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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 235 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Stacking Fault Energy Based Alloy Screening for Hydrogen Compatibility

The selection of austenitic stainless steels for hydrogen service is challenging since there are few intrinsic metrics that relate alloy composition to hydrogen degradation. One such metric, explored here, is intrinsic stacking fault energy. Stacking fault energy has an influence on the character and structure of dislocations and on the formation of secondary crystalline phases created during mechanical deformation in austenitic alloys. In this work, a data-driven model for the intrinsic stacking fault energy of common austenitic stainless steel alloys is applied to compare the relative degradation of tensile performance in the presence of hydrogen. A transition in the tensile reduction of area of both 300-series and manganese stabilized stainless steels is observed at a calculated stacking fault energy of approximately 43 mJ m -2 , below which pronounced hydrogen degradation on tensile ductility is observed. The model is also applied to suggest alloying strategies for low nickel austenitic stainless steels for hydrogen service. Lastly, through this investigation, we find that calculated intrinsic stacking fault energy is a high-throughput screening metric that enables the ranking of the performance of a diverse range of austenitic stainless steel compositions, as well as the identification of new alloys, with regard to hydrogen compatibility.

08 HYDROGEN↗

DOE OSTI2026

Investigating charm quark energy loss in medium with the nuclear modification factor of D 0 -tagged jets

The nuclear modification factor R AA of charm jets, identified by the presence of a D 0 meson among the jet constituents, has been measured for the first time in Pb–Pb collisions at a centre-of-mass energy per nucleon pair $\sqrt{s_{NN}} = 5.02$ TeV with the ALICE detector at the LHC. The D 0 mesons and their charge conjugates are reconstructed from the hadronic decay D 0 → K – π + . Jets are reconstructed from D 0 -meson candidates and charged particles using the anti-k T algorithm with jet resolution parameter R = 0.3, in the jet transverse momentum (p T ) range 5 < $p^{ch}_{T}$$^{jet}$ < 50 GeV/c and pseudorapidity |η ch jet| < 0.6. A hint of reduced suppression in the charm-jet R AA is observed in comparison to inclusive jets in central Pb–Pb collisions with a significance of about 2σ in 20 < $p^{ch}_{T}$$^{jet}$ < 50 GeV/c, suggesting the in-medium energy loss to depend on both the difference between quark and gluon coupling strength (Casimir colour-charge effect) and quark mass (dead-cone effect). The data are compared with model calculations that include mass effects in the in-medium energy loss. Several state-of-the-art models are consistent with the data, with the LIDO model providing the best description of the data in the common kinematic range of inclusive and D 0 -tagged jets, highlighting the role of mass effects in interpreting the results.

Acharya, S. (ORCID:0000000292135329)↗

DOE OSTI2023

The Impact of Inherited Morphology on Sandbar Migration During Mild Wave Seasons

Sandbars are ubiquitous morphologic features found in the nearshore environment throughout the world, yet predictive capabilities of their evolution remain limited. In order to provide new insights on the relevant processes controlling sandbar morphodynamics, this study uses a 41-year record of 637 monthly cross-shore profiles from Duck, North Carolina, USA, to derive complex empirical orthogonal functions representative of the two dominant modes of sandbar migrations: offshore and onshore propagation. Interference of these two modes produces commonly observed sandbar states. While mild wave energies are traditionally assumed to drive onshore sandbar migration, the offshore mode is repeatedly seen to dominate sandbar migration in mild wave seasons following anomalously high late-winter wave energy and when the inherited morphology is composed of a single bar or terrace, as opposed to a more common two-bar state. A data-derived conceptual model is presented synthesizing the effect of antecedent morphology and wave climate chronology on interannual trends in net offshore sandbar migration.

58 GEOSCIENCES↗

DOE OSTI2020

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Short-crack growth behaviour in various aircraft materials

The results of the first phase of an AGARD Cooperative Test Program on the behavior and growth of short fatigue cracks are reviewed. The establishment of a common test method, means of data collection/analysis and crack growth modeling in an aircraft alloy AA 2024-T3 are described. The second phase allowed testing of various materials and loading conditions. The results of this second phase are described. All materials exhibited a short-crack effect to some extent. The effect was much less evident in 4340 steel than in the other materials. For the aluminum, aluminum-lithium, and titanium alloys, short cracks grew at stress-intensity factor ranges lower, in some cases much lower, than the thresholds obtained from long crack tests. Several laboratories used the same crack growth model to analyze the growth of short cracks. Reasonable agreement was found between measured and predicted short-crack growth rates and fatigue lives.

Edwards, P. R.↗

NASA NTRS2003

Information Environments

The objective of GRC CNIS/IE work is to build a plug-n-play infrastructure that provides the Grand Challenge Applications with a suite of tools for coupling codes together, numerical zooming between fidelity of codes and gaining deployment of these simulations onto the Information Power Grid. The GRC CNIS/IE work will streamline and improve this process by providing tighter integration of various tools through the use of object oriented design of component models and data objects and through the use of CORBA (Common Object Request Broker Architecture).

Follen, Gregory J.↗

NASA NTRS2010

Modeling Broadband X-Ray Absorption of Massive Star Winds

We present a method for computing the net transition of X-rays emitted by shock-heated plasma distributed throughout a partially optically thick stellar wind from a massive star. We find the transmission by an exact integration of the formal solution, assuming the emitting plasma and absorbing plasma are mixed at a constant mass ratio above some minimum radius, below which there is assumed to be no emission. This model is more realistic than either the slab absorption associated with a corona at the base of the wind or the exospheric approximation that assumes all observed X-rays are emitted without attenuation from above the radius of optical depth unity. Our model is implemented in XSPEC as a pre-calculated table that can be coupled to a user-defined table of the wavelength dependent wind opacity. We provide a default wind opacity model that is more representative of real wind opacities than the commonly used neutral ISM tabulation. Preliminary modeling of Chandra grating data indicates that the X-ray hardness trend of OB stars with spectral subtype cars largely be understood as a wind absorption effect.

Leutenegger, Maurice A.↗

DOE OSTI2021

Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2020

Assessment of Transition Modeling Capability in OVERFLOW with Emphasis on Swept-Wing Configurations

In preparation for comparisons with data obtained from the recently concluded experiments in the National Transonic Facility at the NASA Langley Research Center on the new common research model with natural laminar flow (CRM-NLF), an assessment of the current transition modeling capability in the NASA OVERFLOW 2.2o code has been carried out. A combination of the available experimental data and linear stability analysis is used to evaluate the accuracy and robustness of these models for selected swept-wing type configurations, with significant crossflow. An additional goal for this work involves providing a comparative assessment of the relevant transition models in the context of a single flow solver and identifying model limitations as well as the potential for future improvements that would help strengthen the physical basis of such transition models. Included in this investigation is an assessment of the sensitivities of the underlying transition models to grid resolution (wall-normal, as well as streamwise and spanwise) and the values of extra input parameters such as the level of surface roughness and freestream turbulence variables. The flow configurations targeted in this assessment include the NASA NLF(2)-0415 swept-wing configuration, the sickle-shaped wing introduced by the Technical University of Braunschweig, and the wing-body configuration of the CRM model from the fourth and fifth AIAA CFD Drag Prediction Workshops.

Freestream velocity↗

DOE OSTI2020

Cooperative Research and Development Agreement between National Energy Technology Laboratory and VariGrid Explorations Inc. [Final Report]

The scope of this project is to advance the National Energy Technology Laboratory’s (NETL) Variable Grid Method (VGM) technology to commercial status thought enhancements upon prior work to produce a VGM application that can rapidly quantify multiple types of data and uncertainty commonly encountered by the subsurface energy industry. Along with development, the project will also include technology demonstrations, through applications using common industry data types and uncertainty metrics, to stress the value the VGM can offer the subsurface energy industry by providing an improved understanding of the strengths and limitations of their data, models, and analytical results. The resulting commercially ready VGM will offer interoperability with common industry software applications to ensure broad ‘plug and play’ functionality that will help keep costs relatively low and foster more rapid industry adoption.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2004

A Monte Carlo Sensitivity Analysis of CF2 and CF Radical Densities in a c-C4F8 Plasma

A Monte Carlo sensitivity analysis is used to build a plasma chemistry model for octacyclofluorobutane (c-C4F8) which is commonly used in dielectric etch. Experimental data are used both quantitatively and quantitatively to analyze the gas phase and gas surface reactions for neutral radical chemistry. The sensitivity data of the resulting model identifies a few critical gas phase and surface aided reactions that account for most of the uncertainty in the CF2 and CF radical densities. Electron impact dissociation of small radicals (CF2 and CF) and their surface recombination reactions are found to be the rate-limiting steps in the neutral radical chemistry. The relative rates for these electron impact dissociation and surface recombination reactions are also suggested. The resulting mechanism is able to explain the measurements of CF2 and CF densities available in the literature and also their hollow spatial density profiles.

Bose, Deepak↗

DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗

DOE OSTI2023

Tanana River Test Site Model Verification Using the Marine and Hydrokinetic Toolkit (MHKiT)

The marine energy (ME) industry historically lacked a standardized data processing toolkit for common tasks such as data ingestion, quality control, and visualization. The marine and hydrokinetic toolkit (MHKiT) solved this issue by providing a public software deployment (open-source and free) toolkit for the ME industry to store and maintain commonly used functionality for wave, tidal, and river energy. This paper demonstrates an initial model verification study in MHKiT. Using Delft3D, a numerical model of the Tanana River Test Site (TRTS) at Nenana, Alaska was created. Field data from the site was collected using an Acoustic Doppler Current Profiler (ADCP) at the proposed Current Energy Converter (CEC) locations. MHKiT is used to process model simulations from Delft3D and compare them to the transect data from the ADCP measurements at TRTS. The ability to use a single tool to process simulation and field data demonstrates the ease at which the ME industry can obtain results and collaborate across specialties, reducing errors and increasing efficiency.

13 HYDRO ENERGY↗

DOE OSTI2023

Reliable Measures of Spread in High Dimensional Latent Spaces

Understanding geometric properties of the latent spaces of natural language processing models allows the manipulation of these properties for improved performance on downstream tasks. One such property is the amount of data spread in a model’s latent space, or how fully the available latent space is being used. We demonstrate that the commonly used measures of data spread, average cosine similarity and a partition function min/max ratio I (V), do not provide reliable metrics to compare the use of latent space across data distributions. We propose and examine six alternative measures of data spread, all of which improve over these current metrics when applied to seven synthetic data distributions. Of our proposed measures, we recommend one principal component-based measure and one entropy-based measure that provide reliable, relative measures of spread and can be used to compare models of different sizes and dimensionalities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗

NASA NTRS2002

Application of the generalized vertical coordinate ocean model for better representing satellite data

It is found that two adaptive parametric functions can be introduced into the basic ocean equations for utilizing the optimal or hybrid features of commonly used z-level, terrain- following, isopycnal, and pressure coordinates in numerical ocean models. The two parametric functions are formulated by combining three techniques: the arbitrary vertical coordinate system of Kasahara (1 974), the Jacobian pressure gradient formulation of Song (1 998), and a newly developed metric factor that permits both compressible (non-Boussinesq) and incompressible (Boussinesq) approximations. Based on the new formulation, an adaptive modeling strategy is proposed and a staggered finite volume method is designed to ensure conservation of important physical properties and numerical accuracy. Implementation of the combined techniques to SCRUM (Song and Haidvogel1994) shows that the adaptive modeling strategy can be applied to any existing ocean model without incurring computational expense or altering the original numerical schemes. Such a generalized coordinate model is expected to benefit diverse ocean modelers for easily choosing optimal vertical structures and sharing modeling resources based on a common model platform. Several representing oceanographic problems with different scales and characteristics, such as coastal canyons, basin-scale circulation, and global ocean circulation, are used to demonstrate the model's capability for multiple applications. New results show that the model is capable of simultaneously resolving both Boussinesq and non-Boussinesq, and both small- and large-scale processes well. This talk will focus on its applications of multiple satellite sensing data in eddy-resolving simulations of Asian Marginal Sea and Kurosio. Attention will be given to how Topex/Poseidon SSH, TRMM SST; and GRACE ocean bottom pressure can be correctly represented in a non- Boussinesq model.

numerical ocean model generalized coordinate syste↗