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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hydrostratigraphic Region 1 Model

Farnsworth Unit (FWU) CO2EOR Eclipse compositional model: Hydrostratigraphic Region 1 model uses the HS1 relative permeability curves assigned heterogeneously by hydrostratigraphic unit. The model with capillary pressure applies the HS1 curves heterogeneously by hydrostratigraphic unit.

Capillary Pressure↗
DOE OSTI2018

Infrared Microscopy as a Probe of Composition within a Model Biofuel Cell Electrode Prepared from Trametes versicolor Laccase

Abstract Infrared microscopy was applied in the study of laccase biofuel cell electrodes that demonstrated high current and power densities traceable to the use of a low equivalent weight (EW), short side chain (Aquivion) ionomer in the catalyst layer. The electrodes were prepared from a biocatalyst ink composed of orientation‐directing anthracene‐modified multi‐walled carbon nanotubes (An‐MWCNTs) in a dispersion of ionomer exchanged by hydrophobic cations. The ink was applied to an electronically conductive carbon felt support that was hot‐pressed to a Nafion‐NRE 212 (51 μm thickness) membrane, forming the framework for an air‐breathing biofuel cell cathode. Infrared microscopy sampling was performed with the use of a micro‐attenuated total reflection probe that enabled spectra to be recorded from ∼32 μm diameter circular spatial regions on the materials investigated. Sensitivity to deformation of the soft polymer membrane‐based materials was assessed through trial experiments on pure membrane samples before approaching the compositionally more complex biofuel cell electrodes. In studies of laccase cathodes, infrared spectral features of enzyme, buffer anions and surrounding An‐MWCNTs were identified. Heterogeneity in the spatial distribution of components and unexpected bands traceable to insoluble copper salts were detected. The results suggest directions for improving electrode activity and bring to light needs for advancing quantitative interpretations.

Liang, Ying↗
DOE OSTI2015

Glass Property Models, Constraints, and Formulation Approaches for Vitrification of High-Level Nuclear Wastes at the US Hanford Site

The legacy nuclear wastes stored in underground tanks at the US Department of Energy’s Hanford site is planned to be separated into high-level waste and low-activity waste fractions and vitrified separately. Formulating optimized glass compositions that maximize the waste loading in glass is critical for successful and economical treatment and immobilization of nuclear wastes. Glass property-composition models have been developed and applied to formulate glass compositions for various objectives for the past several decades. The property models with associated uncertainties and combined with composition and property constraints have been used to develop preliminary glass formulation algorithms designed for vitrification process control and waste form qualification at the planned waste vitrification plant. This paper provides an overview of current status of glass property-composition models, constraints applicable to Hanford waste vitrification, and glass formulation approaches that have been developed for vitrification of hazardous and highly radioactive wastes stored at the Hanford site.

composition constraint↗
DOE OSTI2021

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 4

This report summarizes and analyzes the data collected on a fourth test matrix of 25 low-activity waste glass compositions intended to expand the composition-property database and to validate the Vienna et al. (2020) property-composition models. The 25 low-activity waste glass compositions were statistically designed to be within the composition region of the 2020 models. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, canister centerline cooling with crystal identification, product consistency test response, vapor hydration test response, and sulfur solubility. This report discusses the results obtained from these tests.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A theory of electrical conductivity, dielectric constant, and electromagnetic interference shielding for lightweight graphene composite foams

This work was driven by the need to understand the electromagnetic interference (EMI) shielding effectiveness (SE) of light weight, flexible, and high performance graphene composite foams, but as EMI SE of a material depends on its electrical conductivity, dielectric permittivity, and magnetic permeability, the investigation of these three properties also became a priority. In this paper, we first present a continuum theory to determine these three electromagnetic properties, and then use the obtained properties to evaluate the EMI SE of the foam. A two-scale composite model is conceived to evaluate these three properties, with the large one being the skeleton-void composite and the small one being the graphene-polymer composite that serves as the skeleton of the foam. To evaluate the properties of the skeleton, the effective-medium approach is taken as the starting point. Subsequently, the effect of an imperfect interface and the contributions of electron tunneling to the interfacial conductivity and Maxwell-Wagner-Sillars polarization mechanism to the dielectric constant are also implemented. The derived skeleton properties are then utilized on the large scale to determine the three properties of the composite foam at a given porosity. Then a uniform plane electromagnetic wave is considered to evaluate the EMI SE of the foam. It is demonstrated that the electrical conductivity, dielectric constant, and EMI SE of the foam calculated from the developed theory are in general agreement with the reported experimental data of graphene/PDMS composite foams. The theory is further proven to be valid for the EMI SE of solid graphene/epoxy and solid carbon nanotube/epoxy nanocomposites. It is also shown that, among the three electromagnetic properties, electrical conductivity has the strongest influence on the EMI shielding effectiveness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa (for corresponding manuscript: DOI: 10.1002/bbb.2148) PDF Files: Images of 1H NMR spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum). Suppression of the water peak was achieved using a NOESY-1D with presaturation, a recycle delay of 5 s, and a total of 64 scans. Spectra were acquired at 298 K and processed with automatic phase correction, baseline correction, and chemical shift referencing to TSP-d4. Images show all 1H data from 10 to 1ppm with inset spectra of region of interest (4.0 to 3.1 ppm). Text Files: Spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum) were converted into text files for plotting. Files contain 8192 points of raw spectral data from 12.23 to -2.78 ppm. The text file contains 4 columns of data and includes: Point number, Intensity, Hz, and ppm. Xcel Spreadsheet: HPLC measured monomeric sugar concentrations and bucketed 1H NMR data used to build monomeric sugar composition prediction models. Sugar composition in biomass determined from HPLC analyses are given in mg sugar/mg of biomass. Spectral bucketing was performed using Bruker’s AMIX software. Spectra were divided into 0.005 ppm buckets in the region of 3.10– 4.15 ppm for a total of 210 buckets. Headers for the bucketed data are the chemical shift in ppm of the center of the bucket. Bucketed data was used to build partial least squares models for subsequent predictions in The Unscrambler v. 10.5(CAMO A/S, Trondheim, Norway). The formation of methanol during hydrolysis interferes with the quantitative NMR analysis of sugars, so the methanol peak centered at 3.37 ppm and spanning four buckets (3.2925 – 3.2775 ppm) was set to zero for all spectra.

09 BIOMASS FUELS↗
DOE OSTI2023

Hydrostratigraphic Region 4 Model - with HSUPc

Farnsworth Unit (FWU) CO2EOR Eclipse compositional model: Hydrostratigraphic Region 4 model uses the Morrow2 (UNOCAL) relative permeability curve with capillary pressure assigned heterogeneously by hydrostratigraphic unit

Capillary Pressure↗
DOE OSTI2012

Two-current model of the composition dependence of resistivity in amorphous (Fe100-xCox)(89-y)Zr7B4Cuy alloys using a rigid-band assumption

Composition dependence of resistivity is studied in amorphous (Fe100-xCox)(89-y)Zr7B4Cuy (0 <= x <= 50, y = 0, 1) alloys. The two-current model proposed by Mott for crystalline materials is extended to a disordered amorphous system where s-d scattering is dominant in electron conduction. A rigid-band assumption is made due to the small atomic number difference between Fe and Co. Band structures with a constant density of states (DOS), parabolic distributed DOS, and Gaussian distributed DOS were investigated to fit experimental data. The Gaussian distributed DOS was found to simulate the resistivity maximum and magnetic moment maximum in the Fe-rich region. The basic concepts presented here can potentially provide insight into the optimization of FeCo-based HITPERM alloys for applications at increased frequencies. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4765673]

Shen, S↗
DOE OSTI2023

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗