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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Methods—Meso-Scale Electrodes for Characterizing Diffusion-Reaction Properties of Redox-Active Organics in Viscous Electrolytes

Deep Eutectic Solvents (DESs) have recently gained interest as flow battery electrolytes. Their advantages include a wider electrochemical stability window compared to aqueous electrolytes, higher solubility for redox-active species, and negligible volatility. However, DESs are often highly viscous, and suffer from low ionic conductivities. This can make assessing redox kinetics difficult when attempting to determine their viability for energy storage. In classical voltammetric measurements, low ionic conductivity manifests as high solution resistance, thereby requiring "live" compensation of the electrolyte ohmic drop when performing fast-scan voltammetry. An uncompensated or inadequately-compensated ohmic drop leads to misinterpretation of the voltammetric behavior, e.g., assessing reversibility vs. irreversibility of a redox reaction. Here, we present micro-fabricated electrodes as facile "meso-scale" electrodes, which overcome these issues by nearly eliminating the ohmic drop while retaining uniformity of the current distribution over the electrode surface. Their use in precise transport-kinetics measurements is demonstrated using a redox-active organic, i.e., 4-Hydroxy-TEMPO in an aqueous medium and in ethaline, which is a viscous DES. This study provides a methodical approach to design and to implement voltammetry experiments using meso-scale electrodes leading to reliable measurements of diffusion-reaction properties of 4-Hydroxy-TEMPO.

25 ENERGY STORAGE↗
DOE OSTI2024

Ionic Diffusion in Slurry Electrolytes for Redox Flow Batteries

Slurry electrodes have been proposed as a method to decouple the storage and power capacities of hybrid redox flow batteries by allowing the reduced metal to adhere to a flowing dispersion of electrically conductive particles rather than accumulate in the flow cell. In this work, the ionic mass transport through these slurry electrolytes is investigated via voltammetry at a rotating disc electrode and in a parallel channel flow cell. A modified Levich equation, i l = 0.6 2 nFC ω 1 / 2 ν − 1 / 6 ( D + r 2 Φ ω m ) 2 / 3 , is developed to describe limiting currents to a rotating disc electrode when using a particle filled electrolyte. Mass transfer is demonstrated to be enhanced by greater particle loading so long as there are sufficient particles such that their respective flow velocity boundary layers interact. In the flow cell, faradaic current in a carbon black slurry electrolyte was observed to decrease over time. This suggests that the carbon particles adhere and accumulate on the stationary electrode, impeding mass transport by imposing diffusion path tortuosity. This accumulation is undesirable in hybrid redox flow batteries as it encourages metal deposition in the cell, limiting the scalability of the technology.

Electrochemistry↗
DOE OSTI2025

A Diffuse-Interface Model for Predicting the Evolution of Metallic Negative Electrodes and Interfacial Voids in Solid-State Batteries with Homogeneous and Polycrystalline Solid Electrolyte Separators

Here, this paper presents a novel diffuse-interface electrochemical model that simultaneously simulates the evolution of the metallic negative electrode and interfacial voids during the stripping and plating processes in solid-state batteries. The utility and validity of this model are demonstrated for the first time on a cell with a sodium (Na) negative electrode and a Na-β″-alumina ceramic solid electrolyte (SE) separator. Three examples are simulated. First, stripping and plating with a perfect electrode/electrolyte interface; second, stripping and plating with a single interfacial void at the electrode/electrolyte interface; third, stripping with multiple interfacial voids. Both homogeneous and polycrystalline SEs with low and high-conductivity grain boundaries (GBs) are considered for all three examples. Heterogeneous GB conductivity marginally impacts the thickness of the Na electrode in cases with a perfect electrode/electrolyte interface. Moreover, it results in local changes to void growth due to the interactions between the void edge and the GBs. The void growth rate is a linear function of the flux of Na atoms at the void edge, which in turn depends on the applied current density. We also show that the void coalescence rate increases with applied current density and can be marginally influenced by GB conductivity.

batteries↗
DOE OSTI2026

Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

42 ENGINEERING↗
DOE OSTI2020

Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO2R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. Here, we specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Defect diffusion graph neural networks (d2gnn)

SAND2025-01004O Defect Diffusion Graph Neural Networks (d2gnn) is a software tool that assists in the discovery of new materials for high-temperature, clean-energy applications. It uses advanced graph neural networks to model the relationship between material structures and their defect properties. The application helps predict how materials will behave under different conditions and accelerates the development of innovative materials. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Witman, Matthew [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗
DOE OSTI2025

Boundary Effects in the Diffusion of New Products on Cartesian Networks

The Role of Boundaries in the Spreading of Solar Peer effects by neighbors play a key role in the spreading of residential solar. Thus, people are more likely to install a solar system on their roof if some of their neighbors have already done so. Because people who live near the municipality boundary have fewer neighbors, does this imply that they are less likely to adopt solar? In “Boundary Effects in the Diffusion of New Products on Cartesian Networks,” Fibich, Levin, and Gillingham analyze this problem analytically using the Bass model on two-dimensional networks and empirically using data on installations of solar systems. They show that boundaries have a significant impact on the adoption of residential units near the municipality boundary. Their effect on the aggregate adoption in the municipality, however, is negligible.

Business & Economics↗
DOE OSTI2022

Error analysis of numerical methods for thick diffusive neutron transport problems on Shishkin mesh

A thin layer will develop at the boundary if the incoming angular flux is anisotropic in thick diffusive neutron transport problems. Solving such singularly perturbed problems, which have non-smooth solutions with singularity near the boundary, is computationally challenging. Standard finite difference schemes on a uniform mesh cannot yield ε-uniform convergence, where ε is a small parameter, while it can be achieved on a suitable piecewise-uniform Shishkin mesh. We present a formal error analysis of the diamond difference (DD) method and step difference (SD) method for solving the S{sub N} neutron transport equation. The analysis can be extended to other finite difference methods. Numerical results are presented to confirm the error estimates and the advantages of the Shishkin mesh. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

CEFR simulation using diffusion code system RAST-F

In this study, the RAST-F code system, which is based on the two-step approach that couples a multi-group cross-section generation Monte-Carlo (MC) code and a multi-group nodal diffusion solver, was used for the neutronic simulation of the CEFR start-up experiments. The numerical solution of the RAST-F system was verified against the full core MC solution MCS at all control rods fully inserted and withdrawn states. The RAST-F solution of the selected experimental simulations was compared against the measurement data. A good agreement between RAST-F and MCS solutions was observed with less than 120 pcm discrepancies and 1.2% root-mean-square error in terms of k{sub eff} and power distribution, respectively. Meanwhile, the RAST-F result agreed well with the experimental data within two-sigma of experimental uncertainty. The good agreement of these results indicates that RAST-F can be used to neutronic steady-state simulations for small core-size SFR, which was challenged to deterministic code systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Hierarchical model reduction driven by a proper orthogonal decomposition for parametrized advection-diffusion-reaction problems

This work combines the Hierarchical Model (HiMod) reduction technique with a standard Proper Orthogonal Decomposition (POD) to solve parametrized partial differential equations for the modeling of advection-diffusion-reaction phenomena in elongated domains (e.g., pipes). This combination leads to what we define as HiPOD model reduction, which merges the reliability of HiMod reduction with the computational efficiency of POD. Two HiPOD techniques are presented and assessed by an extensive numerical verification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The ab initio amorphous materials database: Empowering machine learning to decode diffusivity

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductiv- ity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Analysis of vapor diffusion Nb$^{3}$Sn coating at Fermilab: Minimizing impurities using TOF-SIMS

Nb₃Sn demonstrates steady advancements nowadays offering reduced power cost in superconducting radio-frequency cavities due to its high critical temperature, quality factor, and achieved accelerating gradient. However, theoretical estimates of its radio-frequency parameters have not been achieved due to several potentially limiting mechanisms: tin spots, patchy regions, defects, thermal impedance, and impurities. While some of these limitations have been intensively studied, impurity analysis in Nb$_3$Sn coatings have received less attention. We report an investigation of impurities in several vapor-diffused Nb₃Sn coated samples using time-of-flight secondary ion mass spectroscopy (TOF-SIMS) and show allowable impurity levels in view of superconducting cavity performance. Challenges and lessons learned in maintaining clean Nb$_3$Sn coatings are also discussed.

Tagdulang, Nikki [Fermilab]↗
DOE OSTI2022

Managing Sn-Supply to Tune Surface Characteristics of Vapor-Diffusion Coating of Nb₃Sn

Nb₃Sn promises better RF performance (Q and E_{acc}) than niobium at any given temperature because of superior superconducting properties. Nb₃Sn-coated SRF cavities are now produced routinely by growing a few microns thick Nb₃Sn films inside Nb cavities via the tin vapor diffusion technique. Sn evaporation and consumption during the growth process notably affect the quality of the coating. Aiming at favorable surface characteristics that could enhance the RF performance, many coatings were produced by varying Sn sources and temperature profiles. Coupon samples were examined using different material characterization techniques, and a selected few sets of coating parameters were used to coat 1.3 GHz single-cell cavities for RF testing. The Sn supply’s careful tuning is essential to manage the microstructure, roughness, and overall surface characteristics of the coating. We summarize the material analysis of witness samples and discuss the performance of several Nb₃Sn-coated single-cell cavities linked to Sn-source characteristics and observed Sn consumption during the film growth process.

Pudasaini, U.↗
DOE OSTI2023

Rapid Simulation of Unconventional Reservoirs by Multidomain Multiresolution Modeling Based on the Diffusive Time of Flight

Summary The fast marching method (FMM)-based rapid flow simulation has been shown to accelerate simulation efficiency by orders of magnitude by transforming 3D simulation to equivalent 1D simulation using the concept of the “diffusive time of flight” (DTOF). However, the 1D transformation does not directly apply to multiwell problems. In this paper, we propose a novel DTOF-based multidomain multiresolution discretization scheme to accelerate multiwell simulation of unconventional reservoirs. Our method formulates multiwell simulation problems based on the DTOF which displays the pressure front propagation in unconventional reservoirs. The DTOF contours are used to partition the reservoir into local and shared domains. A local domain is where the flow is dominated by a single well, and the shared domain is where the fluid flow is influenced by multiple wells. The DTOF contours expand independently in local domains and interfere in the shared domain. After the partitioning, each domain is discretized using a multiresolution scheme whereby the original 3D fine mesh is preserved near the wells to account for detailed physics including gravity, and the rest of the domain is discretized into 1D mesh based on the DTOF contours to alleviate the simulation workload. The power and efficacy of our approach are demonstrated using synthetic and field-scale simulation models with different degrees of geologic and well-completion complexity. The simulation results, number of active cells, and computation time for the proposed discretization scheme are compared with the original high-fidelity 3D model for each case. The results show that the proposed method is suitable for multiwell simulation problems in unconventional reservoirs and can accelerate flow simulations by orders of magnitude with minimal loss of accuracy. The novelty of this work is the creation of DTOF-derived multiresolution discretization with local and shared domains to simplify and accelerate the calculation of subsurface flow problems, especially in unconventional reservoirs. Our workflow can be easily interfaced with commercial simulators, making it suitable for large-scale field applications.

Engineering↗
DOE OSTI2025

Full event particle-level unfolding with variable-length latent variational diffusion

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.

Shmakov, Alexander↗
DOE OSTI2025

Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models

Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗