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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Microstructural design and additive manufacturing and characterization of 3D orthogonal short carbon fiber/acrylonitrile-butadiene-styrene preform and composite

In contrast to conventional preforming techniques, additive manufacturing features direct and layer-by-layer fabrication, which provides viable new capabilities for the fabrication of reinforced composites. In this paper, we explore the microstructural design as well as additive manufacturing and characterization of 3D orthogonal, short carbon fiber/acrylonitrile-butadiene-styrene (ABS) preforms and composite. First, an array of 3D orthogonal preforms is designed based on topological consideration and validated by fused filament fabrication of pure ABS wire; high fidelity between models and preforms is accomplished. Then, short carbon fibers are introduced into the designed 3D orthogonal preforms as reinforcement, using a short carbon fiber/ABS wire. Lastly, the compressive behavior of a 3D orthogonal, short carbon fiber/ABS preform and that of its silicone infused composite are characterized. Finally, the preform design methodology developed in this research as well as the preliminary effort made in composite fabrication and characterization demonstrates the feasibility of additive manufacturing of 3D orthogonal preform based fiber composites.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-throughput experimentation for microstructural design in additively manufactured 316L stainless steel

In the present study, a combination of high-throughput (HT) and low-throughput (LT) techniques was used to rapidly determine the processing window and generate processing maps for Selective Laser Melting (SLM) of 316L stainless steel. The HT method includes the fabrication of hundreds of hex nut-shaped specimens, each processed with a unique combination of laser power, scanning speed, and hatch spacing. An easily removable scaffolding permitted rapid sample extraction from the base plate, thus saving machining cost and time. Hardness and immersion density measurements were used for HT characterization to identify a processing window for maximum strength and density. Within the defined processing window, a low-throughput (LT) microstructural interrogation of specimens were performed. The microstructural analysis included quantification at various length scales (i.e., grains size and morphology, texture, primary dendrite arm spacing, and melt pool geometry analysis). Microstructure-based processing maps as a function of volumetric energy density were generated. The combination of HT and LT methods produced a predictive relationship between hardness and primary dendrite arm spacing using a Hall-Petch relationship. A model is proposed to explain the dependence of microstructure on the melt pool geometry. Here, the HT method can be applied for the microstructural design of SLM-fabricated components in other alloys.

316L stainless steel↗
DOE OSTI2020

3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling

Driving range and fast charge capability of electric vehicles are heavily dependent on the 3D microstructure of lithium-ion batteries (LiBs) and substantial fundamental research is required to optimise electrode design for specific operating conditions. Here we have developed a full microstructure-resolved 3D model using a novel X-ray nano-computed tomography (CT) dual-scan superimposition technique that captures features of the carbon-binder domain. This elucidates how LiB performance is markedly affected by microstructural heterogeneities, particularly under high rate conditions. The elongated shape and wide size distribution of the active particles not only affect the lithium-ion transport but also lead to a heterogeneous current distribution and non-uniform lithiation between particles and along the through-thickness direction. Building on these insights, we propose and compare potential graded-microstructure designs for next-generation battery electrodes. To guide manufacturing of electrode architectures, in-situ X-ray CT is shown to reliably reveal the porosity and tortuosity changes with incremental calendering steps.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Towards intelligent microstructural design of Nanocomposite Materials. Lightweight, high strength structural/armor materials for service in extreme environments

The intent of this research effort is to prove the hypothesis that: Through the employment of controlled processing parameters which are based upon integrated advanced material characterization and multi-physics material modeling, bulk nanolayered composites can be designed to contain high densities of preferred interfaces that can serve as supersinks for the defects responsible for premature damage and failure.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Microstructure design for fast oxygen conduction

Research from the last decade has shown that in designing fast oxygen conducting materials for electrochemical applications has largely shifted to microstructural features, in contrast to material-bulk. In particular, understanding oxygen energetics in heterointerface materials is currently at the forefront, where interfacial tensile strain is being considered as the key parameter in lowering oxygen migration barriers. Nanocrystalline materials with high densities of grain boundaries have also gathered interest that could possibly allow leverage over excess volume at grain boundaries, providing fast oxygen diffusion channels similar to those previously observed in metals. In addition, near-interface phase transformations and misfit dislocations are other microstructural phenomenon/features that are being explored to provide faster diffusion. In this review, the current understanding on oxygen energetics, i.e., thermodynamics and kinetics, originating from these microstructural features is discussed. Moreover, our experimental observations, theoretical predictions and novel atomistic mechanisms relevant to oxygen transport are highlighted. In addition, the interaction of dopants with oxygen vacancies in the presence of these new microstructural features, and their future role in the design of future fast-ion conductors, is outlined.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Fractal nature structure reconstruction method in designing microstructure properties

Highlights: • Structure and material’s properties, within the material’s consolidation triad: Synthesis – Structure – Properties. • The grain prediction methods is based on the measurements of the solid Euclidean geometric fractal characteristics. • The intergranular contacts inside the BaTiO3 -ceramic are responsible for intergranular capacities. • The inner complex geometry of intergrain contacts imposes fractal correction factor for permeability. - Abstract: In the process of predicting materials properties, it is necessary to predict microstructures. This is important for ceramic materials, for the purpose of miniaturization and for a higher level of integration. Although the method of material structure reconstruction is for the first time used on BaTiO{sub 3}-ceramics by these authors, it can also be used for silicate, refractory and other ceramics, and, for any powder-based material. The reconstruction is based on the grain perimeter fractal analysis and the Richardson method. The main contribution of this paper is establishing the relationship between the electronic properties of BaTiO{sub 3}-ceramics and its microstructural fractal nature. Similar relations can be used for any ceramics or even any materials. The samples consolidation includes both powder pressing (cold sintering phase) and sintering in a furnace (hot sintering phase). The fractal nature characterization has a very important role from the powder phase up to the final microstructure, through which structures it directly impacts electro-physical and other ceramics properties. Thereby, the new possibilities in the microstructure characterization applications are directly introduced to an engineering system, providing the new solutions related to computer aided support. This creates new industrial production and application possibilities, especially in the process of ceramics material consolidation, now based on microstructural properties, which is providing a basis for “fractal electronics’’ development.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Synergistic Computational and Microstructural Design of Next- Generation High-Temperature Austenitic Stainless Steels

The purpose of this project was to: 1) study deformation twinning, its evolution, thermal stability, and the contribution on mechanical response of the new advanced stainless steels, especially at elevated temperatures; 2) study alumina-scale formation on the surface, as an alternative for conventional chromium oxide, that shows better oxidation resistance, through alloy design; and 3) design new generation of high temperature stainless steels that form alumina scale and have thermally stable nano-twins. The work involved few baseline alloys for investigating the twin formation under tensile loading, thermal stability of these twins, and the role of deformation twins on the mechanical response of the alloys. These baseline alloys included Hadfield Steel (Fe-13Mn-1C), 316, 316L and 316N stainless steels. Another baseline alloy was studied for alumina-scale formation investigations. Hadfield steel showed twinning but undesired second phases formed at higher temperatures. 316N stainless steel did not show signs of deformation twinning. Conventional 316 stainless steel demonstrated extensive deformation twinning at room temperature. Investigations on this alloy, both in single crystalline and polycrystalline forms, showed that deformation twins evolve in a hierarchical manner, consisting of micron–sized bundles of nano-twins. The width of nano-twins stays almost constant as the extent of strain increases, but the width and number of the bundles increase with increasing strain. A systematic thermomechanical cycling study showed that the twins were stable at temperatures as high as 900°C, after the dislocations are annealed out. Using such cycles, volume fraction of the thermally stable deformation twins were increased up to 40% in 316 stainless steel. Using computational thermodynamics and kinetics calculations, we designed two generations of advanced austenitic stainless steels. In the first generation, Alloy 1, which had been proposed as an alumina-forming austenitic stainless steel, is fully austenitic, but possesses carbides that were not dissolvable and could not be controlled. This alloy also did not show deformation twinning. Alloy 2 was designed based on alloy 1, but was not fully austenitic and had significant traces of uncontrollable precipitates as well. Alloy 3, also designed based on alloy 1, was mainly austenitic with evolution of a second phase along the grain boundaries, but also had precipitates that were not controllable. Based on the knowledge gained from the first generation of the designed steels, two more steels, called PGAA1 and PGAA2, were proposed using genetic algorithms that, based on the modelling, were supposed to exhibit alumina-scale formation. PGAA1, however, did not demonstrate a fully austenitic structure. PGAA2 could achieve a mostly austenitic structure through thermo-mechanical processing, and was then used for oxidation tests. The oxidation tests of PGAA2, with and without nitrogen impurities, along with alloy 1, suggested that PGAA2 can form alumina-scale similar to alloy 1, but N impurity will prevent formation of such a scale, probably through formation of aluminum nitrides. For the above mentioned genetic algorithm framework of alloy design, separate models were developed for specific design criteria. For prediction of alumina formation in stainless steels, a model was constructed based off of two criteria – effective valence and third element effect. These criteria capture the thermodynamics and kinetics of alumina formation in steels. To test the efficacy and robustness of this model, they were tested against alloys in the literature which had been experimentally verified to exhibit alumina formation and the predictions were in excellent agreement with the experiments. Another meta-model for prediction of twinning in unknown steel compositions was developed by an informatics based machine learning/data mining approach. Stacking Fault Energy data was captured from the literature for a large number of steel compositions and then this data was used to build a classifier to predict deformation mechanisms. Here a training set-test set based analysis was performed to test performance. The above genetic algorithm based optimization framework for alloy design was exhibited to be a successful methodology for accelerated materials discovery in the context of alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2018

Stochastic Design Optimization of Microstructural Features Using Linear Programming for Robust Design

Microstructure design can have a substantial effect on the performance of critical components in numerous aerospace applications. However, the stochastic nature of metallic microstructures leads to deviations in material properties from the design point and alters the performance of these critical components. In this paper, a novel stochastic linear programming (LP) methodology is developed for microstructure design accounting for uncertainties in desired properties. The metallic microstructure is represented using a finite element discretized form of the orientation distribution function (ODF). The inverse LP problem solves the mean values and covariance matrix of the ODFs to maximize the mean values of a property, given the statistical constraints on other properties. The highlight is an analytical uncertainty quantification model via a Gaussian distribution to model propagation of microstructural uncertainties to the properties. Examples illustrate maximization of the yield strength and magnetostriction of a galfenol alloy when constrained by uncertainties in a set of stiffness constants.

42 ENGINEERING↗
DOE OSTI2022

Harnessing structural stochasticity in the computational discovery and design of microstructures

This paper presents a deep generative model-based design methodology for tailoring the structural stochasticity of microstructures. Although numerous methods have been established for designing deterministic (periodic) or stochastic microstructures, a systematic design approach that allows the unified treatment of both deterministic and stochastic microstructure design domains has yet to be created. The proposed methodology resolves this issue by learning a unified feature space that embodies diverse structural patterns with continuously varying stochasticity levels. A highly diverse microstructure database is established to incorporate various types of deterministic and stochastic microstructure patterns. A property-aware deep generative model is proposed to learn a unified feature space of the structural characteristics, as well as the relationship between structure features and properties of interest. Autoencoder (AE), Variational Autoencoder (VAE), and Adversarial Autoencoder (AAE) are compared to understand their relative merits in the property-aware learning of the unified feature space. Microstructural designs with tailorable stochasticity and properties are obtained by searching the unified feature space. Multiple design cases are presented to demonstrate the capability of designing microstructures for structural stochasticity and properties. Furthermore, the proposed method is employed to create stochastically graded structures, which manipulate the mechanical behaviors by varying the local stochasticity of the structure.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microstructural Materials Design Via Deep Adversarial Learning Methodology

Identifying the key microstructure representations is crucial for computational materials design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for microstructural materials design. Some MCR approaches are not applicable for microstructural materials design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for microstructural materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

Engineering↗
DOE OSTI2015

TAOI B- Computational Microstructural Optimization Design Tool for High Temperature Structural Materials

The objectives of this research were two-fold: (a) develop a methodology for microstructural optimization of alloys - genetic algorithm approach for alloy microstructural optimization using theoretical models based on fundamental micro-mechanisms, and (b) develop a new computationally designed Ni-Cr alloy for coal-fired power plant applications. The broader outcome of these objectives is expected to be creation of an integrated approach for ‘structural materials by microstructural design’. Three alloy systems were considered for computational optimization and validation, (i) Ni-20Cr (wt.%) base alloy using only solid solution strengthening, (ii) nano-Y 2 O 3 containing Ni-20Cr-1.2Y 2 O 3 (wt.%) alloy for dispersion strengthening and (iii) a sub-micron Al 2 O 3 for composite strengthening, Ni-20Cr-1.2Y 2 O 3 -5.0Al 2 O 3 (wt.%). The specimens were synthesized by mechanical alloying and consolidated using spark plasma sintering. Detailed microstructural characterization was done along with initial mechanical properties to validate the computational prediction. A key target property is to have creep rate of 1x10 -9 s -1 at 100 MPa and 800°C. The initial results were quite promising and require additional quantification of strengthening contributions from dislocation-particle attractive interaction and load transfer. The observed creep rate was in order of 10 -9 s -1 for longer time creep test of Ni-20Cr -1.2Y 2 O 3 -5Al 2 O 3 , lending support to the overall approach pursued in this project.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Li-ion battery design through microstructural optimization using generative AI

Lithium-ion batteries are used across various applications, necessitating tailored cell designs to enhance performance. Optimizing electrode manufacturing parameters is a key route to achieving this, as these parameters directly influence the microstructure and performance of the cells. However, linking process parameters to performance is complex, and experimental or modeling campaigns are often slow and expensive. This study introduces a fast computational optimization framework for electrode manufacturing parameters. A generative model, trained on a small dataset of microstructural images associated with different manufacturing parameters, efficiently generates representative microstructures for new parameters. This model is integrated into a Bayesian optimization loop that includes microstructure generation, characterization, and simulation, aiming to find optimal manufacturing parameters for a particular application. Significant improvement in the energy density of a 4680 cell is achieved through bespoke cell design, highlighting the importance of cell-scale normalization. The framework’s modularity allows its application to various advanced materials manufacturing scenarios.

batteries↗