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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Multiscale Investigation of the Mechanisms Controlling Materials Degradation in the Fusion Energy Environment (DE-SC0006661: Final Report)

Realizing the promise of fusion as a commercially attractive energy source requires the development of advanced structural materials capable of sustained operation in an incredibly hostile environment. The fusion environment presents many challenges associated with high cyclic thermal-mechanical loadings, welding and joining disparate materials, and achieving chemical compatibility with coolants and tritium breeders. Yet, the overarching concern is the degradation of physical and mechanical properties, resulting from a neutron energy spectrum peaked at 14 MeV. The high-energy fusion neutron irradiation produces both displacement damage and high levels of hydrogen and helium through transmutation reactions. Advanced materials development for use in such a hostile environment is predicated on understanding the underlying mechanisms responsible for physical and material property degradation. This project has closely combined computational, theoretical and experimental techniques within a multiscale materials science paradigm to determine the mechanisms controlling material degradation in the fusion environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗
DOE OSTI2023

RootSlice —A novel functional-structural model for root anatomical phenotypes

Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

DNA-assembled superconducting 3D nanoscale architectures

Studies of nanoscale superconducting structures have revealed various physical phenomena and led to the development of a wide range of applications. Most of these studies concentrated on one- and two-dimensional structures due to the lack of approaches for creation of fully engineered three-dimensional (3D) nanostructures. Here, we present a ‘bottom-up’ method to create 3D superconducting nanostructures with prescribed multiscale organization using DNA-based self-assembly methods. We assemble 3D DNA superlattices from octahedral DNA frames with incorporated nanoparticles, through connecting frames at their vertices, which result in cubic superlattices with a 48 nm unit cell. The superconductive superlattice is formed by converting a DNA superlattice first into highly-structured 3D silica scaffold, to turn it from a soft and liquid-environment dependent macromolecular construction into a solid structure, following by its coating with superconducting niobium (Nb). Through low-temperature electrical characterization we demonstrate that this process creates 3D arrays of Josephson junctions. This approach may be utilized in development of a variety of applications such as 3D Superconducting Quantum interference Devices (SQUIDs) for measurement of the magnetic field vector, highly sensitive Superconducting Quantum Interference Filters (SQIFs), and parametric amplifiers for quantum information systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

High-throughput design of high-performance lightweight high-entropy alloys

Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cobalt-free composite-structured cathodes with lithium-stoichiometry control for sustainable lithium-ion batteries

Abstract Lithium-ion batteries play a crucial role in decarbonizing transportation and power grids, but their reliance on high-cost, earth-scarce cobalt in the commonly employed high-energy layered Li(NiMnCo)O 2 cathodes raises supply-chain and sustainability concerns. Despite numerous attempts to address this challenge, eliminating Co from Li(NiMnCo)O 2 remains elusive, as doing so detrimentally affects its layering and cycling stability. Here, we report on the rational stoichiometry control in synthesizing Li-deficient composite-structured LiNi 0.95 Mn 0.05 O 2 , comprising intergrown layered and rocksalt phases, which outperforms traditional layered counterparts. Through multiscale-correlated experimental characterization and computational modeling on the calcination process, we unveil the role of Li-deficiency in suppressing the rocksalt-to-layered phase transformation and crystal growth, leading to small-sized composites with the desired low anisotropic lattice expansion/contraction during charging and discharging. As a consequence, Li-deficient LiNi 0.95 Mn 0.05 O 2 delivers 90% first-cycle Coulombic efficiency, 90% capacity retention, and close-to-zero voltage fade for 100 deep cycles, showing its potential as a Co-free cathode for sustainable Li-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Confinement-controlled selective CO 2 insertion into a dicopper dihydride core: A multiscale mechanistic study

CO 2 is an abundant C 1 feedstock for fuel and chemical synthesis. We have previously demonstrated experimentally a stepwise insertion of CO 2 into a [Cu 2 H 2 ] core via a solid–gas in crystallo reaction, forming formate species that are unstable and inaccessible under solution-phase conditions. This work elucidates how structural confinement within the crystal lattice enables such selective reactivity. In particular, co-crystallized tetrahydrofuran molecules induce site asymmetry around the [Cu 2 H 2 ] unit, modulating both the local electronic environment and CO 2 diffusion pathways. Using a multiscale computational approach that combines classical molecular mechanics, hybrid quantum mechanics/molecular mechanics molecular dynamics, and enhanced-sampling free energy calculations, we demonstrate how site asymmetry affects CO 2 binding affinities and reaction pathways. These results provide detailed mechanistic insight into CO 2 insertion and hydride transfer, highlighting key differences between crystal- and solution-phase pathways and offering a general framework for understanding how lattice confinement shapes chemical reactivity.

Chemical bonding↗
DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
DOE OSTI2021

The persistence of large scale structures. Part I. Primordial non-Gaussianity

Abstract We develop an analysis pipeline for characterizing the topology of large scale structure and extracting cosmological constraints based on persistent homology . Persistent homology is a technique from topological data analysis that quantifies the multiscale topology of a data set, in our context unifying the contributions of clusters, filament loops, and cosmic voids to cosmological constraints. We describe how this method captures the imprint of primordial local non-Gaussianity on the late-time distribution of dark matter halos, using a set of N-body simulations as a proxy for real data analysis. For our best single statistic, running the pipeline on several cubic volumes of size 40 (Gpc/h) 3 , we detect f NL loc =10 at 97.5% confidence on ~ 85% of the volumes. Additionally wetest our ability to resolve degeneracies betweenthe topological signature of f NL loc and variation of σ 8 and argue that correctly identifying nonzero f NL loc in this case is possible via an optimal template method. Our method relies on information living at $\mathcal{O}$(10) Mpc/h, a complementary scale with respect to commonly used methods such as the scale-dependent bias in the halo/galaxy power spectrum. Therefore, while still requiring a large volume, our method does not require sampling long-wavelength modes to constrain primordial non-Gaussianity. Moreover, our statistics are interpretable: we are able to reproduce previous results in certain limits and we make new predictions for unexplored observables, such as filament loops formed by dark matter halos in a simulation box.

Astronomy & Astrophysics↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Multiphysics and Multiscale Simulation Methods for Electromagnetic Energy Assisted Fossil Fuel to Hydrogen Conversion (Final Scientific/Technical Report)

This report summarizes the technical accomplishments of the four-year research project “Multiphysics and Multiscale Simulation Methods for Electromagnetic Energy Assisted Fossil Fuel to Hydrogen Conversion” (Award No. DE-FE0032092), conducted at Howard University and the University of Houston (subawardee) from September 2021 to August 2025. The project successfully achieved all four major objectives: 1. 3D Structural Characterization – Developed 3D optical imaging and mechanical sectioning methods to characterize catalyst distribution and support morphology in nickel foam substrates. Successfully reconstructed 3D geometries and imported them into COMSOL Multiphysics for electromagnetic simulations. 2. EM Hotspot Simulation – Created all-frequency stable electromagnetic formulations and 3D nodal discontinuous Galerkin (NDG) methods for coupled electromagnetic-thermal-fluid problems in multiscale catalytic media. Demonstrated stable solutions from DC to microwave frequencies. 3. Multiphysics Coupling – Developed multiscale simulation methods coupling FEM electromagnetic solvers with thermal transport equations. Reactive molecular dynamics (ReaxFF MD) simulations were performed to investigate catalytic reaction mechanisms at the atomistic level. Demonstrated electromagnetic-thermal co-simulation capabilities for porous catalyst structures. 4. System Optimization – Designed and optimized EM-assisted catalytic systems using nickel foam and carbon foam structures, demonstrating significant temperature increases due to microwave heating. Observed and characterized plasma generation in carbon fiber catalysts. Investigated multiple reaction chamber geometries for improved microwave energy deposition. The project produced significant scientific contributions including 15+ peer-reviewed publications, trained multiple Ph.D. students and undergraduate researchers, and advanced the understanding of microwave-assisted hydrogen production from fossil fuels.

08 HYDROGEN↗
DOE OSTI2024

A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations

The structure of the magnetic reconnection boundary, particularly the presence of slow-mode shocks in the near-Earth magnetotail was studied by using magnetospheric multiscale (MMS) observations and 2.5D hybrid simulations. A total of 51 crossings of MMS from 2017 to 2021 were analyzed. We found that the detection percentage of slow-mode shocks in the near-Earth magnetotail is 41%–55%. Previous studies have only reported one slow-mode shock event in the near-Earth magnetotail and a slow-mode shock detection percentage of 10% or lower in the mid-to-distant magnetotail. It was observed that if the high-energy beam region data is removed from the slow-mode shock downstream observations then the detection of slow-mode shocks reduces, implying that the kinetic effects play an important role in the detection of slow-mode shocks. For the crossings where the interface was not identified as a slow-mode shock, it was found that the turbulence in those crossings can change the mass flux values and disrupt the detection of slow-mode shock. However, the macroscopic slow-mode shock-like structure stably exists around the magnetic reconnection interface, as most of the conditions for slow-mode shocks were satisfied. This result suggests that slow-mode shocks are a general feature of magnetic reconnection geometry. We find that the lack of detection of slow-mode shocks in previous observations and simulations can be explained by taking into account the kinetic structure of slow-mode shocks and the presence of turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Three-dimensional inhomogeneity of electron-temperature-gradient turbulence in the edge of tokamak plasmas

Nonlinear multiscale gyrokinetic simulations of a Joint European Torus edge pedestal are used to show that electron-temperature-gradient (ETG) turbulence has a rich three-dimensional structure, varying strongly according to the local magnetic-field configuration. In the plane normal to the magnetic field, the steep pedestal electron temperature gradient gives rise to anisotropic turbulence with a radial (normal) wavelength much shorter than in the binormal direction. In the parallel direction, the location and parallel extent of the turbulence are determined by the variation in the magnetic drifts and finite-Larmor-radius (FLR) effects. The magnetic drift and FLR topographies have a perpendicular-wavelength dependence, which permits turbulence intensity maxima near the flux-surface top and bottom at longer binormal scales, but constrains turbulence to the outboard midplane at shorter electron-gyroradius binormal scales. Here our simulations show that long-wavelength ETG turbulence does not transport heat efficiently, and significantly decreases overall ETG transport—in our case by ~40%—through multiscale interactions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Emerging Modification Technologies of Lignin-based Activated Carbon toward Advanced Applications

Lignin-based activated carbon (LAC) is a promising high-quality functional material due to high surface area, abundant porous structure, and various functional groups. Modification is the most important step to functionalize LAC by altering its porous and chemical properties. Here, this Review summarizes the state-of-the-art modification technologies of LAC toward advanced applications. Promising modification approaches are reviewed to display their effects on the preparation of LAC. The multiscale changes in the porosity and the surface chemistry of LAC are fully discussed. Advanced applications are then introduced to show the potential of LAC for supercapacitor electrode, catalyst support, hydrogen storage, and carbon dioxide capture. Finally, the mechanistic structure-function relationships of LAC are elaborated. These results highlight that modification technologies play a special role in altering the properties and defining the functionalities of LAC, which could be a promising porous carbon material toward industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Determining Catalytically Relevant Surfaces through Coverage-Dependent Lattice Gas Models: Carbon Adsorption on Fe(100)

Here, we have quantified the C–C lateral interactions on Fe(100) using a density functional theory (DFT)-parameterized lattice gas cluster expansion (LG CE) model trained using 265 unique configurations spanning a C coverage from 0 to 1 monolayer (ML). Our LG CE model shows high predictive accuracy with a leave-multiple-out cross-validation score of 10.2 and 16.6 meV/site for systems with and without the top two layers of Fe atoms fixed, respectively. Electronic ground-state structures identified from the lattice gas model (including the structures at 0 and 1 monolayers) were further used to generate ab initio phase diagrams under a range of temperatures and pressures. At low temperatures (<400 K), we found that the 1.0 monolayer structure is dominant, whereas at higher temperatures (>500 K), the 0.88 ML structure is most likely to form on the Fe surface. Interestingly, our model identified a c (2 × 2) ordered structure at 1/2 ML, which correlates well with previous DFT studies for carbon adsorption on iron surfaces and matches with the experimentally observed low-energy electron diffraction structure. Overall, the DFT-parameterized energies for the C/Fe system including effects of coverage and configurational space can further help in developing multiscale models for various heterogeneous reactions involving C–C and C–Fe interactions.

08 HYDROGEN↗
DOE OSTI2020

Surface and bulk characterization of reservoir and cap-rocks: Past, present, and future

Geologic formations provide potentially some of the largest volume capacities for CO 2 storage or sequestration. Potential storage sites can be deep saline aquifers, depleted oil reservoirs, and coal seams, surrounded by sealing layers to prevent CO 2 from leaking. It is therefore critical to understand mechanisms contributing to CO 2 trapping and CO 2 leaks. Both phenomena are governed by reactions at the interfaces of the reservoir and cap-rocks and are controlled by the complex chemistry and pore structures of rocks. Mechanisms at the macroscale are affected by the processes occurring at the nanoscale. This review highlights the necessity of multitechnique, multiscale characterization of rocks and points to the importance of surface analysis and surface science studies. Two shale rocks (seals) from Niobrara and Agardhfjellet formations with complex surface chemistry are used as examples throughout the paper. Typically, evaluation of rocks with x-ray diffraction, thermogravimetric analysis, Rock-Eval pyrolysis, gas adsorption, and electron microscopy combined with energy dispersive x-ray spectroscopy is conducted to provide valuable information about the bulk mineralogy, elemental composition, pore volume, and adsorbed species on the sample surface. These studies are necessary prior to designing surface sensitive experiments with x-ray photoelectron spectroscopy (XPS), guiding both sample preparation and sample analysis. XPS has been widely used to study the surface composition of rocks during the investigations of their fine-scale wettability, and the main findings are highlighted here. This paper also reviews the existing literature on ambient-pressure XPS, which provides new opportunities to study in situ chemical alteration due to interactions with CO 2 and offers recommendations for adapting this technique to study rock-fluid interactions, allowing for the identification of fundamental interactions during CO 2 sequestration and guide selection of formation sites for improved storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

In-Situ Visualization of Long-Range Defect Interactions at the Edge of Melting

Connecting a bulk material’s microscopic defects to its macroscopic properties is an age-old problem in materials science. Long-range interactions between dislocations (line defects) are known to play a key role in how materials deform or melt, but we lack the tools to connect these dynamics to the macroscopic properties. We introduce time-resolved dark-field X-ray microscopy to directly visualize how dislocations move and interact over hundreds of micrometers, deep inside bulk aluminum. With real-time movies, we reveal the thermally activated motion and interactions of dislocations that comprise a boundary, and show how weakened binding forces inhomogeneously destabilize the structure at 99% of the melting temperature. Connecting dynamics of the microstructure to its stability, we provide important opportunities to guide and validate multiscale models that are yet untested.

36 MATERIALS SCIENCE↗