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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geometric origin of the intrinsic transverse spin transport in a canted-antiferromagnet/heavy-metal heterostructure

We theoretically study the conditions under which an intrinsic spin Nernst effect–a transverse spin current induced by an applied temperature gradient–can occur in a canted-antiferromagnet insulator, such as LaFeO 3 and other materials of the same family. The spin Nernst effect may provide a microscopic mechanism for an experimentally observed anomalous thermovoltage in LaFeO 3 /Pt heterostructures, where spin is transferred across the insulator/metal interface when a temperature gradient is applied to LaFeO 3 parallel to the interface. We find that LaFeO 3 exhibits an intrinsic spin Nernst effect when inversion symmetry is broken on the axes parallel to both the applied temperature gradient and the direction of spin transport, which can result in a spin injection across the insulator/metal interface. Furthermore, our paper provides a general derivation of a symmetry-breaking-induced spin Nernst effect, which may open a path to engineering a finite spin Nernst effect in systems where it would otherwise not arise.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2022

Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. Finally, a corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.

42 ENGINEERING↗
DOE OSTI2022

Modeling of Supercritical CO2 Shell-and-Tube Heat Exchangers Under Extreme Conditions. Part I: Correlation Development

Abstract High-temperature supercritical CO2 Brayton cycles are promising possibilities for future stationary power generation and hybrid electric propulsion applications. Heat exchangers are critical components in supercritical CO2 thermal cycles and require accurate correlations and comprehensive performance modeling under extreme temperatures and pressures. In this paper (Part I), new Colburn and friction factor correlations are developed to quantify shell-side heat transfer and friction characteristics of flow within heat exchangers in the shell-and-tube configuration. Using experimental and computational fluid dynamics (CFD) data sets from existing literature, multivariate regression analysis is conducted to achieve correlations that capture the effect of multiple critical geometric parameters. These correlations offer superior accuracy and versatility as compared to previous studies and predict the thermohydraulic performance of about 90% of the existing experimental and CFD data within ±15%. Supplementary thermohydraulic performance data are acquired from CFD simulations with supercritical CO2 as working fluid to validate the developed correlations and demonstrate its capability to be applied to supercrtical CO2 heat exchangers.

Engineering↗
DOE OSTI2020

Precision snow removal analysis

A method for precision snow removal analysis provides a melt rate in inches per day for roadways in a geometric region is provided. Elevation data is used to calculate upward looking hemispherical viewsheds at each roadway coordinate to determine the total solar radiation. The snowmelt capacity and slope are determined at each coordinate and the data is displayed on a geographical map of the roadway. The processing speed of the computer is improved since the snowmelt capacity is only determined for a roadway subset of the region coordinates.

42 ENGINEERING↗
DOE OSTI2024

Oak Ridge National Laboratory Neutron Spin Echo Beamline on NB-2

Neutron Spin Echo (NSE) spectroscopy uniquely measures the Q-dependence of slow relaxation dynamics, and having such a machine at HFIR will advance polarized neutron spectroscopy and promote the study of biophysical and energy materials using neutrons. The 2018 Instrument Advisory Board (IAB) advises Neutron Sciences Directorate, ORNL to upgrade the cold neutron delivery system at HFIR, addressing geometrical challenges in neutron transfer from the bright cold source. This is being optimized now: the entrance to the guide system is moving closer to the source, and more guides are being added. However, there are still significant challenges to making the cold flux at the sample world-class. With the proposed guide system, NB-2 can deliver more than 10 6 polarized neutrons per cm 2 /sec below 8Å, and probably 10Å with further optimization. With no potential for running user experiments with neutrons longer than 15Å, the utility of such a machine for biological studies is limited. However, there is a large demand for this class of spectrometer in materials science, chemical engineering, and nanotechnology. Workshops held over the past decade and the three-source vision have highlighted the community's need for a low-angle, high-energy resolution spectrometer at HFIR. A detailed design study of this spectrometer is needed, focusing on optimizing the neutron delivery system for SANS-type studies. This neutron spectrometer would complement studies already performed on both SNSNSE and BASIS by overlapping and extend the dynamic range accessible, while acknowledging the proposed machine, EXPANSE, at the Second Target Station. Two technologies currently exist for such a spectrometer: traditional DC solenoids like those on BL-15 at SNS and IN15 at the ILL, and RF-flipper-based machines similar to RESEDA at FRMII. Either instrument would satisfy the scientific justification. However, there are strong business and scientific cases to utilize the thermal flux, the resonant expertise developed at ORNL, and the potential to utilize an entangled beam of neutrons to probe quantum matter by adopting the neutron resonant spin-echo configuration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Defect-Engineered High-Entropy Spinel Oxide@Onion-Like Carbon Catalysts for High-Areal-Energy Rechargeable Zinc–Air Batteries

Rechargeable zinc-air batteries (ReZAB) have emerged as the next-generation batteries with several advantages over the conventional lithium-ion battery. In this work, single nanocrystals of inverse-type high-entropy spinel oxides (HESOx, particle size of 10−12 nm) confined in highly curved defective onion-like carbons (HESOx/OLC AT ) as efficient electrocatalysts for oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and ReZAB, have been synthesized. The HESOx materials were thoroughly characterized using several analytical techniques, including X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy (STEM), Raman, and electron paramagnetic resonance (EPR). HESOx/OLC AT catalyst was tested for ReZAB using literature-recommended parameters that would allow for real technological application. These parameters include a current loading of 10 mA cm −2 and a discharge areal energy density of 35 mWh cm geometric −2 , which maps a Li-ion battery pack-level specific energy of 120 Wh kg pack −1 . HESOx/OLC AT electrocatalysts allowed for continuous discharging and charging at a current loading of 10 mA cm −2 with discharge areal energy densities between 37 and 74 mWh cm geometric −2 , thus outperforming the recommended threshold of 35 mWh cm geometric −2 . Considering that most studies (>90%) hardly meet the recommended threshold for technological application of ReZAB, the present work represents one of the top-performing electrocatalysts for ReZAB. The excellent electrocatalytic properties of defect-rich HESOx/OLC AT toward ORR/OER and ReZAB are governed by the strong electronic modulation arising from d-π hybridization, the availability of multiple catalytic sites for intermediates, and weakened d-band centers of the rate-determining intermediates (i.e., *O adsorption for ORR and *OOH formation for OER) compared to the pristine HESOx. This work introduces an effective approach for the design and synthesis of single nanocrystals of high-entropy electrocatalysts for the development of low-cost, robust, and technologically relevant rechargeable zinc−air batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Numerical and Analytical Modeling of Flow Partitioning in Partially Saturated Fracture Networks

Infiltration processes in fractured-porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially-saturated conditions flows in fractures, percolating fracture networks, and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components and hence the bulk flow velocity and dispersion of fracture networks. In this work, we derive an approximate analytical solution for the partitioning process and validate it using smoothed particle hydrodynamics simulations. The developed transfer function is conceptually based on simulation results and laboratory experiments carried out in previous works. It allows to efficiently simulate flow through fracture networks with simple cubic structure and arbitrary number of fractures and aperture sizes via linear response theory and convolution of a given input signal. We derive a non-dimensional bulk flow velocity ($\widetilde{v}$) and dispersion coefficient ($\widetilde{D}$) to characterize fracture networks in terms of dimensionless horizontal and vertical time scales $\tau_m$ and $\tau_0$. The dispersion coefficient is shown to strongly depend on the horizontal time scale and converges towards a constant value of $0.08$ within reasonable fluid and geometrical parameter ranges, while the non-dimensional velocity exhibits a characteristic $\widetilde{v} \sim \tau_m^{-1/2}$ scaling. Given that hydraulic information is often only available at limited places within (fractured-porous) aquifer system, such as boreholes or springs, our study intends to provide a rudimentary analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, e.g., the dispersive properties of discharge (groundwater level fluctuations).

42 ENGINEERING↗
DOE OSTI2026

Fast solvers for tokamak fluid models with PETSc

Multigrid (MG) is widely recognized as a highly effective solver for the model problem, the Laplacian, but textbook MG fails on most problems of interest. MG methods have been applied to complex, real-world applications with careful consideration of the physical model and discretization. In this work we develop the first step in applying MG methods to science and engineering relevant magnetohydrodynamics (MHD) tokamak models in the M3D-C1 (https://m3dc1.pppl.gov) fusion energy science code. The semi-implicit time integrator in M3D-C1 is composed of many linear solves. The implicit advance of the momentum equation is the most challenging and is the focus of this work. The current production solver in M3D-C1 is a block Jacobi (BJ) preconditioner within a Krylov solver, where blocks group degrees of freedom on planes of constant toroidal coordinate. BJ convergence degrades as the number of planes increases due to the spectral properties of the matrix preconditioned with BJ. The partially magnetic field-aligned, regular toroidal grid structure in M3D-C1 is amenable to semi-coarsening geometric MG in the toroidal direction. This paper develops such a solver and demonstrates competitive performance on a runaway electron model of a SPARC (https://cfs.energy/technology/sparc) disruption, and superior robustness on a stellarator model on which the BJ solver fails to converge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Hypersonic Jets of Detonation Products in the Hydrodynamic Collapse of Macroscopic Voids

Localizing the energetic output from detonation waves has been a long-standing challenge in applied detonation physics. Here, energy localization is achieved via machined millimeter scale voids in pressed samples of PBX 9501, an HMX (1,3,5,7-Tetranitro-1,3,5,7-tetrazocane)-based plastic bonded explosive. A main mechanism of energy localization in these systems, the formation of hydrodynamic jets of dense product gases, is characterized experimentally using a semicylindrical geometry in witness plate impact experiments and streak imaging of the jet propagating into the air. The distance at which the jet is optimally developed is identified and the supersonic flow structure in the vicinity of this feature is explored using hydrocode simulations. This analysis found that most of the kinetic energy of the hydrodynamic jet arises from pressure gradients induced by geometrically mediated squeeze flow lateral to the direction of detonation propagation. This work presents a new development in the control of energetic output from detonation waves and applications to detonation wave shaping are discussed.

42 ENGINEERING↗
DOE OSTI2024

Stress intensity factor models using mechanics-guided decomposition and symbolic regression

The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. Here, in this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learned with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity.

42 ENGINEERING↗
DOE OSTI2020

In Situ X-Ray Absorption Spectroscopy Disentangles the Roles of Copper and Silver in a Bimetallic Catalyst for the Oxygen Reduction Reaction

Silver-based bimetallic catalysts for the oxygen reduction reaction (ORR) are promising for a wide variety of renewable energy technologies, including alkaline fuel cells and metal-air batteries. The activity of bimetallic catalysts can sometimes surpass that of either constituent element, but the origin of the enhanced performance is still debated. At a given active site, two complementary mechanisms are proposed to explain the performance improvements: the binding energy of intermediate adsorbates can be tuned by direct electronic contributions from the alloying element or by changes in the bond lengths from lattice distortion. To distinguish between these effects and elucidate the respective roles of each element in the bimetallic, it is critical to study catalysts at the molecular scale under reaction conditions. In this work, we use in situ X-ray absorption spectroscopy (XAS) alongside density functional theory (DFT) to show that direct electronic rather than geometric effects are the primary cause of improved ORR activity in a bimetallic CuAg catalyst. Our results indicate that the local bonding as well as the electronic structure of Ag are virtually unchanged by the presence of Cu, whereas the electronic states of Cu in CuAg are significantly altered. DFT calculations support these experimental findings. We show strong evidence that the activity of the bimetallic CuAg catalyst exceeds the sum of the activities of Cu and Ag, not by incremental improvement of the active Ag sites, but by creating highly active Cu-centered catalytic sites. The insight that the main role of Ag in bimetallic catalysts may be to promote its fellow element through local electronic interactions provides a new design principle for engineering the next generation of bimetallic catalysts for the ORR and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Control Selection for the Neutralization Tank in the Aqueous Recovery System at the Savannah River Plutonium Processing Facility

The Aqueous Recovery System (ARS) at the Savannah River Plutonium Processing Facility (SRPPF) recovers and purifies plutonium (Pu) using aqueous chemistry techniques. The neutralization tanks in the ARS collect waste streams with various impurities and greatly reduced concentrations of Pu than are produced during aqueous processing. Criticality safety is primarily achieved in the ARS by using geometrically favorable process vessels. However, due to the geometry of neutralization tanks, each tank is administratively limited to no more than 450 g Pu, thus representing the transition from geometry to mass control. To ensure that this mass limit is not exceeded, an administrative control requires taking two samples of any solution to be sent to a neutralization tank. Normal operations are expected to result in less than 50 g Pu in a neutralization tank filled to its capacity of 250 L. Process upsets may cause an inadvertent transfer of up to 1000 g Pu to a neutralization tank, producing a system that is potentially not subcritical for all possible configurations of the tank. To ensure that a neutralization tank remains safety subcritical, passive engineered controls (e.g. changing tank geometry or adding fixed poisons), active engineered controls (e.g. interlocks), and administrative controls (e.g. soluble poisons and valve isolation) were considered and their viability evaluated. Ultimately, the team chose an administrative, dual-valve isolation strategy. This paper will thoroughly discuss the various control strategy options and why many of the options were not feasible for maintaining criticality safety.

Dressman, Phillip M. [Savannah River Nuclear Solut↗
DOE OSTI2025

Control Selection for the Neutralization Tank in the Aqueous Recovery System at the Savannah River Plutonium Processing Facility

The Aqueous Recovery System (ARS) at the Savannah River Plutonium Processing Facility (SRPPF) recovers and purifies plutonium (Pu) using aqueous chemistry techniques. The neutralization tanks in the ARS collect the waste streams, containing various impurities and greatly reduced concentrations of Pu, that are produced during aqueous processing. Criticality safety is primarily achieved in the ARS by using geometrically favorable process vessels. However, due to the geometry of neutralization tanks, each tank is administratively limited to no more than 450 g Pu, thus representing the transition from geometry to mass control. To ensure that this mass limit is not exceeded, an administrative control requires taking two samples of any solution to be sent to a neutralization tank. Normal operations are expected to result in less than 50 g Pu in a neutralization tank filled to its capacity of 250 L. Process upsets may cause an inadvertent transfer of up to 1000 g Pu to a neutralization tank, producing a system that is potentially not subcritical for all possible configurations of the tank. To ensure that a neutralization tank remains safety subcritical, passive engineered controls (e.g.changing tank geometry or adding fixed poisons), active engineered controls (e.g. interlocks), and administrative controls (e.g. soluble poisons and valve isolation) were considered and their viability evaluated. Ultimately, the team chose an administrative, dual-valve isolation strategy. This paper will thoroughly discuss the various control strategy options and why many of the options were not feasible for maintaining criticality safety.

Dressman, Phillip M. [Savannah River Nuclear Solut↗
DOE OSTI2023

A simple, validated approach for design of two-dimensional periodic particle patterns via acoustophoresis

Two-dimensional patterning of microparticles enables a wide range of functional materials, including patterned energy storage electrodes, flexible electronics, and sensor arrays. Particle patterning via acoustics offers an attractive path to generate a wide variety of 2D periodic patterns that introduce tailorable hierarchical porosity, useful for controlling surface area, transport distances, and other properties. This method is most effective with micron scale particles and patterns of tens to hundreds of microns. To enable systematic exploration of the broad design space for such patterns, this work develops a model of 2D and 3D assembly of particles at high loadings and validates the obtained patterns against both experiments and more computationally intensive modeling techniques. Using this simple model, connections are mapped between input parameters (like actuation conditions, particle volume fraction, material properties) and output geometrical features (like void size and shape, pattern connectivity, and surface area) so that they can be tailored to given applications. The utility of this simple model is illustrated by predicting and then experimentally demonstrating new hierarchical patterns resulting from multiple waves of different frequencies interacting. These multiscale patterns offer the potential to lift the limits on surface area, diffusion distances, and other features.

42 ENGINEERING↗
DOE OSTI2023

Twisted moiré photonic crystal enabled optical vortex generation through bound states in the continuum

The twisted stacking of two layered crystals has led to the emerging moiré physics as well as intriguing chiral phenomena such as chiral phonon and photon generation. In this work, we identified and theoretically formulated a non-trivial twist-enabled coupling mechanism in twisted bilayer photonic crystal (TBPC), which connects the bound state in the continuum (BIC) mode to the free space through the twist-enabled channel. Moreover, the radiation from TBPC hosts an optical vortex in the far field with both odd and even topological orders. We quantitatively analyzed the twist-enabled coupling between the BIC mode and other non-local modes in the photonic crystals, giving rise to radiation carrying orbital angular momentum. The optical vortex generation is robust against geometric disturbance, making TBPC a promising platform for well-defined vortex generation. As a result, TBPCs not only provide a new approach to manipulating the angular momentum of photons, but may also enable novel applications in integrated optical information processing and optical tweezers. Our work broadens the field of moiré photonics and paves the way toward the novel application of moiré physics.

42 ENGINEERING↗
DOE OSTI2024

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗
DOE OSTI2023

Laser Welded Burst Disk Weld Tooling

A production run of 5550 hermetically sealed cylinder assemblies with precision pressure relief burst discs will be assembled and laser welded at a manufacturing lab at Sandia National Labs. Production of these cylinder assemblies requires many steps, including piece part machining, geometric inspection, cleaning, subassembly, complete assembly via laser welding, and finally leak checking. While this production run is large enough to invest in process optimization and specialized tooling, it is not quite large enough to dedicate new lab space and specify equipment specifically for this job. This study will investigate process parameters and their effects on quality and process flow time, as well as a fixturing design study with the goal of reducing process time while maintaining quality requirements.

42 ENGINEERING↗