time-stepping scheme and a higher-resolution unstructured mesh with sub-ice-shelf cavities, while the sea ice model integrates advanced snow and ice physics for more realistic cryospheric simulations. The land model introduces prognostic vegetation dynamics and a new sub-grid topographic treatment of solar radiation. A new tri-grid configuration harmonizes the horizontal grids of the land and river components for improved process coupling. It is enabled by a new non-linear remapping between the atmosphere and land. E3SMv3 underwent extensive testing through a comprehensive simulation campaign, including pre-industrial control, idealized CO 2 experiments, and historical simulations spanning 1850–2024. The model demonstrates significant improvements in simulating the evolution of the historical surface temperature, particularly addressing the “pothole cooling” bias in earlier versions. Reduced aerosol-related forcing contributes to more realistic radiative forcing and better alignment with the observational record. Ocean heat content (OHC) and sea ice trends are also improved as a result.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Operational Parameter Database for Molten Salt Thermal Energy Storage Tank Modeling

The second generation of concentrated solar power (CSP) plants is characterized by the use of a central receiver (either cavity or external), two molten nitrate salt tanks (60 wt.% NaNO 3 and 40 wt.% KNO 3 ), and a steam Rankine power-generation cycle connected through a primary heat exchanger. Molten salt thermal energy storage (TES) tanks have been widely deployed in commercial CSP plants worldwide and have been essential for increasing plant dispatchability and capacity factor, while also reducing the levelized cost of electricity (LCOE). These systems enable energy storage at the gigawatt-hour scale, typically providing 6 to 17 hours of storage duration. Despite being a commercial technology, the multiple failures observed after only a few months or years of operation in plants around the world demonstrate the technology's relative infancy and highlight the need for further research to improve its reliability. The National Laboratory of the Rockies (NLR), in collaboration with industry partners and academic and research institutions, has been leading multiple projects funded by the U.S. Department of Energy (DOE). These projects focus on addressing molten salt tank failures by improving tank design and welding fabrication practices, evaluating new alloys and weld fillers, and providing guidelines for tank commissioning and safe operation. In particular, this report presents modeling results on the effect of key tank operation parameters during 60 minutes of operation, including the mass flow rate and temperature of the salt inflow, tank salt inventory temperature, and inventory level for a representative molten salt tank design. These results form a database of tank operation behaviors that captures the effects of each specific parameter during charging, charging/discharging, and discharging processes.

14 SOLAR ENERGY↗
DOE OSTI2016

The structure of molten CuCl: Reverse Monte Carlo modeling with high-energy X-ray diffraction data and molecular dynamics of a polarizable ion model

The results of the structural properties of molten copper chloride are reported from high-energy X-ray diffraction measurements, reverse Monte Carlo modeling method, and molecular dynamics simulations using a polarizable ion model. The simulated X-ray structure factor reproduces all trends observed experimentally, in particular the shoulder at around 1 Å{sup −1} related to intermediate range ordering, as well as the partial copper-copper correlations from the reverse Monte Carlo modeling, which cannot be reproduced by using a simple rigid ion model. It is shown that the shoulder comes from intermediate range copper-copper correlations caused by the polarized chlorides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Experiment-modeling studies comparing energy dissipation in the DIII-D SAS and SAS-VW divertors

Recent DIII-D experiments on Small Angle Slot (SAS) divertors have confirmed that a combination of divertor closure and target shaping can enhance cooling across the divertor target and increase energy dissipation, but with significant dependence on B T (toroidal magnetic field) direction. In these novel divertors, the roles of closure, target shaping, drifts, and scale lengths are all interconnected in optimizing dissipation, with the separatrix electron density n eSEP being the key parameter associated with the level of dissipation/detachment. After modifying the original flat-targeted graphite SAS to include a V shape with a tungsten coating on the outer side of the divertor (SAS-VW), matched series of discharges were run to compare to detailed SOLPS-ITER modeling. Experimentally, when run as designed with the outer strike point at the slot vertex, SAS-VW requires nearly identical n eSEP for detachment as the original SAS, with little difference in dissipation for the new geometry. This is in contrast to (1) earlier modeling predictions that a small change of the SAS geometry to a V shape should enhance dissipation at the same n eSEP for magnetic configurations having better H-mode access (ion B × ∇B drift directed into the divertor), and (2) despite the achievement of significantly higher (2-7x) neutral pressures and compression in the SAS-VW slot. Comparisons of experimental density scans to the most recent SOLPS-ITER modeling with ExB drifts show reasonable agreement for dissipation/detachment onset when using separatrix density as the independent parameter. In order to help understand the discrepancy in modeled vs actual performance for the new configuration, additional measurements varying gas injection location and impurity injection were undertaken. In-slot D 2 gas fueling is more effective (5–22 %) in promoting detachment, in accord with modeling. In-slot impurity injection (N 2 or Ne) can yield 30 % lower core Z eff and 15 % less confinement degradation after detachment compared to main chamber puffing, as well as relatively lower tungsten leakage from the divertor. Modeling can also reproduce the improved detachment seen as the strike point moves inboard of the slot vertex. While we can explain the effects of the most important parameters causing energy dissipation in these slot divertors, it remains that many aspects of their behavior cannot be accurately modeled using state-of-art codes such as SOLPS-ITER. This is of concern for future model-driven designs utilizing similar V-shaped geometries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1985

Modeling a high energy gamma-ray telescope

A Monte Carlo program was developed to simulate a high energy gamma-ray telescope using a coded aperture mask (CAMTRAC). The ultimate purpose of the calculation will be to determine the optimum design parameters for such an instrument. The model can also used to ascertain under what conditions CAMTRAC performance is superior to that of conventional telescopes which employ electron-positron pair direction as the sole means of determining gamma ray direction.

Jenkins, T. L.↗
DOE OSTI2021

A control oriented comprehensive degradation model for battery energy storage system life prediction

In order to ensure that stationary battery energy storage systems (BESSs) provide reliable energy buffering, both for power quality and economic purposes, the degradation must be considered. Cell degradation involves various side reactions and is highly dependent on its operating conditions. To accurately track cell degradation and predict its impact on battery behavior, a comprehensive physics-based degradation model based on an electrolyte phase-enhanced single particle (SP) model is developed. Key degradation physics, namely solid electrolyte interphase (SEI) layer formation and growth, Li plating on the graphite anode, and Mn dissolution on cathode of nickel-cobalt-manganese oxide (NMC622) are considered. The model is validated against experimental capacity data. The results reveal that the deposition rate of both SEI layer and Li metal increase as the charge voltage increases. At the cathode side, the solvent oxidation rate determines the Mn dissolution rate. As a result, the volume fraction of NMC622 in the cathode continually decreases at a gradually increasing rate.

25 ENERGY STORAGE↗
DOE OSTI2026

District-Scale Analysis of Electricity Load and Strategies to Improve Energy Reliability Using Prototype District Models

Projected increases in electricity demand in the U.S. highlight the urgent need for effective load management to ensure grid reliability. As the building sector accounts for approximately 75% of electricity usage, enhancing energy efficiency and flexibility in this sector is crucial. Adopting district-level approaches offers significant advantages over traditional individual building analyses by enabling shared infrastructure and economies of scale. To navigate the data and computational challenges associated with modeling energy at the district level, prototype district models have been proposed as holistic, system-level solutions that capture complex interactions within typical configurations. This study presents these models as a reference tool for analyzing district-scale energy systems across various climate zones in the U.S. Developed with input from stakeholders, these models integrate varied building characteristics, inter-building connections, and energy system interactions. A case study utilizing the Urban Edge prototype district model, implemented on the URBANopt™ platform, evaluates multiple demand scenarios and the impact of distributed energy resources such as fuel-fired backup generators, photovoltaic systems, and batteries. Findings suggest that while new electric systems can significantly reduce annual energy use, they may also elevate peak electricity loads, with a notable 43% increase in heating-dominant climate zone 5B. The optimal backup power solutions vary based on location, influenced by factors such as utility rates and incentives. For example, PV and batteries perform well in high-cost regions like New York City, while diesel backup generators are more suitable for backup needs in climate zone 3A, such as Atlanta. Thus, this research highlights the importance of prototype district models for future district-scale energy planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking

The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. In conclusion, the interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Modeling and Integration of Flexible Demand in Heat and Electricity Integrated Energy System

This paper is focused on utilizing customers' flexible energy demand, including both heat demand and electricity demand, to provide balancing resources and relieve the difficulties of integrating variable wind power with the combined heat and power. The integration of heat and electricity energy systems providing customers with multiple options for fulfilling their energy demand is described. Customer aggregators are introduced to supply downstream demand in the most economical way. Controlling customers' energy consumption behaviors enables aggregators to adjust their energy demand in response to supply conditions. Incorporating aggregators' flexible energy demand into the centralized energy dispatch model, a two-level optimization problem (TLOP) is first formed where the system operatormaximizes social welfare subject to aggregators' strategies, which minimize the energy purchase cost. Furthermore, the subproblems are linearized based on several reasonable assumptions. Optimal conditions of the subproblems are then transformed as energy demands to be described as explicit piecewise-linear functions of electricity prices corresponding to the demand bid curves. In this way, the TLOP is transformed to a standard optimization problem, which requires aggregators to only submit a demand bid to run the centralized energy dispatch program. All the parameters pertaining to the aggregators' energy consumption models are internalized in the bid curves. The proposed technique is illustrated in a modified testing system.

combined heat and power (CHP)↗
NASA NTRS1994

Electrical characterization of 6H crystalline silicon carbide

Crystalline silicon carbide (SiC) substrates and epilayers, undoped as well as n- and p-doped, have been electrically characterized by performing Hall effect and resistivity measurements (van der Pauw) over the temperature range of approximately 85 K to 650 K (200 K to 500 K for p-type sample). By fitting the measured temperature dependent carrier concentration data to the single activation energy theoretical model: (1) the activation energy for the nitrogen donor ranged from 0.078 eV to 0.101 eV for a doping concentration range of 10(exp 17) cm(exp -3) to 10(exp 18) cm(exp -3) and (2) the activation energy for the aluminum acceptor was 0.252 eV for a doping concentration of 4.6 x 10(exp 18) cm(exp -3). By fitting the measured temperature dependent carrier concentration data to the double activation energy level theoretical model for the nitrogen donor: (1) the activation energy for the hexagonal site was 0.056 eV and 0.093 eV corresponding to doping concentrations of 3.33 x 10 (exp 17) cm(exp -3) and 1.6 x 10(exp 18) cm(exp -3) and (2) the activation energy for the cubic site was 0.113 and 0.126 eV corresponding to doping concentrations of 4.2 x 10(exp 17) cm(exp -3) and 5.4 x 10(exp 18) cm(exp -3).

Lempner, Stephen E.↗
DOE OSTI2019

Developing Analytical Techniques for Modeling Construction/Infrastructure of Energy Systems - ISSST2019

This work was presented at the International Symposium on Sustainable Systems and Transportation (ISSST) 2019 Conference in Portland, OR on June 25, 2019. Using detailed component-level cost estimates for new fossil power plants as documented in Cost and Performance Baseline for Fossil Energy Plants Volume 1a: Bituminous Coal (PC) and Natural Gas to Electricity Revision 3, economic demands for the construction of a power plant and the manufacturing of its components are estimated. The model produces national average construction impact results for the life cycle of a power plant. This output highlights areas of further research that help to define valuable data collection efforts.

Carlson, Derrick R.↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗