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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Robust Interfacial Effect in Multi-interface Environment through Hybrid Reconstruction Chemistry for Enhanced Energy Storage

Electrochemical-oxidation-driven reconstruction has emerged as an efficient approach for developing advanced materials, but the reconstructed microstructure still faces challenges including inferior conductivity, unsatisfying intrinsic activity, and active-species dissolution. In this report we present hybrid reconstruction chemistry that synergistically couples electrochemical oxidation with electrochemical polymerization (EOEP) to overcome these constraints. During the EOEP process, the metal hydroxides undergo rapid reconstruction and dynamically couple with polypyrrole (PPy), resulting in an interface-enriched microenvironment. We observe that the interaction between PPy and the reconstructed metal center (i.e., Mn > Ni, Co) is strongly correlated. Theoretical calculation results demonstrate that the strong interaction between Mn sites and PPy breaks the intrinsic limitation of MnO 2 , rendering MnO 2 with a metallic property for fast charge transfer and enhancing the ion-adsorption dynamics. Operando Raman measurement confirms the promise of EOEP-treated Mn(OH) 2 (E-MO/PPy) to stably work under a 1.2 V potential window. The tailored E-MO/PPy exhibits a high capacitance of 296 F g –1 at a large current density of 100 A g –1 . Here our strategy presents breakthroughs in upgrading the electrochemical reconstruction technique, which enables both activity and kinetics engineering of electrode materials for better performance in energy-related fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics

Machine learning (ML), including deep learning (DL), has become increasingly popular in the last few years due to its continually outstanding performance. In this context, we apply machine learning techniques to "learn" the microstructure using both supervised and unsupervised DL techniques. In particular, we focus (1) on the localization problem bridging (micro)structure (localized) property using supervised DL and (2) on the microstructure reconstruction problem in latent space using unsupervised DL. The goal of supervised and semi-supervised DL is to replace crystal plasticity finite element model (CPFEM) that maps from (micro)structure (localized) property, and implicitly the (micro)structure (homogenized) property relationships, while the goal of unsupervised DL is (1) to represent high-dimensional microstructure images in a non-linear low-dimensional manifold, and (2) to discover a way to interpolate microstructures via latent space associating with latent microstructure variables. At the heart of this report is the applications of several common DL architectures, including convolutional neural networks (CNN), autoencoder (AE), and generative adversarial network (GAN), to multiple microstructure datasets, and the quest of neural architecture search for optimal DL architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

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

Microstructural and Micro-Chemical Evolutions in the Irradiated UCO Fuel Kernels of AGR-1 and AGR-2 TRISO Fuel Particles

AGR-1 and AGR-2 TRISO fuel particles were fabricated with slightly different fuel kernel chemical compositions, modified fabrication processes, different fuel kernel diameters, and changed 235U enrichments. To correlate those differences with the fuel kernel responses to neutron irradiations in terms of irradiated fuel microstructure, fission products chemical and physical states, and fission gas bubble evolutions, extensive microstructural and analytical characterizations were conducted. The studies used a state of art transmission electron microscopy (TEM) equipped with Energy-dispersive X-ray spectroscopy (EDS) of four silicon solid-state detectors which have super sensitivity and fast speed. The TEM specimens were prepared from selected AGR-1 and AGR-2 irradiated fuel kernels exposed to safety testing after irradiation. The particles were chosen to represent a representative irradiation conditions with a fuel burnup within the range from 10.8 to 18.6% FIMA, and the time-average volume-average temperatures vary from 1070 to 1287°C. The 235U enrichment was 19.74 wt.% for the AGR1 fuel kernels and 14.03 wt.% for the AGR-2 fuel kernels. The TEM results show that there were significant microstructural reconstructions in the irradiated fuel kernels for both the AGR-1 and AGR-2 fuels. There are four major phases including fuel matrix of UO2 and UC, U2RuC2, and UMoC2 in the irradiated AGR2 fuel kernel. Zr and Nb form solid solution in the UC phase. UMoC2 phase often shows a detectable concentration of Tc. Pd was found to mainly locate in the buffer layer or to be associated with fission gas bubble within the UMoC2 phase. The EDS maps qualitatively show that the rare-earth fission products (Nb, et al.) preferentially reside in the UO2 phase. In contrast, in the irradiated AGR1 fuel kernel, no U2RuC2 or UMoC2 precipitates were positively identified. Instead, there is a high number of rod-shape precipitates enriched with Ru, Tc, Rh, and Pd observed in the fuel kernel center and edge zone. The difference of microstructural and micro-chemical evolutions in irradiated fuel kernels between the AGR-1 and AGR-2 TRISO fuel particle may result from a combined factor of irradiation temperature, fuel geometry and chemical composition. However, the irradiation temperature probably play a more deterministic role. Limited electron energy loss spectroscopy (EELS) characterizations on the AGR2 fuel kernel show that there is nearly no carbon in the UO2 phase while a small fraction of oxygen was detected in the UC/UMoC2 phase.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Buried Interfaces in Halide Perovskite Photovoltaics

Understanding the fundamental properties of buried interfaces in perovskite photovoltaics is of paramount importance to the enhancement of device efficiency and stability. Nevertheless, accessing buried interfaces poses a sizeable challenge because of their non-exposed feature. In this paper, the mystery of the buried interface in full device stacks is deciphered by combining advanced in situ spectroscopy techniques with a facile lift-off strategy. By establishing the microstructure-property relations, the basic losses at the contact interfaces are systematically presented, and it is found that the buried interface losses induced by both the sub-microscale extended imperfections and lead-halide inhomogeneities are major roadblocks toward improvement of device performance. The losses can be considerably mitigated by the use of a passivation-molecule-assisted microstructural reconstruction, which unlocks the full potential for improving device performance. The findings open a new avenue to understanding performance losses and thus the design of new passivation strategies to remove imperfections at the top surfaces and buried interfaces of perovskite photovoltaics, resulting in substantial enhancement in device performance.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Mesoscopic modeling of transport resistances in a polymer-electrolyte fuel-cell catalyst layer: Analysis of hydrogen limiting currents

Understanding transport resistances in a polymer-electrolyte fuel cell (PEFC) catalyst layer (CL) is essential to mitigate the unexpected voltage loss when using low loadings of precious metals. In this paper, we explore through mesoscopic modeling the quantification analyses of the transport resistances in CL as derived using hydrogen-pump limiting current. Numerical treatments on the conjugated interfacial conditions at interfaces of ionomer/pore and Pt/ionomer are proposed to describe the mesoscopic transport processes of hydrogen and proton. Characterizations of the reconstructed microstructure of CL are performed. Parameter analyses on the influences of the critical transport properties such as the permeation coefficient and the dissolution and adsorption reaction rates at the surfaces of ionomer/pore and Pt/ionomer on the local transport resistance are presented. It is found that the local transport resistance is mainly originated from the diffusion resistance of the ionomer thin-film, which is more resistive than its bulk analogue with its permeation coefficient fitted to be 5.9% of the bulk one. The interfacial transport resistances and the diffusion resistance are coupled. The local transport resistance increases with I/C ratio due to thicker ionomer coated on the particles. Higher Pt/C ratio and bare carbon fraction lead to higher local transport resistance since the ionomer loading relative to Pt roughness factor decreases. The local transport resistance decreases with the porosity. Furthermore, the contribution of pores to the CL resistance, which decreases with the porosity, is comparatively small at low loadings.

25 ENERGY STORAGE↗
DOE OSTI2023

Surface Sn(IV) Hydrolysis Improves Inorganic Sn–Pb Perovskite Solar Cells

Cesium tin-lead (Sn-Pb) perovskites are exceptional for their combined capabilities in unlocking ideal bandgaps for solar cells and mitigating the stability issue faced by their hybrid organic-inorganic counterparts. But the development of high-performance solar cells using these materials is retarded by their inherent high density of detrimental Sn(IV) defects. Herein, we demonstrate a sequential surface treatment method, which entails a Sn(II) halide treatment to displace the buried Sn(IV) ions underneath the film surface, followed by a H 2 O treatment to hydrolyze the displaced Sn(IV) ions. The surface treatment induces chemical and microstructural reconstructions that significantly improve the optoelectronic properties and stability of perovskites. As a result, a power conversion efficiency of 16.79% and a T 90 stability of 958 h are achieved, topping all previously reported performance parameters for inorganic Sn-Pb PSCs. This achievement further shortens the performance gap between all-inorganic and hybrid organic- inorganic Sn-Pb PSCs with sub-1.4 eV bandgaps.

14 SOLAR ENERGY↗
DOE OSTI2020

Mesoscopic analyses of the impact of morphology and operating conditions on the transport resistances in a proton-exchange-membrane fuel-cell catalyst layer

Exploring the origins of local transport resistance and characterizing the oxygen transport resistances in the catalyst layer (RCL) are critical for cost reduction. In this paper, a comprehensive mesoscopic model for simulating coupled transport processes of oxygen and water vapor for different structural parameters under different operating conditions in reconstructed microstructures is proposed. The local transport resistance is calculated after achieving the limiting current density and the effective diffusivity of oxygen. The results demonstrate that RCL increases greatly with decreasing platinum loading (LPt) and the transport resistances in other components of the cell dominate for high-loadings. Both the reduced oxygen permeation coefficient in the ionomer thin-film and the adsorption resistance account for the origins of local transport resistance. The local transport resistance increases with the bare carbon ratio for a constant LPt and Pt/C ratio due to the decreased effective ionomer surface, and increases with the I/C ratio due to the increased ionomer thickness and decreased Knudsen diffusivity. Due to the presence of liquid water, a slight decrease followed by an increase of the local transport resistance versus relative humidity is obtained. The contribution of ionomer thin-films to RCL is more sensitive to liquid saturation compared with that of pores. This journal is

Mu, YT↗
DOE OSTI2022

FFT-based model for irradiated aggregate microstructures in concrete

The concrete biological shield of light water reactors is exposed to neutron and gamma irradiation throughout its lifetime, which results in the long-term degradation of the concrete’s mechanical properties. Under neutron irradiation, the concrete’s aggregates are subjected to radiation-induced volumetric expansion (RIVE), which strongly depends on the mineral content of the aggregate and exhibits the largest expansion in silicate-bearing minerals. In this work, the authors used the fast Fourier transform-based code Microstructure-Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) in 2D to model the expansion of five different aggregates provided by the Japan Concrete Aging Management Program (JCAMP). Comparable rock specimens were irradiated at the JEEP-II test reactor. The model uses realistic aggregate microstructure reconstruction based on high-resolution characterization images. The model accounts for anisotropic RIVE, thermal expansion, and the associated initiation and propagation of damage. The RIVE models are calibrated based on expansion data in the literature. The authors assume that damage occurs exclusively at interfaces between the particles that compose an aggregate and that these interfaces also exhibit swelling. Using a micromechanical model, the evolution of Young’s modulus with RIVE is calculated for each aggregate and compared with Russian irradiation data. The modeled linear expansion agrees well with the experimentally measured expansion. Furthermore, the model also predicts that anisotropic RIVE and thermal expansion result in an earlier onset of damage with neutron fluence than in the isotropic case.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. Here, this study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 x 200 pixels, the code can reconstruct a 300 x 300 x 300 structure in under 22 h and reconstruct a 400 x 400 x 400 structure in 43 h with eight cores.

42 ENGINEERING↗
NASA NTRS2014

Three-Dimensional Imaging and Numerical Reconstruction of Graphite/Epoxy Composite Microstructure Based on Ultra-High Resolution X-Ray Computed Tomography

A combined experimental and computational study aimed at high-resolution 3D imaging, visualization, and numerical reconstruction of fiber-reinforced polymer microstructures at the fiber length scale is presented. To this end, a sample of graphite/epoxy composite was imaged at sub-micron resolution using a 3D X-ray computed tomography microscope. Next, a novel segmentation algorithm was developed, based on concepts adopted from computer vision and multi-target tracking, to detect and estimate, with high accuracy, the position of individual fibers in a volume of the imaged composite. In the current implementation, the segmentation algorithm was based on Global Nearest Neighbor data-association architecture, a Kalman filter estimator, and several novel algorithms for virtualfiber stitching, smoothing, and overlap removal. The segmentation algorithm was used on a sub-volume of the imaged composite, detecting 508 individual fibers. The segmentation data were qualitatively compared to the tomographic data, demonstrating high accuracy of the numerical reconstruction. Moreover, the data were used to quantify a) the relative distribution of individual-fiber cross sections within the imaged sub-volume, and b) the local fiber misorientation relative to the global fiber axis. Finally, the segmentation data were converted using commercially available finite element (FE) software to generate a detailed FE mesh of the composite volume. The methodology described herein demonstrates the feasibility of realizing an FE-based, virtual-testing framework for graphite/fiber composites at the constituent level.

Czabaj, M. W.↗
DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗
DOE OSTI2022

Reconstruction of 3D Concrete Microstructures Combining High-Resolution Characterization and Convolutional Neural Network for Image Segmentation

After water, concrete is the second most used material in the world. Concrete’s forming adaptability and low-cost constituents make it a predominant material used in the construction of civil infrastructures in nuclear power plants such as concrete biological shields, containment buildings, turbine buildings, fuel handling and storage buildings, underground piping for cooling, cooling towers, and so on. Depending on environmental and operating conditions, these passive structures are subject to time-dependent phenomena that can either enhance (e.g., continued hydration) or degrade concrete’s structural performance. Unlike components such as the reactor pressure vessel and the primary circuit, concrete composition varies regionally because it is manufactured using local aggregates and cement. Hence, concrete performance metrics over time cannot be derived confidently using empirical relations. Alternatively, the specific characteristics of the local concrete constituents and their assemblage in concrete must be considered.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural Evolution of Steel During Magnetic Field-Assisted Processing

Advancing magnetic field-assisted processing, as an energy-efficient method for tailoring steel microstructures, requires a thorough understanding of how the high magnetic field impacts microstructural evolution, particularly its effect on prior austenite grain structures. The current investigation of a near-eutectoid composition, Fe-C alloy, uses electron backscatter diffraction to examine the morphology and orientation of martensite and pearlite microstructures, and to reconstruct the parent austenite microstructures present during equivalent heating under varied magnetic field strengths (0-T, 2-T, 5-T, and 9-T). It was observed that the magnetic field has a negligible effect on martensite lath/block width, slightly decreases prior austenite grain size, and increases the fraction of austenite grains with annealing twins. Additionally, the magnetic field increases the phase fraction of proeutectoid ferrite but has a negligible effect on pearlite block size and the distribution of boundary misorientation angles. No preferred texture was induced by the magnetic field, regardless of the applied field direction, in the proeutectoid ferrite phase or the martensite and prior austenite microstructures. Furthermore, the observed results contradict previous literature, and the differences are discussed.

Magnetic Materials↗
DOE OSTI2019

Stress field analysis in a stony meteorite under thermal fatigue and mechanical loadings

A micromechanical approach relying on 3D image-based finite element (FE) models, together with experimental testing, is employed to investigate the stress distribution in an L6 ordinary chondrite's microstructure subjected to thermal and mechanical loadings. An in-situ X-ray micro-computed tomography (micro-CT) setup capable of applying thermal and mechanical loads shed light on crack initiation mechanisms in chondrite samples. Micro-CT images were further used to reconstruct realistic microstructural models of the samples, which were transformed into FE models using an automated non-iterative mesh generation algorithm. FE approximation of stress fields showed that under mechanical loads, the stress often concentrates in matrix-particle interfacial regions with higher curvatures. Furthermore, this could result in the nucleation and eventually propagation of cracks in such regions. However, when the thermal cycling takes place, the stress concentrates more uniformly along with particle interfaces, resulting in their debonding from the surrounding matrix. Both observations were verified by experimental data obtained from in-situ micro-CT imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Permeability Hysteresis From Microchannels Opening During Dissolution/Reprecipitation Cycle

Abstract Permeability is a critical parameter for geological resources characterization. Its evolution with respect to porosity is particularly interesting and many research initiatives focus on deriving such relationships, to understand some hydraulic impacts of microstructure alteration. Permeability evolution from chemical reactions, for instance, can become complex as flow channels may open during rock dissolution. In this contribution, we show that this phenomenon can lead to irregular porosity‐permeability curves and permeability hysteresis after reprecipitation. Current approaches describing permeability as a simple function of porosity can therefore not capture this behavior, and we advocate instead the use of dynamic modeling for such scenarios. We demonstrate our approach by modeling a dissolution/precipitation cycle for a unit cell pore channel and quantify the process at larger scale on three different rock samples, whose microstructures are reconstructed from segmented micro‐Computerised Tomography scans.

Lesueur, Martin↗
DOE OSTI2020

Experimentally Validated Computational Modeling of Creep and Creep-Cracking for Nuclear Concrete Structures

In a Nuclear Power Plant, one of the most important components is the concrete nuclear reactor cavity, which serves both a structural and protective function as the biological radiation shield. Given that creep has been identified as a major knowledge gap in the assessment of nuclear structures (NUREG/CR-7153), this work helps to further the understanding of creep behavior of massive concrete containment structures for decades to enable safe and long-term operation of these facilities. This project has developed a robust, experimentally validated model to predict creep in nuclear concrete structures for up 60 years using short-term creep data thereby enabling a longer service life of critical facilities and early detection of structural failure. The work presented in this report is a pairing of computational and experimental methods. For the first time, the time temperature superposition (TTS) principle was successfully used to generate a uniaxial creep compliance master curve to predict mortar creep response for up to 22,500 days (nearly 60 years) at a reference temperature of 20°C. These data were used as input into finite element analysis (FEA) codes that use highly realistic random, 3D concrete microstructures from reconstructed coarse limestone aggregates. Finite element analysis performed provides the ability to quickly upscale mortar viscoelastic behavior to long-term concrete creep/relaxation data. A master creep compliance curve, constructed from the TTS principle, spanning 27 years, was used to validate two and a half decades of simulated concrete creep. Concurrently, three different simulated wall specimens were designed to mimic the behavior of post-tensioned concrete nuclear containment facility vessel walls over time as a result of concrete creep. The specimens were designed with different thicknesses, transverse and longitudinal reinforcement ratios, and level of post-tensioning stress. Each specimen contained various instrumentation to measure internal concrete temperature, concrete strain, and post-tensioning strain hourly for over 3 years. The concrete creep model developed in this project, based on the FEA concrete simulations, was applied to simulate the structural-scale experiments of prestressed concrete walls conducted in this project using the Grizzly code. These models can represent the effects of reinforcing and prestressing. Although there are some discrepancies with the experimental data, the model can predict the overall trends of the creep response in these experiments. One of these experimental models was also applied to an extended time to demonstrate how the findings from this study can be used to predict the behavior of actual structures of interest that have been in service for extended periods of time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗