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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impacts of Permeability Heterogeneity and Background Flow on Supercritical CO 2 Dissolution in the Deep Subsurface

Motivated by CO 2 capture and sequestration (CCS) design considerations, we consider the coupled effects of permeability heterogeneity and background flow on the dissolution of a supercritical CO 2 lens into an underlying deep, confined aquifer. We present the results of a large-scale Monte Carlo simulation study examining the interaction of background flow rate and three parameters describing multi-Gaussian log-permeability fields: mean, variance, and correlation length. Hundreds of high-resolution simulations were performed using the PFLOTRAN finite volume software to model CO 2 dissolution in a kilometer-scale aquifer over 1,000 years. Predictive dimensionless scaling relationships relating CO 2 dissolution rate to heterogeneity statistics, Rayleigh (Ra) and Péclet (Pe) numbers were developed for both gravitationally dominated free convection to background flow-dominated forced convection regimes. An empirical criterion, Pe = Ra 3/4 , was discovered for regime transition. All simulations converged quickly to a quasi-steady, approximately linear dissolution rate. However, this rate displayed profound variability between permeability field realizations sharing the same heterogeneity statistics, even under mild permeability heterogeneity. In general, increased heterogeneity was associated with a lower mean and higher variance of dissolution rate, undesirable from a CCS design perspective. The relationship between dissolution rate and background flow was found to be complex and nonlinear. Dimensionless scaling relationships were uncovered for a number of special cases. Results call into question the validity of the Boussinesq approximation in the context of modest-to-high background flow rates and the general applicability of numerical simulations without background flow.

42 ENGINEERING↗
DOE OSTI2023

A comparison of Eulerian and semi-Lagrangian approaches for modeling stream water quality

This paper describes and compares some of the advantages and limitations of Eulerian and Lagrangian approaches to water quality modeling and introduces a mixed Eulerian-Lagrangian (or semi-Lagrangian) methodology that captures the strengths of both approaches. The semi-Lagrangian modeling approach is applied to advection-dominated rivers, and flexibly ensures unconditional stability for all time step durations and grid segmentations. The semi-Lagrangian modeling approach is demonstrated by applying it to estimate the dissolved oxygen concentrations in the Sava River in Slovenia, focusing on aspects of the methodology and findings that would be of broad interest to managers of water quality in fluvial water bodies. Results of comparisons of the semi-Lagrangian model with the Eulerian-based QUAL2K model in steady and non-steady scenarios demonstrate that while both models are fully capable of producing satisfactory results when optimally configured, the semi-Lagrangian approach offers accuracy and stability without sensitivity to the interaction of time step size and computational grid segmentation scheme.

Sava River↗
DOE OSTI2019

Utilizing guanine-coordinated Zn 2+ ions to determine DNA crystal structures by single-wavelength anomalous diffraction

The experimental phase determination of crystal structures of nucleic acids and nucleic acid–ligand complexes would benefit from a facile method. Even for double-stranded DNA, software-generated models are generally insufficiently accurate to serve as molecular replacement search models, necessitating experimental phasing. Here, it is demonstrated that Zn 2+ ions coordinated to the N7 atom of guanine bases generate sufficient anomalous signal for single-wavelength anomalous diffraction (SAD) phasing of DNA crystal structures. Using zinc SAD, three crystal structures of double-stranded DNA oligomers, 5'-AGGGATCCCT-3', 5'-GGGATCCC-3' and 5'-GAGGCCTC-3', were determined. By determining the crystal structure of one of these oligomers, GAGGCCTC, in the presence of Mg 2+ instead of Zn 2+ , it was demonstrated that Zn 2+ is not structurally perturbing. These structures allowed the analysis of structural changes in the DNA on the binding of analogues of the natural product mithramycin to two of these oligomers, AGGGATCCCT and GAGGCCTC. Zinc SAD may become a routine approach for determining the crystal structures of nucleic acids and their complexes with small molecules.

DNA–drug complexes↗
DOE OSTI2024

Heave Plate Hydrodynamic Coefficients for Floating Offshore Wind Turbines - A Compilation of Data

The National Renewable Energy Laboratory's OpenFAST software is utilized by academics and industry professionals alike to simulate offshore wind turbines. The software's modeling of hydrodynamic loads on heave plates attached to these structures relies on user-specified hydrodynamic coefficients. To guide the proper selection of these coefficients and potentially develop a new functionality within OpenFAST that automatically prescribes and/or adjusts the heave-plate hydrodynamic coefficients, we review past literature to examine the dependence of the added mass, damping, and drag coefficients on various relevant nondimensional parameters, including the Keulegan-Carpenter number, the frequency parameter, and the plate thickness ratio. Existing data in the literature show strong dependence of the hydrodynamic coefficients on the Keulegan-Carpenter number. We observe consistent trends across a range of different plate geometry, plate porosity, and flow conditions. Secondary dependence of the coefficients on the frequency parameter and plate thickness ratio is also present.

added mass↗
DOE OSTI2023

Physical vapor deposition simulator by graphical processor unit ray casting

This paper presents fast, accurate software for modeling physical vapor deposition systems over irregular surfaces. The model is implemented using graphics processing unit (GPU) ray casting. Applied models are viewed as a cross section of the area of interest. Given evaporation rate, time, and angular profiles in a vacuum system, an iterative time-step approach for calculating deposition profiles is calculated in the GPU architecture following a ballistic modeling approach. Thin-film technologies for the electronics industry will require evaporations on complex surfaces. Depending on the nature of the surface, a uniform thin film across the topology is wanted for various device parameters. The ray casting method is tested against various profiles. The code is freely distributed on GitHub (see https://github.com/adam-r-thomas/PVDS).

Engineering↗
DOE OSTI2019

Bayesian Segmentation (BCNN) v.1

Deep learning has been applied with great success to the segmentation of 3D X-Ray Computed Tomography (CT) scans. Establishing the credibility of these segmentations requires uncertainty quantification (UQ) to identify problem areas. Bayesian neural networks (BNNs), which use variational inference to learn the posterior distribution of the neural network weights, have been proposed to incorporate UQ into deep learning models. This software is an implementation of a novel 3D Bayesian convolutional neural network (BCNN) that provides accurate binary segmentations and uncertainty maps for 3D volumes. In particular, the uncertainty maps generated by this BCNN capture continuity and visual gradients, making them interpretable as confidence intervals for segmentation usable in numerical simulations. SAND2019-12927 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

LaBonte, TylerMatthew↗
DOE OSTI2020

Scalable Linked Dynamic Equilibrium (SLiDE)

SLiDE (Scalable Linked Dynamic Equilibrium) replicates the blueNOTE (National Open source Tools for general Equilibrium analysis) datastream from the Wisconsin National Data Consortium. From there, it produces a recursive dynamic computable general equilibrium model of the US states and eventually the global/national economies. The datastream operations have been set up to allow for scalability of both regions and sectors represented. The model has the capability to flexibly define functional forms similar to other general equilibrium model construction software.

Hughes, Caroline↗
DOE OSTI2022

Framework For Optimization Of Resources And Economics Ecosystem

The Framework for Optimization of ResourCes and Economics is a collection of software tools, models, and datasets acquired and developed under the Integrated Energy Systems (IES) program to enable analysis of technical and economic viability of myriad IES configurations. FORCE is the consolidating interface and data repository for all the IES toolsets ranging from macro technoeconomic analysis to transient process modeling and experimental validation for integrated energy systems.

Frick, KonorL↗
DOE OSTI2022

Multi-Channel Time-Frequency Analysis

This software contains models for multi-channel time series data. The models are time-frequency descriptions of the data. Also included are prediction performance evaluation tools. These models can be applied to electrophysiology data such as electrocorticography.

Kaplan, AlanD↗
DOE OSTI2022

California Load Flexibility Research and Development Hub (CalFlexhub) v0.1

This repository will host the software, data models and documentation developed as part of the California Energy Commission (CEC) funded California Load Flexibility Research and Development Hub (CalFlexHub) project. The main objectives of CalFlexHub are: - Identify, develop, evaluate, demonstrate and deploy cost-effective, scalable, building load-flexible (LF) technologies that are consistent with building energy efficiency, appliance, and load management standards. - Create a portfolio of LF RDD&D technology projects across various building types and sizes including single-family-residential, multi-family, commercial buildings and integrated campuses. - Deploy LF technologies to demonstrate the ability for 99% of the state's customers to receive the load management standards price and marginal GHG signals.

Prakash, Anand Krishnan↗
DOE OSTI2023

Hydrogen Extremely Low Probability of Rupture Version 1

SAND2024-08511O The Hydrogen Extremely Low Probability of Rupture (HELPR) software toolkit provides probabilistic fracture assessments of natural gas infrastructure for transporting hydrogen. Using probabilistic sampling and sensitivity analysis—along with engineering fatigue and fracture mechanics— the software develops models that can predict structural integrity measures for natural gas pipelines under stress. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Schroeder, Benjamin↗
DOE OSTI2025

FLORIS Wake Map [SWR-25-71]

The FLORIS Wake Map repository is a set of tools for creating maps of cluster wake impacts based on NREL's FLORIS software and modeling tool (https://github.com/nrel/floris). The floriswakemap package comprises two main modules: the wake_map module, dedicated to the WakeMap class, and the area_selector module, dedicated to the AreaSelector class. The WakeMap class is used to generate maps of wake impacts on both new and existing clusters, while the AreaSelector class provides selections of candidate areas for new developments based on an instantiated WakeMap object.

Sinner, Michael (Misha) [National Renewable Energy↗
DOE OSTI2026

TransTissueFormer

This software builds models for translating transcriptomic profiles between tissues.

Cong, Guojing↗
DOE OSTI2026

MCNPy

SAND2026-20425O MCNPy runs and analyzes simulations from MCNP, a software that models radiation transport of neutrons and gamma rays. MCNPy uses Python to start MCNP, retrieve event data files, and convert them into graph structures for detailed analysis. It offers visualization tools, including 2D views of particle histories, making complex simulation data easier to interpret for researchers and engineers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Nowack, Aaron [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2026

GAT (Grid Analysis Toolkit) [SWR-25-41]

Grid Analysis Toolkit (GAT) is a unified Python API and plotting for power system PCM and CEM results (Sienna, PLEXOS, ReEDS™). It's a toolkit for wrangling data for Bulk Grid Dispatch and Transmission Analysis. GAT aims to provide simplified access to PCM and CEM results in a standard format while also allowing raw data access to underlying datasets specific to the model. This software can also be found on PyPI at For plotting, GAT defaults to standard National Lab of the Rockies (NLR) color schemes and standard styles while allowing customization.

Webb, Micah [National Laboratory of the Rockies (N↗
DOE OSTI2022

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Data from Adopting Bioenergy Crops: Does Farmers’ Attitude Toward Loss Matter?

We apply prospect theory to examining farmers’ economic incentives to divert a share of their land to bioenergy crops (miscanthus and switchgrass in this study). Numerical simulation is conducted for 1,919 rain‐fed U.S. counties to identify the impact of loss aversion on bioenergy crop adoption, and how this impact is influenced by biomass price, discount rate, credit constraint status, and policy instruments. Results show that ignoring farmer’s loss aversion causes overestimation of miscanthus production but underestimation of switchgrass production, particularly when farmers are credit constrained and have a high discount rate. We find that establishment cost subsidy induces more miscanthus production whereas subsidized energy crop insurance induces more switchgrass production. The efficacy of these two policy instruments, measured by biomass production increased by per dollar of government outlay, depends on the magnitude of farmers’ loss aversion and discount rate.

Economics↗
DOE OSTI2024

Data for "Design of Diverse, Functional Mitochondrial Targeting Sequences Across Eukaryotic Organisms Using Variational Autoencoder"

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

AI/ML↗