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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Efficient Simulation of Cascading Outages Using an Energy Function-Embedded Quasi-Steady-State Model

Here, this paper proposed an energy function-embedded quasi-steady-state model for efficient simulation of cascading outages on a power grid while addressing transient stability concerns. Compared to quasi-steady-state models, the proposed model incorporates short-term dynamic simulation and an energy function method to efficiently evaluate the transient stability of a power grid together with outage propagation without transient stability simulation. Cascading outage simulation using the proposed model conducts three steps for each disturbance such as a line outage. First, it performs time-domain simulation for a short term to obtain a post-disturbance trajectory. Second, along the trajectory, the system state with the local maximum potential energy is found and used as the initial point to search for a relevant unstable equilibrium by Newton's method. Third, the transient energy margin is estimated based on this unstable equilibrium to predict an out-of-step condition with generators. The proposed energy function-embedded quasi-steady-state model is tested in terms of its accuracy and time performance on an NPCC 140-bus power system and compared to a quasi-steady-state model embedding transient stability simulation.

Guo, Zhenping [Univ. of Tennessee, Knoxville, TN (↗
DOE OSTI2026

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗
DOE OSTI2020

GADRAS Batch Inject Tool User Guide

Gamma Detector Response and Analysis Software (GADRAS) is used by the radiation detection and emergency response community to perform modeling and spectral analysis for gamma detector systems. Built into GADRAS is the ability to define a detector, geometry, background characteristics and source composition to generate synthetic spectra for drills and exercises (injects). Consequence Management is currently in development of a sample result data simulator tool in which a deposition model is probed for source conditions at moments in time and locations in space. These values are used to generate realistic sample results for use in drills and exercises. In addition to sample results, there is a need to simulate the actual spectra that would be observed in the field by downlooking HPGe instruments given a deposition activity. This way, the FRMAC Gamma Spectroscopist can practice their process of generating quantified results from spectra on realistic data as well. Recognizing the decades of work done in GADRAS to accurately generate synthetic spectra, this team decided to build a link between the new simulator and GADRAS to generate these spectra quickly and easily. The simulator tool will generate a file that specifies the name of the spectra, its location, date/time of measurement, duration of measurement, height off the ground, and the deposition activity and age for every radionuclide in the simulation. Then, a new tool within the Inject Tab of GADRAS was developed to read in this file given a detector selection and generate In-Situ spectra for each row in the file in any file format the user chooses. This way, simulation cell staff can take these files and then upload them to the appropriate data system (RAMS or RadResponder) for use during drills and exercises. An advanced feature of this tool allows for generating any spectra given an appropriate model and mapping of source to model layer in the batch inject tool. This way, spectra from field sample counts, mobile laboratories, or even fixed laboratories can be generated in bulk given an estimate of the radioactivity concentration or total radioactivity in an import file. This expands the capabilities of this tool a great deal and will make it a more useful tool for CM and others to help estimate detector response for nearly any situation. This user guide will explain the steps needed to perform a batch inject file generation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Coupling LaGrit unstructured mesh generation and model setup with TOUGH2 flow and transport: A case study

Furthermore, there are many applications in science and engineering modeling where an accurate representation of a complex model geometry in the form of a mesh is important. In applications of flow and transport in subsurface porous media, this is manifest in models that must capture complex geologic stratigraphy, structure (faults, folds, erosion, deposition) and infrastructure (tunnels, boreholes, excavations). Model setup, defined as the activities of geometry definition, mesh generation (creation, optimization, modification, refine, de-refine, smooth), assigning material properties, initial conditions and boundary conditions requires specialized software tools to automate and streamline the process. In addition, some model setup tools will provide more utility if they are designed to interface with and meet the needs of a particular flow and transport software suite. A control volume discretization that uses a two point flux approximation is for example most accurate when the underlying control volumes are 2D or 3D Voronoi tessellations. In this paper we will present the coupling of LaGriT, a mesh generation and model setup software suite and TOUGH2 to model subsurface flow problems and we show an example of how LaGriT can be used as a model setup tool for the generation of a Voronoi mesh for the simulation program TOUGH2. To generate the MESH file for TOUGH2 from the LaGriT output a standalone module Lagrit2Tough2 was developed, which is presented here and will be included in a future release of LaGriT. Here in this paper an alternative method to generate a Voronoi mesh for TOUGH2 with LaGriT is presented and thanks to the modular and command based structure of LaGriT this method is well suited to generating a mesh for complex models.

58 GEOSCIENCES↗
DOE OSTI2019

Adiabatic Heating in Modular Plasticity Models

Plastic deformations in metals are dissipative. Some fraction of the dissipated mechanical energy (plastic work) is converted into thermal energy and serves as a heat source. In cases where the heat cannot be readily transferred to the environment, the local temperature will increase thereby producing variations in mechanical behaviors associated with temperature-dependent properties (e.g. thermal softening due to decreasing yield strengths). This issue is often referred to as "adiabatic heating as an adiabatic temperature condition corresponds to the limiting case where no heat transfer takes place. The impact of converting plastic work into heat on the mechanical response of metals has been long studied. Nonetheless, it still remains an issue. For instance, with respect to ductile failure, the second Sandia Fracture Challenge noted that accounting for plastic heat generation was necessary for predictions under dynamic loading conditions. Furthermore, both experimental and modeling efforts continue to be pursued to better describe and understand the effect of plastic work conversion into heat on structural responses. Noting the need for capturing plastic work conversion into heat in structural analyses, a simple and fairly traditional representation of these responses has been added into existing modular plasticity models in the Library of Advanced Materials for Engineering (LAME). Here, these capabilities are briefly described with the underlying theory and numerical implementation discussed in Sections 2 and 3, respectively. Examples of syntax are given in Section 4 and some verification exercises are found in Section 5. Simple structural analyses are presented in Section 6 to briefly highlight the impact of these features and concluding thoughts are given.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The X-Ray Emission Effectiveness of Plasma Mirrors: Reexamining Power-Law Scaling for Relativistic High-Order Harmonic Generation

Ultrashort pulsed lasers provide uniquely detailed access to the ultrafast dynamics of physical, chemical, and biological systems, but only a handful of wavelengths are directly produced by solid-state lasers, necessitating efficient high-power frequency conversion. Relativistic plasma mirrors generate broadband power-law spectra, that may span the gap between petawatt-class infrared laser facilities and x-ray free-electron lasers; despite substantial theoretical work the ultimate efficiency of this relativistic high-order-harmonic generation remains unclear. We show that the coherent radiation emitted by plasma mirrors follows a power-law distribution of energy over frequency with an exponent that, even in the ultrarelativistic limit, strongly depends on the ratio of laser intensity to plasma density and exceeds the frequently quoted value of -8/3 over a wide range of parameters. The coherent synchrotron emission model, when adequately corrected for the finite width of emitting electron bunches, is not just valid for p-polarized light and thin foil targets, but generally describes relativistic harmonic generation, including at normal incidence and with finite-gradient plasmas. Our numerical results support the ω-4/3 scaling of the synchrotron emission model as a limiting efficiency of the process under most conditions. The highest frequencies that can be generated with this scaling are usually restricted by the width of the emitting electron bunch rather than the Lorentz factor of the fastest electrons. The theoretical scaling relations developed here suggest, for example, that with a 20-PW 800-nm driving laser, 1 TW/harmonic can be produced for 1-keV photons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Theory of highly efficient multiexciton generation in type-II nanorods

Multiexciton generation, by which more than a single electron–hole pair is generated on optical excitation, is a promising paradigm for pushing the efficiency of solar cells beyond the Shockley–Queisser limit of 31%. Utilizing this paradigm, however, requires the onset energy of multiexciton generation to be close to twice the band gap energy and the efficiency to increase rapidly above this onset. This challenge remains unattainable even using confined nanocrystals, nanorods or nanowires. Here, we show how both goals can be achieved in a nanorod heterostructure with type-II band offsets. Using pseudopotential atomistic calculation on a model type-II semiconductor heterostructure we predict the optimal conditions for controlling multiexciton generation efficiencies at twice the band gap energy. For a finite band offset, this requires a sharp interface along with a reduction of the exciton cooling and may enable a route for breaking the Shockley–Queisser limit.

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

Generative AI for Wind-Wave Inflows

Extreme wind-wave conditions can create dangerous or damaging environmental conditions for sea-based structures and watercrafts. However, the model of consistent, coupled wind-wave flow conditions is computational expensive, even for simple flow scenarios. This work examines the use of diffusion-based generative artificial intelligence tools to accelerate the generation of these two-phase flows and to characterize probabilities of various states to help with the generation of extreme patterns.

17 WIND ENERGY↗
DOE OSTI2017

ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon

We present a new force field and development scheme for atomistic simulations of materials under extreme conditions. These models, which explicitly include twoand three-body interactions, are generated by fitting linear combinations of Chebyshev polynomials through force matching to trajectories from Kohn–Sham density functional theory (DFT).We apply our method to liquid carbon near the diamond/graphite/liquid triple point and at higher densities and temperatures, where metallization and manybody effects may be substantial. We show that explicit inclusion of three-body interaction terms allows our model to yield improved descriptions of both dynamic and structural properties over previous empirical potential efforts, while exhibiting transferability to nearby state points. The simplicity of our functional form and subsequent efficiency of parameter determination allows for extension of DFT to experimental time and length scales while retaining most of its accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

We report molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet’s theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different datasets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T dataset (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this dataset with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a dataset comprising of 13 heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending the MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone–backbone interaction dataset to an accuracy of 0.28 kcal/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Coherent fluctuations in a partially magnetized small magnetron discharge operating on molecular nitrogen

We report experimental data on rotating plasma disturbances in a quasi-steady direct-current magnetron discharge operating with molecular nitrogen at pressures of 27, 40, 67, and 133 Pa. Experiments with a segmented anode measuring the local current flow reveal up to 12 coherent structures (“spokes”) propagating in the +E×B and −E×B azimuthal directions. The current–voltage characteristics are studied and found to follow a negative resistance profile at the lower discharge currents. The existence of coherent structures and the number of spokes observed depend on the discharge current and operating pressure. Lower pressures are more favorable to the retrograde −E×B spoke rotation, which is observed in the 27, 40, and 67 Pa discharges. The results are interpreted with a gradient-driven drift wave model, and using the dispersion relation, we show that the propagation direction depends on the orientation of the local electric field within the plasma. Using an electron fluid model, we show how experimental conditions can lead to field reversals that generate −E×B spoke rotation in the lower pressure discharges.

Physics↗
DOE OSTI2023

Microgrid Modeling for Stability Analysis

Here this document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid (MG) Dynamic Modeling, IEEE Power and Energy Society, Tech. Rep. PES-TR106, 2023. In this paper, the major issues and challenges in microgrid modeling for stability analysis are discussed, and a review of state-of-the-art modeling approaches and trends is presented. In the context of the IEEE 1547 standard, the document covers issues associated with component models for MG dynamic studies and simulations, including generator and grid modeling, full and average converter models, unbalanced and balanced system conditions, dynamic and static loads, protection requirements, and detailed and simplified controls considering communications delays, packet losses, and security issues. Considering the future integration of grids and MGs to form broad integrated networks, a discussion is presented of the use of phasor vis-à-vis electromagnetic transient simulation tools for MG dynamic stability studies, as well as modeling scale-up issues and MG equivalent models. Specifically white-, grey-, and black-box models, are presented. This TF paper and companion report constitute a modeling guide for R&D groups working on developments and standards of MGs with a focus on stability issues.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

An Implicit Approach to Phase Field Modeling of Solidification for Additively Manufactured Alloys [Slides]

We are leveraging modern algorithms and computational science to provide a route to predictive simulation of microstructure evolution on emerging exascale architectures. We are utilizing the fastest supercomputers in the world for modeling and simulation of microstructure evolution for generation of data under AM conditions. Solidification conditions in AM can be tailored for the reliable design of materials to specific performance requirements. Developing computational tools to further characterize alloys and correlate the processing-structure-properties-performance (PSPP) relationship.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Battery models, systems, and methods using robust fail-safe iteration free approach for solving differential algebraic equations

Battery models using robust fail-safe iteration free approach for solving Differential Algebraic Equations, and associated systems and methods are disclosed. In one embodiment, a method includes generating a model of the rechargeable battery; determining one or more initial conditions for one or more algebraic variables of the model using a solver; holding differential variables of the model static by a switch function while determining the one or more initial conditions; applying the initial conditions to the model by the switch function; and determining one or more parameters for the rechargeable battery by solving the algebraic and differential equations.

25 ENERGY STORAGE↗
DOE OSTI2018

Large-eddy simulations for hill terrains: validation with wind-tunnel and field measurements

Development and validation of large-eddy simulation (LES) framework to study atmospheric boundary layer flows over complex terrains is reported here. The LES model uses the fourth-order time-accurate Runge–Kutta scheme and a fractional step method. The inflow boundary conditions are generated by using the so-called recycling (or mapping) technique. Evaluation of potential in-land wind park locations is the main application of the validated model. In this work, LES are carried out for turbulent boundary-layer flows over both simple and complex hill geometries. The prediction of the flow separation and reattachment length for a steeper wind-tunnel hill was closer to the measurements than previous numerical studies reported by other authors for the same hill geometry. For the complex hill case, the LES model showed good agreement with full-scale measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

U.S. energy sector impacts of technology innovation, fuel price, and electric sector CO2 policy: Results from the EMF 32 model intercomparison study

We study the impact of fuel prices, technology innovation, and a CO2 emissions reduction policy on both the electric power and end-use sectors by comparing outputs from four U.S. energy-economic models through the year 2050. Achieving innovation goals decreases CO2 emissions in all models, regardless of natural gas price, due to increased energy efficiency and low-carbon generation becoming more cost competitive. For the models that include domestic natural gas markets, achieving innovation goals lowers wholesale electricity prices, but this effect diminishes as projected natural gas prices increase. Higher natural gas prices lead to higher wholesale electricity prices but fewer coal capacity retirements. A CO2 electric power sector emissions cap influences electric sector evolution under reference technology assumptions but has little to no incremental influence when added to innovation goals. Long-term, meeting innovation goals achieves a generation mix with similar CO2 emissions compared to the CO2 policy but with smaller increases to wholesale electricity prices. In the short-term, the relative effect on wholesale prices differs by model. Finally, higher natural gas prices, achieving innovation goals, and the combination of the two, increases the amount of renewable generation that is cost-effective to build and operate while slowing the growth of natural-gas fired generation, which is the predominant generation type in 2050 under reference conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Numerical investigation of wind turbine wakes under high thrust coefficient

Abstract We study wind turbine wakes of rotors operating at high thrust coefficients ( C T > 24/25) using large‐eddy simulations with a rotating actuator disk model. Wind turbine wakes at high thrust coefficients are different from wakes at low thrust coefficients. Wakes behave differently at high thrust, with increased turbulence and faster recovery. Lower induction in the wake is achieved because wakes in high‐thrust conditions recover much faster than in normal operating conditions. This enhanced recovery is possible thanks to the turbulence generated in the near wake. We explore the mechanism behind this behavior and propose a simple model to reproduce it. We also propose a Gaussian fit for the wakes under high‐thrust conditions and use it use it to initialize an Ainslie type model within the FAST.Farm framework.

FAST.Farm↗