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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Alignment of magnetic particles in anisotropic Nd–Fe–B bonded magnets

High-performance anisotropic bonded magnets need a high volumetric filling fraction of magnetic powder in a polymer binder and good alignment while possessing adequate mechanical properties. The correlation between the degree of alignment (DOA) and the volumetric filling fraction of magnetic powder, the type of binder and the processing parameters have been studied using Nd–Fe–B anisotropic bonded magnets. Here, thermomagnetic analysis and differential scanning calorimetry results show that the magnetic powder can be well aligned in a bonded magnet under an external magnetic field at a temperature higher than the binders' melting point by 20–60 K, depending on the type of magnetic powder and binder. The DOA results mainly from the interplay between the Zeeman energy (E app ) and the inter-particle static energy (E stat ) in bonded magnets at the chosen alignment temperature and the magnetic fields. A good alignment can be achieved by an alignment magnetic field which is about twice its coercivity in an anisotropic Nd–Fe–B bonded magnet, which is confirmed by our experimental and modeling results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

MnSn 2 and MnSn 2 –TiO 2 nanostructured anode materials for lithium-ion batteries

The high theoretical lithium storage capacity of Sn makes it an enticing anode material for Li-ion batteries (LIBs); however, its large volumetric expansion during Li–Sn alloying must be addressed. Combining Sn with metals that are electrochemically inactive to lithium leads to intermetallics that can alleviate volumetric expansion issues and still enable high capacity. Here, we present the cycling behavior of a nanostructured MnSn 2 intermetallic used in LIBs. Nanostructured MnSn 2 is synthesized by reducing Sn and Mn salts using a hot injection method. The resulting MnSn 2 is characterized by x-ray diffraction and transmission electron microscopy and then is investigated as an anode for LIBs. The MnSn 2 electrode delivers a stable capacity of 514 mAh g -1 after 100 cycles at a C/10 current rate with a Coulombic efficiency >99%. Unlike other Sn-intermetallic anodes, an activation overpotential peak near 0.9 V versus Li is present from the second lithiation and in subsequent cycles. We hypothesize that this effect is likely due to electrolyte reactions with segregated Mn from MnSn 2 . To prevent these undesirable Mn reactions with the electrolyte, a 5 nm TiO 2 protection layer is applied onto the MnSn 2 electrode surface via atomic layer deposition. The TiO 2 -coated MnSn 2 electrodes do not exhibit the activation overpotential peak. The protection layer also increases the capacity to 612 mAh g -1 after 100 cycles at a C/10 current rate with a Coulombic efficiency >99%. This higher capacity is achieved by suppressing the parasitic reaction of Mn with the electrolyte, as is supported by x-ray photoelectron spectroscopy analysis.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Kinetic simulations of collision-less plasmas in open magnetic geometries

Laboratory plasmas in open magnetic geometries can be found in many different applications such as (a) scrape-of-layer (SOL) and divertor regions in toroidal confinement fusion devices, (b) linear divertor simulators, (c) plasma-based thrusters and (d) magnetic mirrors etc. A common feature of these plasma systems is the need to resolve, in addition to velocity space, at least one physical dimension (e.g. along flux lines) to capture the relevant physics. In general, this requires a kinetic treatment. Fully kinetic particle-in-cell (PIC) simulations can be applied but at the expense of large computational effort. A common way to resolve this is to use a hybrid approach: kinetic ions and fluid electrons. In the present work, the development of a hybrid PIC computational tool suitable for open magnetic geometries is described which includes (a) the effect of non-uniform magnetic fields, (b) finite fully-absorbing boundaries for the particles and (c) volumetric particle sources. Analytical expressions for the momentum transport in the paraxial limit are presented with their underlying assumptions and are used to validate the results from the PIC simulations. A general method is described to construct discrete particle distribution functions in a state of mirror-equilibrium. This method is used to obtain the initial state for the PIC simulation. Collisionless simulations in a mirror geometry are performed. The results show that the effect of magnetic compression is correctly described and momentum is conserved. The self-consistent electric field is calculated and is shown to modify the ion velocity distribution function in a manner consistent with analytic theory. Based on this analysis, the ion distribution function is understood in terms of a loss-cone distribution and an isotropic Maxwell-Boltzmann distribution driven by a volumetric plasma source. Finally, the inclusion of a Monte Carlo based Fokker-Planck collision operator is discussed in the context of future work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

AR reactor neutronics multi-code validation*

The affordable, robust, compact (ARC) reactor is a tokamak fusion reactor concept currently under development by Commonwealth Fusion Systems and Massachusetts Institute of Technology. There are three important neutronics considerations for the operation of the ARC reactor: (1) breeding of enough tritium in the blanket to sustain the D–T reaction in the plasma; (2) ensuring low fluence on the superconducting toroidal field coils; and (3) assessing neutron volumetric heating in structural components. Here, this work aims to perform a validation of the neutronics analysis approach by code-to-code comparison. State-of-the-art software stacks are employed for the neutronics analysis of the ARC reactor, and a computer-aided design (CAD) model is used directly for Monte Carlo (MC) neutron transport calculations. Three software stacks, Attila-MCNP, OpenMC-DAGMC, and Shift-DAGMC, are used to perform neutronic analyses of a 90° sector CAD model of the ARC reactor. In conclusion, results show that the flux tallies calculated by the three software stacks are very close. Also, the volumetric heating and tritium breeding values have less than 0.6% relative difference between codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment

The exponential growth of wind energy and the need to exploit wind resources over areas with higher energy potential have led to the construction of neighboring wind turbines and farms with relatively small separation distances. As a result, for specific wind and atmospheric conditions, the wakes generated by an upstream wind farm may affect wind resources available for a downstream wind farm resulting in detrimental impacts on energy harvesting and structural loads for the downwind wind turbines. Distances between neighboring wind farms are typically larger than those associated with intra-wind-farm wake interactions, generating cumulative wakes whose characteristics might differ from those predicted through classical engineering wake models. These phenomena are referred to as farm-to-farm interactions. A better understanding and characterization of farm-to-farm interactions is one of the science goals tackled by the ongoing American WAKE experimeNt (AWAKEN). The site under investigation for this field campaign comprises two large wind farms in northern Oklahoma, USA, which are spaced roughly 5km apart along the prevailing South-North wind direction. To investigate possible interactions between these two wind farms, the WindFluX mobile LiDAR station has been deployed mainly to perform volumetric scans over their gap region. In this paper, preliminary results from these LiDAR volumetric scans will be discussed, specifically for a case with multiple wind turbine wakes evolving during the occurrence of a low-level jet.

17 WIND ENERGY↗
DOE OSTI2024

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

Abstract Motivation Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. Results This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementation ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Lai, Jhih-Siang (ORCID:0000000156775890)↗
DOE OSTI2020

Isotropic seismic radiation from rock damage and dilatancy

SUMMARY We present semi-analytical and numerical results on isotropic radiation expected to be generated by seismic sources with damaged dilatant materials that sustain during failure an overall shear deformation. The results are derived for inclusions with different stiffness components, including those responsible for shear-volumetric coupling in dilatant material, and different geometries, in a surrounding elastic solid. The obtained relations indicate that the isotropic seismic potency and seismic moment components are proportional to the shear potency components multiplied by the aspect ratio between the thickness and length of the source zone. For a typical rupture zone with a thin tabular geometry, the expected small aspect ratio explains the general seismological observations of small isotropic radiation. However, isotropic radiation induced by dynamic dilation for short and thick sources is more pronounced and may reach for spherical sources sustaining shear deformation 18 per cent of the shear components. Sources that include also tensile components and explosions can produce larger isotropic radiation. The results provide rigorous relations that can be used to estimate different source properties in numerical simulations, experiments and field studies.

Geochemistry & Geophysics↗
DOE OSTI2023

The effect of shear strain and shear localization on fault healing

The seismic cycle of repeated earthquake failure requires that faults regain frictional strength during the interseismic phase, when the fault is locked or undergoing quasi-static creep. Fault healing plays a central role in determining earthquake stress drop, recurrence interval, elastic radiation frequency and other source parameters. In particular, the longer a fault remains quasi-stationary, the stronger it becomes and the larger the potential stress drop can be for the next event. Here, we address the role of shear strain and strain localization on fault healing and healing rate. We performed slide-hold-slide friction experiments on quartz gouge in the double-direct shear configuration for shear strain up to 25 and hold times from 10 to 1000 s. The results show that both healing and healing rate increase nonlinearly with increasing shear strain. Frictional healing scales with volumetric strain within the laboratory fault zone. Using the volumetric strain upon reshear as a proxy for strain localization, we demonstrate that the capacity of a fault to heal is directly proportional to shear bandwidth and degree of strain localization. The more the deformation is localized, the higher are the healing and healing rate, and thus, the fault strength. Our data provide a framework for understanding variations in fault strength over the seismic cycle and the role of brecciation and strain localization on spatiotemporal variations in fault strength.

58 GEOSCIENCES↗
DOE OSTI2023

Nucleosynthesis in outflows of compact objects and detection prospects of associated kilonovae

ABSTRACT We perform a comparative analysis of nucleosynthesis yields from binary neutron star (BNS) mergers, black hole-neutron star (BHNS) mergers, and core-collapse supernovae (CCSNe) with the goal of determining which are the most dominant sources of r-process enrichment observed in stars. We find that BNS and BHNS binaries may eject similar mass distributions of robust r-process nuclei post-merger (up to third peak and actinides, A ∼ 200−240), after accounting for the volumetric event rates. Magnetorotational (MR) CCSNe likely undergo a weak r-process (up to A ∼ 140) and contribute to the production of light element primary process (LEPP) nuclei, whereas typical thermal, neutrino-driven CCSNe only synthesize up to first r-process peak nuclei (A ∼ 80−90). We also find that the upper limit to the rate of MR CCSNe is $\lesssim 1~{{\ \rm per\ cent}}$ the rate of typical thermal CCSNe; if the rate was higher, then weak r-process nuclei would be overproduced. Although the largest uncertainty is from the volumetric event rate, the prospects are encouraging for confirming these rates in the next few years with upcoming surveys. Using a simple model to estimate the resulting kilonova light curve from mergers and our set of fiducial merger parameters, we predict that ∼7 BNS and ∼2 BHNS events will be detectable per year by the Vera C. Rubin Observatory (LSST), with prior gravitational wave (GW) triggers.

Astronomy & Astrophysics↗
DOE OSTI2025

The rate of extreme coronal line emitters in the Baryon Oscillation Spectroscopic Survey LOWZ sample

Extreme coronal line emitters (ECLEs) are a rare class of galaxy that exhibit strong, high-ionization iron coronal emission lines in their spectra. In some cases, these lines are transient and may be the result of tidal disruption event (TDEs). To test this connection, we calculate the rate of variable ECLEs (vECLEs) at redshift $\sim 0.3$. We search for ECLEs in the Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ sample and discover two candidate ECLEs. Using follow-up spectra from the Dark Energy Spectroscopic Instrument and Gemini Multi-Object Spectrograph, and mid-infrared observations from the Wide-field Infrared Survey Explorer, we determine that one of these galaxies is a vECLE. Using this galaxy, we calculate the galaxy-normalized vECLE rate at redshift $\sim 0.3$ to be $R_\mathrm{G}=1.6~^{+3.8}_{-1.4}\times 10^{-6}~\mathrm{galaxy}^{-1}~\mathrm{yr}^{-1}$and the mass-normalized rate to be $R_\mathrm{M}=7~^{+16}_{-6}\times 10^{-18}~\mathrm{M_\odot ^{-1}}~\mathrm{yr}^{-1}$. This is then converted to a volumetric rate of $R_\mathrm{V}=1.8~^{+4.5}_{-1.5}\times 10^{-9}~\mathrm{Mpc}^{-3}~\mathrm{yr}^{-1}$. Formally, the LOWZ vECLE rates are $2 \!-\! 4$ times lower than the rates calculated from the Sloan Digital Sky Survey Legacy sample at redshift $\sim 0.1$. However, given the large uncertainties on both measurements, they are consistent with each other at $1\sigma$. Both the galaxy-normalized and volumetric rates are one to two orders of magnitude lower than TDE rates from the literature, consistent with vECLEs being caused by $5 \!-\! 20$ per cent of all TDEs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Detecting damage in composites using volume decomposition analysis of tomographic data

Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques.

Middleton, Ceri A.↗
DOE OSTI2026

Power Electronic Development Challenges for High Power Density Integrated Electric Drive Applications

The ever-increasing demand for compact, efficient, and high-performance traction drive systems for transportation applications has accelerated the development of integrated electric drives. In these systems, the electric machine and inverter are integrated within a single housing, offering significant advantages in electrical performance, volume, weight, cost, and overall system efficiency. Despite these benefits, such high levels of integration introduce a new set of challenges, particularly in power electronic design and component selection. This article presents an in-depth investigation of a highly integrated electric drive architecture with an internal stator-mounted inverter, highlighting key design considerations and trade-offs aimed exclusively at maximizing system power density.Based on a comprehensive review of the literature, the power density of a voltage-source-inverter-driven electric drive is primarily governed by the volumetric contributions of the power modules, heat sinks, and DC-link capacitors. Accordingly, this work focuses on the optimization of these three critical components to enhance the inverter's overall power density. The proposed design achieves a power density of 100 kW/L, demonstrating the effectiveness of the presented approach for next-generation integrated electric drive systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Studying the Peak Force of Magnetic Linear Motion Devices Using Dimensional Neutrality

The merits of different sizing equations for linear motion magnetic force devices are assessed. A peak force scaling analysis for a linear magnetic coupling with respect to the volumetric force density, mass force density, magnetic shear stress and energy density is compared. By utilizing a 3-D analytic based model of a Halbach cylinder magnetic coupling it is shown that the maximizing of volumetric and mass force density is not an appropriate sizing metric for a linear magnetic actuator. Only energy density and magnetic shear stress are dimensionally neutral sizing metrics for linear actuators.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network

Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. Here, the experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://github.com/stevenhan1991/CoordNet.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2020

Pairing Directional Solar Inputs From Ray Tracing to Solar Receiver/Reactor Heat Transfer Models on Unstructured Meshes: Development and Case Studies

A novel method for pairing surface irradiation and volumetric absorption from Monte Carlo ray tracing to computational heat transfer models is presented. The method is well-suited to directionally and spatially complex concentrated radiative inputs (e.g., solar receivers and reactors). The method employs a generalized algorithm for directly mapping absorbed rays from a Monte Carlo ray tracing model to boundary or volumetric source terms in the computational mesh. The algorithm is compatible with unstructured, two and three-dimensional meshes with varying element shapes. Four case studies were performed on a directly irradiated, windowed solar thermochemical reactor model to validate the method. The method was shown to conserve energy and preserve spatial variation when mapping rays from a Monte Carlo ray tracing model to a computational heat transfer model in ansys fluent.

14 SOLAR ENERGY↗
DOE OSTI2021

Cycling-Induced Microstructural Changes in Alloy Anodes for Lithium-Ion Batteries

Abstract High-capacity electrochemical alloying materials, such as tin and tin-based alloys, present an opportunity for the advancement of lithium-ion batteries. However, the destructive effects of volumetric expansion must be mitigated in order to sustain this high capacity during extended cycling. One way to mitigate these effects is by alloying Sn with more malleable metals to accommodate the strain related to severe volumetric expansion. Ex situ X-ray microtomography data of cycled Cu6Sn5 pellets were used to quantify the microstructural changes that occur during lithiation and delithiation. The microtomography data were segmented into three distinct phases to evaluate phase size distributions, specific surface area, and tortuosity. Electrodes lithiated and then delithiated showed the most substantial reduction in overall phase sizes. This suggests that full lithiation of the Sn followed by partial delithiation of the Li4.4Sn to Li2CuSn can cause substantial microstructural changes related to volume expansion on lithiation and structural collapse upon delithiation. When considering other microstructural characteristics, this subset of the electrodes analyzed showed the highest tortuosity values. These results show that in addition to the mechanical degradation of the electrodes, excessive volume expansion can also influence transport networks in the active material and supporting phases of the electrode. While based on studies of the active–inactive alloy Cu6Sn5 for lithium-ion battery applications, the insights obtained are expected to be applicable to other alloy electrodes and battery chemistries.

Electrochemistry↗
DOE OSTI2023

Neutron Characterization of 3D Printed Gas Turbine Alloy With Internal Cooling Structure

The objective of this research work is to investigate the residual strains induced by the presence of a exit hole and internal hollow geometry present in a 3D printed IN718 similar to that seen in a gas turbine blade. The specimens were manufactured with an internal hollow structure that ended with an exit hole, and fabricated by direct metal laser sintering additive manufacturing technique. Using this technique, metal powder was melted by a laser source and rapidly cooled to form the part layer by layer. Rapid heating and cooling generate large thermal gradients and residual stresses in the 3D printed material. To be aligned with industry standards, specimens were heat-treated to relieve most of the residual stresses as well as to investigate whether IN718 still preserved a nonhomogeneous stress state after the thermal treatment. Roughness and microstructure analysis was performed to understand the heat-treated sample properties. To investigate the volumetric residual strain, the neutron diffraction technique was used with a definite neutron wavelength. Bragg’s law was used to determine lattice spacing. Interplanar lattice spacing and stress-free state lattice spacings were measured to calculate the strains. Residual strains were measured in the x and y direction to understand the impact of the presence of a exit hole and internal hollow geometry. The results of volumetric residual strain in the x and y directions showed no significant variations in the distribution of the strain due to the presence of a hollow structure as well as the exit hole. Overall, no significant stress concentration was observed in the heat treated sample. Sample geometry, instrumentation for neutron diffraction, and material properties are discussed in detail in this paper.

Raju, Nandhini↗