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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Theory of nuclear fission

Atomic nuclei are quantum many-body systems of protons and neutrons held together by strong nuclear forces. Under the proper conditions, nuclei can break into two (sometimes three) fragments which will subsequently decay by emitting particles. This phenomenon is called nuclear fission. Since different fission events may produce different fragmentations, the end-products of all fissions that occurred in a small chemical sample of matter comprise hundreds of different isotopes, including α particles, together with a large number of emitted neutrons, photons, electrons and antineutrinos. The extraordinary complexity of this process, which happens at length scales of the order of a femtometer, mostly takes less than a femtosecond but is not entirely over until all the lingering β decays have completed – which can take years – is a fascinating window into the physics of atomic nuclei. While fission may be more naturally known in the context of its technological applications, it also plays a crucial role in the synthesis of heavy elements in astrophysical environments. In both cases, simulations are needed for the many systems or energies inaccessible to experiments in the laboratory. In this context, the level of accuracy and precision required poses formidable challenges to nuclear theory. Overall, the goal of this article is to provide a comprehensive overview of the theoretical methods employed in the description of nuclear fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1974

Inventory and monitoring of natural vegetation and related resources in an arid environment: A comprehensive evaluation of ERTS-1 imagery

The author has identified the following significant results. A vegetation classification, with 31 types and compatible with remote sensing applications, was developed for the test site. Terrain features can be used to discriminate vegetation types. Elevation and macrorelief interpretations were successful on ERTS photos, although for macrorelief, high sun angle stereoscopic interpretations were better than low sun angle monoscopic interpretations. Using spectral reflectivity, several vegetation types were characterized in terms of patterns of signature change. ERTS MSS digital data were used to discriminate vegetation classes at the association level and at the alliance level when image contrasts were high or low, respectively. An imagery comparison technique was developed to test image complexity and image groupability. In two stage sampling of vegetation types, ERTS plus high altitude photos were highly satisfactory for estimating kind and extent of types present, and for providing a mapping base.

Schrumpf, B. J.↗
NASA NTRS2017

New Developments in NASA's Rodent Research Hardware for Conducting Long Duration Biomedical and Basic Research in Space

Animal models, particularly rodents, are the foundation of pre-clinical research to understand human diseases and evaluate new therapeutics, and play a key role in advancing biomedical discoveries both on Earth and in space. The National Research Councils Decadal survey emphasized the importance of expanding NASA's life sciences research to perform long duration, rodent experiments on the International Space Station (ISS) to study effects of the space environment on the musculoskeletal and neurological systems of mice as model organisms of human health and disease, particularly in areas of muscle atrophy, bone loss, and fracture healing. To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA Ames Research Center (ARC) to enhance science return for both commercial (CASIS) and government-sponsored rodent research. The Rodent Research Project at NASA ARC has pioneered a new research capability on the International Space Station and has progressed toward translating research to the ISS utilizing commercial rockets, collaborating with academia and science industry, while training crewmembers to assist in performing research on orbit. The Rodent Research Habitat provides a living environment for animals on ISS according to standard animal welfare requirements, and daily health checks can be performed using the habitats camera system. Results from these studies contribute to the science community via both the primary investigation and banked samples that are shared in publicly available data repository such as GeneLab. Following each flight, through the Biospecimen Sharing Program (BSP), numerous tissues and thousands of samples will be harvested, and distributed from the Space Life and Physical Sciences (SLPS) to Principal Investigators (PIs) through the Ames Life Science Data Archive (ALSDA). Every completed mission sets a foundation to build and design greater complexity into future research and answer questions about common human diseases. Together, the hardware improvements (enrichment, telemetry sensors, cameras), new capabilities (live animal return), and experience that the Rodent Research team has gained working with principal investigator teams and ISS crew to conduct complex experiments on orbit are expanding capabilities for long duration rodent research on the ISS to achieve both basic science and biomedical research objectives.

Shirazi, Yasaman↗
DOE OSTI2023

Microdiverse bacterial clades prevail across Antarctic wetlands

Antarctica's extreme environmental conditions impose selection pressures on microbial communities. Indeed, a previous study revealed that bacterial assemblages at the Cierva Point Wetland Complex (CPWC) are shaped by strong homogeneous selection. Yet which bacterial phylogenetic clades are shaped by selection processes and their ecological strategies to thrive in such extreme conditions remain unknown. Here, we applied the phyloscore and feature-level βNTI indexes coupled with phylofactorization to successfully detect bacterial monophyletic clades subjected to homogeneous (HoS) and heterogenous (HeS) selection. Remarkably, only the HoS clades showed high relative abundance across all samples and signs of putative microdiversity. The majority of the amplicon sequence variants (ASVs) within each HoS clade clustered into a unique 97% sequence similarity operational taxonomic unit (OTU) and inhabited a specific environment (lotic, lentic or terrestrial). Our findings suggest the existence of microdiversification leading to sub-taxa niche differentiation, with putative distinct ecotypes (consisting of groups of ASVs) adapted to a specific environment. We hypothesize that HoS clades thriving in the CPWC have phylogenetically conserved traits that accelerate their rate of evolution, enabling them to adapt to strong spatio-temporally variable selection pressures. Variable selection appears to operate within clades to cause very rapid microdiversification without losing key traits that lead to high abundance. Variable and homogeneous selection, therefore, operate simultaneously but on different aspects of organismal ecology. The result is an overall signal of homogeneous selection due to rapid within-clade microdiversification caused by variable selection. It is unknown whether other systems experience this dynamic, and we encourage future work evaluating the transferability of our results.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

pH profile of the adsorption of nucleotides onto montmorillonite. I - Selected homoionic clays

The effect of pH and adsorbed ions on the adsorption of purine and pyrimidine nucleotides on montmorillonite clay was studied experimentally. The specific nucleotides examined were: 5 prime-AMP; 3-prime AMP; and 5 prime-CMP. The pH of the clay samples was adjusted to various levels in the 2-12 pH range using microliter volumes of concentrated acid (1N HCl) and base (1NHNaOH). It was found that preferential adsorption among nulceotides was dependent on the pH level and on the characteristics of the substituted metal cation and anion exchange mechanisms. Below pH 4, adsorption was attributed to cation and anion exchange mechanisms. Above pH 4, however, adsorption was attributed to the complexation mechanisms occurring between the metal cations in the clay exchange site and in the biomolecule. The possible role of homoionic clays in the concentration mechanisms of biomonomers in the prebiotic environment is discussed.

Lawless, J. G.↗
DOE OSTI2022

Capturing Carbonation: Understanding Kinetic Complexities through a New Era of Electron Microscopy

Cryogenic plasma focused ion beam (PFIB) electron microscopy analysis is applied to visualizing ex situ (surface industrial) and in situ (subsurface geologic) carbonation products, to advance understanding of carbonation kinetics. Ex situ carbonation is investigated using NIST fly ash standard #2689 exposed to aqueous sodium bicarbonate solutions for brief periods of time. In situ carbonation pathways are investigated using volcanic flood basalt samples from Schaef et al. (2010) exposed to aqueous CO 2 solutions by them. The fly ash reaction products at room temperature show small amounts of incipient carbonation, with calcite apparently forming via surface nucleation. Reaction products at 75° C show beginning stages of an iron carbonate phase, e.g., siderite or ankerite, common phases in subsurface carbon sequestration environments. This may suggest an alternative to calcite in carbonation low calcium-bearing fly ashes. Flood basalt carbonation reactions show distinct zonation with high calcium and calcium-magnesium bearing zones alternating with high iron-bearing zones. The calcium-magnesium zones are notable with occurrence of localized pore space. Oscillatory zoning in carbonate minerals is distinctly associated with far-from-equilibrium conditions where local chemical environments fluctuate via a coupling of reaction with transport. The high porosity zones may reflect a precursor phase (e.g., aragonite) with higher molar volume that then “ripens” to the high-Mg calcite phase-plus-porosity. These observations reveal that carbonation can proceed with evolving local chemical environments, formation and disappearance of metastable phases, and evolving reactive surface areas. Together this work shows that future application of cryo-PFIB in carbonation studies would provide advanced understanding of kinetic mechanisms for optimizing industrial-scale and commercial-scale applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Remote quantification of Cm(III) and HNO 3 by fluorescence spectroscopy and chemometrics

A unique approach to remotely quantify Cm(III) (0–100 µg mL −1 ) in HNO 3 (1–12 M) using steady-state laser fluorescence spectroscopy and multivariate regression models was developed. Photoluminescence is amenable to remote measurements using fiber-optic cables and is sensitive to numerous lanthanide and actinide species. In-line measurements can provide feedback to support complex processing in harsh environments (e.g., hot cells) to help guide and optimize radiochemical separations. In this work, Cm(III) spectra were acquired remotely in a glove box as a function of HNO 3 concentration to better understand spectral characteristics and evaluate the utility of multivariate regression models in this system. Furthermore, the Cm(III) fluorescence peak shape, width, position, and intensity changed significantly as a function of HNO 3 concentration, likely because of the displacement of emission quenching inner-sphere water molecules and complexation with nitrate ions. Despite significant covariance and nonlinearity in the data, a D-optimal design strategy successfully minimized training set sample size and was used to build effective partial least squares regression models for Cm(III) and HNO 3 concentrations without a priori knowledge of solution conditions. Chemometrics for modeling complex fluorescence spectra are promising and may find widespread applicability for online analysis in numerous chemical systems found in the nuclear field.

Actinide↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2023

Analysis of Pitting Corrosion on Wrought and Additively Manufactured 316L Stainless Steel in Atmospheric Environments

Additive manufacturing of metal components enables rapid fabrication of complex geometries. However, metal additive manufacturing also introduces new morphological and microstructural characteristics which might be detrimental to component performance. Here we report the pitting corrosion properties of wrought and additively manufactured 316L stainless steel after atmospheric exposure to coastal environments and laboratory-created environments. Qualitative visualization in combination with quantitative analysis of resulting pits provided an in-depth understanding of pitting differences between wrought and additively manufactured 316L stainless steel and between coastal and laboratory-based exposure. Optical and scanning electron microscopy were utilized for visualization, while white light interferometry measured pits across approximately 5mm x 5mm areas on each sample. Post-processing of the interferometry data enables quantification of pitting attack for each sample in terms of both pit depth and pit volume. The pitting analysis introduced herein offers a new technique to compare pitting attack between different manufacturing processes and materials.

36 MATERIALS SCIENCE↗
NASA NTRS1991

A general multiblock Euler code for propulsion integration. Volume 3: User guide for the Euler code

This manual explains the procedures for using the general multiblock Euler (GMBE) code developed under NASA contract NAS1-18703. The code was developed for the aerodynamic analysis of geometrically complex configurations in either free air or wind tunnel environments (vol. 1). The complete flow field is divided into a number of topologically simple blocks within each of which surface fitted grids and efficient flow solution algorithms can easily be constructed. The multiblock field grid is generated with the BCON procedure described in volume 2. The GMBE utilizes a finite volume formulation with an explicit time stepping scheme to solve the Euler equations. A multiblock version of the multigrid method was developed to accelerate the convergence of the calculations. This user guide provides information on the GMBE code, including input data preparations with sample input files and a sample Unix script for program execution in the UNICOS environment.

Chen, H. C.↗
DOE OSTI2024

Elucidating the role of Cr migration in Ni-Cr exposed to molten FLiNaK via multiscale characterization

Structural materials used in nuclear reactor environments are subjected to coupled extremes such as high temperature, irradiation, and corrosion which act in concert to degrade their functional performance. Connecting alloy microstructure such as grain boundaries, and accumulating point defects with corrosion attack and pore morphology is critical to understanding underlying mechanisms. We uncover the compositional variations and morphology at multiple length scales in corrosion-damaged Ni-Cr alloy after exposure to oxidants in molten fluoride salts. A complex network of dense corrosion pores is detected by surface-level SEM observations. The corrosion pores take on a 1-dimensional morphology and are enriched with Ni and depleted of Cr 1–5 µm from the pore surface. STEM-EDS and 4D-STEM strain maps acquired simultaneously highlight the local variations in composition and structure of a ≤ 200 nm Cr-rich layer identified from a cross-section taken at the bottom of an isolated corrosion pore between the Ni-Cr alloy matrix and the embedded salt. However, the absence of an observed interface between the Ni-Cr alloy matrix and the FCC-structured Cr-rich layer suggests that Cr plating from the salt did not transpire. These findings support a proposed Cr lattice diffusion mechanism rather than Cr-precipitation from the salt to accommodate temperature transient conditions during sample cooling. Through the development of a 1D phase field model, these results are rationalized by formation energies for the Ni- and Cr-oxidation into the molten salt. This study reveals the locally altered microstructure caused by high temperature corrosion in non-steady-state molten salt nuclear reactor environments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantitative 14 N NMR with Monte Carlo Uncertainty Analysis of Nitrate/Nitrite in Alkaline Nuclear Waste

While monitoring of nitrate and nitrite concentrations is important for managing corrosion in nuclear waste systems, existing analytical methods are hindered by turbidity, spectral interference, and delays from sample handling. Here, we demonstrate quantitative 14 N nuclear magnetic resonance (qNMR) spectroscopy as a direct, matrix-tolerant approach for nitrate and nitrite detection at natural abundance. Monte Carlo resampling was integrated into the workflow to quantify random error, establish precision–time tradeoffs, and separate noise-limited uncertainty from systematic bias arising from shimming, transmitter offset, or excitation pulse conditions. Quantification of nitrate and nitrite were validated in controlled alkaline matrix challenges and in 18-component Hanford-type simulants. These results establish 14 N qNMR as a practical, uncertainty-bounded tool for monitoring redox-active nitrogen species in chemically complex environments and provide a generalizable framework for quantitative analysis of quadrupolar nuclei.

Graham, Trent R. [Pacific Northwest National Labor↗
DOE OSTI2023

Impact of peripheral alkyl chain length on mesocrystal assemblies of G2 dendrons

Unique sphere-packing mesophases such as Frank–Kasper (FK) phases have emerged from the viable design of intermolecular interactions in supramolecular assemblies. In this work, a series of C n -G2-CONH 2 dendrons possessing an identical core wedge are investigated to elucidate the impact of peripheral alkyl chain lengths (C n ) on the formation of the close-packed structures. The C 18 and C 14 dendrons, of which the contour lengths of the periphery L p are longer than the wedge length L w , assemble into a uniform sphere-packing phase such as body-centred cubic (BCC), whereas the C 8 dendron with short (L p < L w ) corona environment forms the FK A15 phase. Particularly in the intermediate C 12 and C 10 dendrons (L p ≈ L w ), cooling the samples from an isotropic state leads to cooling-rate-dependent phase behaviours. The C 12 dendron produces two structures of hexagonal columnar and sphere-packing phases (BCC and A15), while the C 10 dendron generates the A15 and σ phases by the fast- and slow-cooling processes, respectively. Our results show the impact of peripheral alkyl chain lengths on the formation of mesocrystal phases, where the energy landscape of the dendrons at L p /L w ≈ 1 must be more complex and delicate than those with either longer or shorter peripheral alkyl chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dynamic PRA-Based Estimation of PWR Coping Time Using a Surrogate Model for Accident Tolerant Fuel

In this study, we propose an interpolation-based response surface surrogate methodology to manage a large number of scenarios in dynamic probabilistic risk assessment. It adopts the shape Dynamic Time Warping algorithm to cluster the interpolation neighborhood from time series sample data. The interpolation method was adapted from Taylor Kriging to allow a reduced-order model of the Taylor series. In order to demonstrate its applicability to complex issues in risk assessment for nuclear engineering, an example risk response surface to estimate emergency core cooling system (ECCS) criteria for triplex silicon carbide (SiC) accident-tolerant fuel was constructed. The response surface was exploited to estimate the cumulative failure probability of the fuel cladding structure due to the uncertainties in operator actions and safety systems. The functional failures were assessed based on a combination of individual layer failures computed by coupling Risk Analysis Virtual Environment software with a pressurized water reactor 1000-MW(electric) RELAP5 model and the in-house fuel performance assessment module. Results showed that SiC cladding failure probability spiked less than 1 min after a large-break loss-of- coolant accident whenever the current ECCS criteria for Zircaloy-4 (Zr-4) cladding was used. However, it still provides an increased safety margin of three orders of magnitude compared to Zr-4. This positive margin could be utilized to relax active ECCS requirements by allowing deviations of up to 450 s in its actuation time. The proposed surrogate methodology generated a response surface of SiC cladding failure probability reasonably well, with a significant savings of computation time. This methodology is expected to be useful in the analysis of system response with complex uncertainty sources.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1995

Reduction and coding of synthetic aperture radar data with Fourier transforms

Recently, aboard the Space Radar Laboratory (SRL), the two roles of Fourier Transforms for ocean image synthesis and surface wave analysis have been implemented with a dedicated radar processor to significantly reduce Synthetic Aperture Radar (SAR) ocean data before transmission to the ground. The object was to archive the SAR image spectrum, rather than the SAR image itself, to reduce data volume and capture the essential descriptors of the surface wave field. SAR signal data are usually sampled and coded in the time domain for transmission to the ground where Fourier Transforms are applied both to individual radar pulses and to long sequences of radar pulses to form two-dimensional images. High resolution images of the ocean often contain no striking features and subtle image modulations by wind generated surface waves are only apparent when large ocean regions are studied, with Fourier transforms, to reveal periodic patterns created by wind stress over the surface wave field. Major ocean currents and atmospheric instability in coastal environments are apparent as large scale modulations of SAR imagery. This paper explores the possibility of computing complex Fourier spectrum codes representing SAR images, transmitting the coded spectra to Earth for data archives and creating scenes of surface wave signatures and air-sea interactions via inverse Fourier transformations with ground station processors.

Tilley, David G.↗
DOE OSTI2024

Kilonova light-curve interpolation with neural networks

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2019

Experimental Investigation of Cement Hydration in Gravity-Free Environment

In this work, cement hydration in terrestrial and microgravity environment was compared. This was for the first time, when the International Space Station was utilized to fully investigate the complex process of cement solidification. Microstructural development of hydrating cement occurs in stages during the hydration reaction and hardening process, which results in elaborate combinations of amorphous and crystalline phases. The morphology, volume fraction, and distribution of these phases ultimately determine the hardened cement’s material properties. Minimizing gravity-driven phenomena, such as thermosolutal convective flow and sedimentation ensures crystal growth strictly by diffusion and a microstructure forms differently from that observed in typical laboratory conditions on Earth. A test matrix was developed that includes samples with various w/c ratio and various compositions; incorporating alite, pure water and cement system, cement with chemical admixtures, as well as commercial products. This paper reports on main changes initially observed in the microstructural development of hydrated pure compound of tricalcium silicate (C3S).

Neves, Juliana↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗