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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Lattice Litany for Transition Metal Oxides

In this tribute to K Alex Müeller, I describe how his early insights have influenced future decades of research on perovskite ferroelectrics and more broadly transition metal oxides (TMOs) and related quantum materials. I use his influence on my own research journey to discuss impacts in three areas: structural phase transitions, precursor structure, and quantum paraelectricity. I emphasize materials functionality in ground, metastable, and excited states arising from competitions among lattice, charge, and spin degrees of freedom, which results in highly tunable landscapes and complex networks of multiscale configurations controlling macroscopic functions. I discuss competitions between short- and long-range forces as particularly important in TMOs (and related materials classes) because of their localized and directional metal orbitals and the polarizable oxygen ions. I emphasize crucial consequences of elasticity and metal–oxygen charge transfer

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗
NASA NTRS2008

Multiscale Computer Simulation of Failure in Aerogels

Aerogels have been of interest to the aerospace community primarily for their thermal properties, notably their low thermal conductivities. While such gels are typically fragile, recent advances in the application of conformal polymer layers to these gels has made them potentially useful as lightweight structural materials as well. We have previously performed computer simulations of aerogel thermal conductivity and tensile and compressive failure, with results that are in qualitative, and sometimes quantitative, agreement with experiment. However, recent experiments in our laboratory suggest that gels having similar densities may exhibit substantially different properties. In this work, we extend our original diffusion limited cluster aggregation (DLCA) model for gel structure to incorporate additional variation in DLCA simulation parameters, with the aim of producing DLCA clusters of similar densities that nevertheless have different fractal dimension and secondary particle coordination. We perform particle statics simulations of gel strain on these clusters, and consider the effects of differing DLCA simulation conditions, and the resultant differences in fractal dimension and coordination, on gel strain properties.

Good, Brian S.↗
DOE OSTI2025

Janus Superiority of Membranes in Chemical Engineering and Beyond

Janus configurations, characterized by their inherent asymmetry, enable directional mass transfer in membrane materials that drive novel and energy-efficient chemical processes. This Janus superiority spans applications from nanoscale molecular and ionic transport to macro-scale separation systems with asymmetric spatial architectures. This review provides an analysis of the material foundations including design principles, structure regulation, and scalability challenges underlying Janus membranes. Here, we explore the physics that governs their unique behavior and examine their diverse applications across chemical engineering, including phase transfer, and molecular or ionic transport. Through a multiscale perspective, we provide a comprehensive understanding of the impact of Janus superiority in advancing chemical engineering technologies. Finally, we discuss the hurdles in translating theoretical advances into practical applications and propose promising avenues for future research to harness the full potential of Janus membranes and systems in addressing global challenges related to energy, sustainability, and beyond.

Yang, Hao‐Cheng [Zhejiang University, Hangzhou (Ch↗
DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation

Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.

cellulose↗
DOE OSTI2021

Multiphase, Multiscale Chemomechanics at Extreme Low Temperatures: Battery Electrodes for Operation in a Wide Temperature Range

Understanding the behavior of lithium-ion batteries (LIBs) under extreme conditions, for example, low temperature, is key to broad adoption of LIBs in various application scenarios. LIBs, poor performance at low temperatures is often attributed to the inferior lithium-ion transport in the electrolyte, which has motivated new electrolyte development as well as the battery preheating approach that is popular in electric vehicles. A significant irrevocable capacity loss, however, is not resolved by these measures nor well understood. Herein, multiphase, multiscale chemomechanical behaviors in composite LiNi x Mn y Co z O 2 (NMC, x + y + z = 1) cathodes at extremely low temperatures are systematically elucidated. The low-temperature storage of LIBs can result in irreversible structural damage in active electrodes, which can negatively impact the subsequent battery cycling performance at ambient temperature. Furthermore, beside developing electrolytes that have stable performance, designing batteries for use in a wide temperature range also calls for the development of electrode components that are structurally and morphologically robust when the cell is switched between different temperatures.

25 ENERGY STORAGE↗
DOE OSTI2022

In situ infrared nanospectroscopy of the local processes at the Li/polymer electrolyte interface

Solid-state batteries possess the potential to significantly impact energy storage industries by enabling diverse benefits, such as increased safety and energy density. However, challenges persist with physicochemical properties and processes at electrode/electrolyte interfaces. Thus, there is great need to characterize such interfaces in situ, and unveil scientific understanding that catalyzes engineering solutions. To address this, we conduct multiscale in situ microscopies (optical, atomic force, and infrared near-field) and Fourier transform infrared spectroscopies (near-field nanospectroscopy and attenuated total reflection) of intact and electrochemically operational graphene/solid polymer electrolyte interfaces. We find nanoscale structural and chemical heterogeneities intrinsic to the solid polymer electrolyte initiate a cascade of additional interfacial nanoscale heterogeneities during Li plating and stripping; including Li-ion conductivity, electrolyte decomposition, and interphase formation. Moreover, our methodology to nondestructively characterize buried interfaces and interphases in their native environment with nanoscale resolution is readily adaptable to a number of other electrochemical systems and battery chemistries.

42 ENGINEERING↗
NASA NTRS2018

Direct Observation of Electron Distributions Inside Millisecond Duration Electron Holes

Despite the importance of millisecond duration spatial structures [chorus wave nonlinearity or time domain structures (TDS)] to plasma dynamics, there have been no direct observations of the generation and interaction of these waves and TDS with electrons at the millisecond timescale required for their understanding. Through superposition of 0.195 ms Magnetospheric Multiscale Satellite electron measurements inside 37 superposed, millisecond duration electron holes, the first observations of electron spectra and pitch angle distributions on a submillisecond timescale have been obtained. They show that keV electrons inside the superposed electron hole are accelerated by several hundred volts and that the spectrum of electrons inside the electron hole contain several maxima and minima that are explained by a model of electron energy changes on entering the holes. We report the first observation of trapped electrons inside the TDS, in accordance with the theoretical requirement that such electrons must be present in order to form the phase space holes. Mechanisms of electron acceleration by electron holes (through perpendicular energy gain as the TDS moves into a converging magnetic field) and scattering (due to the perpendicular electric field) are discussed.

Mozer, F. S.↗
DOE OSTI2021

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2021

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Abstract The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( $$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗
NASA NTRSDate not supplied

Multiscale Modeling of Woven Ablative Thermal Protection System Materials

The NASA Entry Systems Modeling project maintains a portfolio of computational model and tool development activities focused on reducing performance uncertainties in ablative Thermal Protection System (TPS) materials for NASA missions. The development activities span material scale and strive to allow microstructural characterization of material structure and properties, mesoscale analyses of damage, and macroscale evaluation of heatshield performance and recession in a given aerothermodynamic environment. This talk will detail the application of developed capabilities at all three scales to the woven TPS material that the Agency has selected as the heatshield for the Mars Sample Return Earth Entry System (MSR-EES) mission – 3D Mid-Density Carbon Phenolic (3MDCP). Each of the applications focuses on driving down uncertainties in material performance and thus risk for MSR-EES and other future missions that may leverage woven TPS. At the microscale, machine learning techniques are used to characterize images from destructive microscopy and inform structural variability. At the mesoscale, Lagrangian techniques are used to simulate ballistic impact and interpret damage modes noted in experiments. At the macroscale, coupled flow-material response techniques are validated by Arc Jet testing to enable heatshield design for missions with massive ablation.

Justin B Haskins↗
NASA NTRS2017

Magnetospheric Multiscale Mission Observations of the Outer Electron Diffusion Region

This paper presents Magnetospheric Multiscale mission (MMS) observations of the exhaust region in the vicinity of the central reconnection site in Earth's magnetopause current sheet. High-time-resolution measurements of field and particle distributions enable us to explore the fine structure of the diffusion region near the X line. Ions are decoupled from the magnetic field throughout the entire current sheet crossing. Electron jets flow downstream from the X line at speeds greater than the E by B drift velocity. At or around the magnetospheric separatrix, large-amplitude electric fields containing field-aligned components accelerate electrons along the magnetic field toward the X line. Near the neutral sheet, crescent-shaped electron distributions appear coincident with (1) an out-of-plane electric field whose polarity is opposite to that of the reconnection electric field and (2) the energy transfer from bulk kinetic to field energy. The observations indicate that MMS passed through the edge of an elongated electron diffusion region (EDR) or the outer EDR in the exhaust region.

Hwang, K.-J.↗
DOE OSTI2023

Advanced Multiscale Microscopy Characterization of High Burnup LWR UO 2 Before and After LOCA Testing

Previous microscopy work within the campaign has observed increased porosity coupled with subgrain formation in the mid-radial region of the fuel. However, examinations of high burnup fuel taken from multiple reactors with different operating conditions have shown that this structure can move radially inward depending on the fuel operating conditions. It is currently theorized that inter- and intragranular fission gas bubble nucleation is precursory to the grain subdivision observed in the mid-radial and central regions of the fuel. Characterization of samples after loss-of-coolant accident (LOCA) testing has revealed that these restructured regions with a high density of bubbles and subgrains pulverize during the transient test. It appears that the increase in fission gas bubbles coupled with grain subdivision renders the fuel mechanically weaker during a LOCA transient and thus susceptible to fuel fragmentation relocation and dispersal (FFRD). Work this past fiscal year has prioritized understanding this behavior by using advanced microscopy to investigate fission gas behavior in the as-irradiated and post-LOCA state. Additional work has been performed to verify the theory described above by analyzing multiple post-LOCA optical micrographs and comparing that to as-irradiated microstructural data. This document reports progress in the post-irradiation characterization of high burnup nuclear fuel with emphasis on the restructured fuel regions, particularly how microstructural features influence FFRD and fission gas release behavior in LOCA conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Topologically derived dislocation theory for twist and stretch moiré superlattices in bilayer graphene

In this work, we develop a continuum dislocation description of twist and stretch moiré superlattices in two-dimensional material bilayers. The continuum formulation is based on the topological constraints introduced by the periodic dislocation network associated with the moiré structure. The approach is based on solving analytically for the structural distortion and displacement fields that satisfy the topological constraints and that minimize the total energy. The total energy is described by both the strain energy of each individual distorted layer and a Peierls-Nabarro-like interfacial contribution arising from stacking disregistry. The dislocation core emerges naturally within the formalism as a result of the competition between the two contributions. The approach presented here captures the structure and energetics of twist and stretch moiré superlattices of dislocations with arbitrary direction and character, without assuming an analytical solution a priori and while accounting naturally for dislocation-dislocation image interactions. In comparisons to atomistic simulations using classical potentials, the maximum structure deviation is 6%, while the maximum line energy deviation is 0.019 eV/Å. Several applications of our model are shown, including predicting the variation of structure with twist angle and describing dislocation line tension and junction energies.

36 MATERIALS SCIENCE↗