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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Incorporation of drift loss cone effects on the trapping of an artificial radiation belt into LANL's modeling capability

Los Alamos National Laboratory has developed an end-to-end model that quantifies the total ionizing dose that a shielded component on a space asset will accrue due to interactions with trapped beta-decay electrons produced after a high-altitude nuclear explosion. The model consists of three major components: initial trapping, spatio-temporal evolution of the trapped population, and interaction of the evolving trapped population with a space asset to produce total ionizing dose. The initial trapping module calculates the fraction of betas produced by radioactive decay of nuclear fission products that will be trapped in the magnetic field surrounding Earth for at least one drift around Earth. Previously, the initial trapping calculation assumed a centered dipole magnetic field model whereas we know that Earth’s magnetic field is offset from its geographic center and has higher-order non-dipole terms. Both the offset and the higher-order terms are captured in the International Geomagnetic Reference Field (IGRF) internal field model. In a centered dipole, the electrons that are trapped on a magnetic field line at one longitude will be trapped at all other longitudes as they drift around Earth due to azimuthal symmetry of the field, but the offset and higher-order terms of the IGRF field affects the drift of the trapped electrons and causes some that are trapped at one longitude to be lost at other longitudes. These electrons are called "quasi-trapped" and are said to be in the drift loss cone since they will be lost during their first drift around Earth. In this paper we report on the effect of incorporating the drift of electrons around Earth using the IGRF field on the trapping fraction as compared to using a centered dipole. Here, we find that the new, more accurate, methodology produces higher trapping fractions for bursts above the South Atlantic Anomaly, a region of low magnetic field strength, and lower trapping fractions for bursts in regions with higher magnetic field strength than expected from a centered dipole.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Green Computing Catalyzer Kickoff: Green Artificial Intelligence (AI) Directions

For the 2022 JISEA annual meeting, JISEA introduces in this presentation the new Green Computing Catalyzer. The new catalyzer looks to establish green computing as a salient research domain at NREL, and crucially, to begin to address the looming computing energy crisis. The presentation provides some basic numbers, establishing energy use and source or carbon emissions for computing. The presentation also includes a brief history of green computing at NREL, empirical deep learning efficiency study, and research directions.

AI efficiency↗
DOE OSTI2023

Reconfigurable Magnetic Liquid Building Blocks for Constructing Artificial Spinal Column Tissues

All-liquid molding can be used to transform a liquid into free-form solid constructs, while maintaining internal fluidity. Traditional biological scaffolds, such as cured pre-gels, are normally processed in solid state, sacrificing flowability and permeability. However, it is essential to maintain the fluidity of the scaffold to truly mimic the complexity and heterogeneity of natural human tissues. Here, this work molds an aqueous biomaterial ink into liquid building blocks with rigid shapes while preserving internal fluidity. The molded ink blocks for bone-like vertebrae and cartilaginous-intervertebral-disc shapes, are magnetically manipulated to assemble into hierarchical structures as a scaffold for subsequent spinal column tissue growth. It is also possible to join separate ink blocks by interfacial coalescence, different from bridging solid blocks by interfacial fixation. Generally, aqueous biomaterial inks are molded into shapes with high fidelity by the interfacial jamming of alginate surfactants. The molded liquid blocks can be reconfigured using induced magnetic dipoles, that dictated the magnetic assembly behavior of liquid blocks. The implanted spinal column tissue exhibits a biocompatibility based on in vitro seeding and in vivo cultivating results, showing potential physiological function such as bending of the spinal column.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence

Here, a new Reverse Monte Carlo (RMC) package ‘fullrmc’ for atomic or rigid body and molecular, amorphous or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython ,C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modelling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. Also fullrmc provides a unique way with almost no additional computational cost to recur a group’s selection, allowing the system to go out of local minimas by refining a group’s position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optical Durability of Contemporary PV Encapsulants Through Artificial UV Weathering

Modern c-Si photovoltaic (PV) cells provide high performance but can be vulnerable to ultraviolet light induced degradation (UV-ID). Encapsulants, if chosen correctly, can mitigate UV-ID of the PV cell. Here, we explore performance and durability of 14 commercial encapsulant materials before, during, and after irradiation with UV-containing light. Materials include contemporary, polymer-based encapsulants with a base polymer of poly (ethylene co-vinyl acetate) (EVA), polyethylene-..alpha..-olefin (POE), or their composite (EPE). Polymers contain additives that induce UV-blocking, UV-transmitting, or UV-downshifting properties. We use test coupons to study degradation in a chamber held at 65 degrees C under a xenon light source for up to 4000 h of exposure, corresponding to a cumulative dose of 11.5 MJ/m2 at 340 nm. We examine optical properties including spectral transmittance, yellowness index and spectral fluorescence, considering changes to both the encapsulant and glass as a function of weathering time. Degradation modes identified include discoloration, changes to UV cutoff wavelength, changes to solar-weighted transmittance, and most notably a change to the UV-managing properties of some additives. We propose the use of solar-weighted transmittance in the 300- to 400-nm range to better track performance changes in the UV region associated with the UV-related additive. This is especially relevant for the emerging class of UV-downshifting additives, as metrics like UV-cutoff can understate the degree of degradation or change in these materials. While most encapsulants show very little change after weathering, some show significant changes that directly impact how much UV light would reach an underlying cell.

14 SOLAR ENERGY↗
DOE OSTI2025

Ensemble‐Based, Large‐Eddy Reconstruction of Wind Turbine Inflow in a Near‐Stationary Atmospheric Boundary Layer Through Generative Artificial Intelligence

ABSTRACT To validate the second‐by‐second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time‐resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral‐based models of the atmosphere. Here, we develop a technique for time‐resolved inflow reconstruction that is rooted in a large‐eddy simulation model of the atmosphere. Our “large‐eddy reconstruction” technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10‐min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large‐eddy simulation. We verify the second‐by‐second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values () between ground‐truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real‐world case studies by driving large‐eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second‐by‐second behavior ().

17 WIND ENERGY↗
DOE OSTI2023

Impact of artificial topological changes on flow and transport through fractured media due to mesh resolution

Abstract We performed a set of numerical simulations to characterize the interplay of fracture network topology, upscaling, and mesh refinement on flow and transport properties in fractured porous media. We generated a set of generic three-dimensional discrete fracture networks at various densities, where the radii of the fractures were sampled from a truncated power-law distribution, and whose parameters were loosely based on field site characterizations. We also considered five network densities, which were defined using a dimensionless version of density based on percolation theory. Once the networks were generated, we upscaled them into a single continuum model using the upscaled discrete fracture matrix model presented by Sweeney et al. (2019). We considered steady, isothermal pressure-driven flow through each domain and then simulated conservative, decaying, and adsorbing tracers using a pulse injection into the domain. For each simulation, we calculated the effective permeability and solute breakthrough curves as quantities of interest to compare between network realizations. We found that selecting a mesh resolution such that the global topology of the upscaled mesh matches the fracture network is essential. If the upscaled mesh has a connected pathway of fracture (higher permeability) cells but the fracture network does not, then the estimates for effective permeability and solute breakthrough will be incorrect. False connections cannot be eliminated entirely, but they can be managed by choosing appropriate mesh resolution and refinement for a given network. Adopting octree meshing to obtain sufficient levels of refinement leads to fewer computational cells (up to a 90% reduction in overall cell count) when compared to using a uniform resolution grid and can result in a more accurate continuum representation of the true fracture network.

58 GEOSCIENCES↗
DOE OSTI2018

Influence of Near-Surface Severe Plastic Deformation of Mild Steel on the Inhibition Performance of Sodium Molybdate and 1H-Benzotriazole in Artificial Sea Water

The effects of near-surface severe plastic deformation (NS-SPD) on the inhibition performance of sodium molybdate (SM) and 1H-benzotriazole (BTA) for mild steel were investigated using weight loss, polarization and electrochemical impedance spectroscopy measurements. The crystal grain size of NS-SPD-processed surface was analyzed by x-ray diffractometry and field emission scanning electron microscopy. A deformed layer with thickness of 20 ± 5 µm was produced on mild steel surface after NS-SPD process due to accumulated strains. The NS-SPD process caused more effective adsorption of corrosion inhibitors due to the fabrication of a surface with a high density of preferential adsorption sites. However, the stability of protective layer was predominantly influenced by the effect of NS-SPD process on inhibition efficiency. The fairly good persistence of protective layer formed on the surface by SM-containing solution and also positive effect of NS-SPD process on adsorption of molybdate ions caused higher inhibition performance for sodium molybdate. However, NS-SPD process encouraged deterioration of protective layer formed on steel surface in the presence of BTA inhibitor. It was ascribed to partial coverage of surface, low stability of adsorbed layer and thus more adsorption of aggressive ions on unprotected area which was uncovered during immersion time.

36 MATERIALS SCIENCE↗