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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework

This report summarizes the activities performed under the data analytics Vertex in the Materials Characterization, Prediction and Control Project funded under laboratory directed research and development at Pacific Northwest National Laboratory. The data analytics Vertex developed models for associating global or local process parameters, microstructural features, and performance properties of friction-stir-processed 316L stainless steel plates. Statistical, machine learning, and deep learning models, as well as generative artificial intelligence approaches, were used to develop the associations between the process-structure-property data streams. These associations formed the basis for predicting global properties of parts manufactured under different process envelopes, providing a basis for predicting performance using data driven as well as physics-informed and physics-constrained approaches. Additionally, the associations were used to predict local process parameters and microstructural features of the product, predictive relationships that have the potential to form the basis of a control framework that could eventually modulate a friction-stir process to maintain product quality.

316L stainless steel↗
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 OSTI2021

Predicting material microstructure evolution via data-driven machine learning

The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.

machine learning (ML), materials characterization↗
DOE OSTI2026

Mechanical behaviour of additively manufactured metals

Additive manufacturing is reshaping the production of engineering components in diverse industries, such as the automotive, aerospace, defense, and biomedical sectors, by offering unprecedented design flexibility. The non-equilibrium processing conditions of additive manufacturing generate materials with unique microstructures and tailored mechanical properties that are often unattainable through conventional routes. This review focuses on recent advances in additively manufactured metals that demonstrate distinctive mechanical behaviors, including strength-ductility synergy, microstresses and gradient plasticity, fracture and fatigue resistance, and high-temperature creep performance. Here, we examine the mechanisms and micromechanical effects arising from the heterogeneous microstructures fabricated by additive manufacturing, to guide the design of a wide range of high-performance structural materials. Furthermore, we discuss critical research needs and emerging opportunities in process control, alloy design, advanced characterization, high-fidelity computational modeling, and machine learning aimed at achieving exceptional mechanical properties in additively manufactured metals.

Additive Manufacturing↗
DOE OSTI2026

Scalable Solar Fuels Production in A Reactor Train System by Thermochemical Redox Cycling of Novel Nonstoichiometric Perovskites

Hydrogen production via two-step thermochemical water splitting redox cycles using nonstoichiometric redox-active metal oxides has the potential to dramatically increase fuel production rates. At moderate-to-low water splitting temperatures, surface reaction kinetics co-limit the process. In such cases, stable and high surface area microstructures that allow exploitation of the full thermodynamic potential of the materials are essential as is tight thermal integration of the reactor module. This project’s goals were the development of novel nonstoichiometric perovskite oxides with high stability and favorable thermodynamic and kinetic properties, to optimize their microstructure for maximizing the fuel productivity, and to build a prototype reactor train system (RTS) comprising at least one reactor to meet specific performance targets: (1) capable of an in-house solar thermochemical hydrogen (STCH) productivity ≥ 12 mL g -1 for stable continuous operation ≥ 20 cycles; and (2) demonstration of scalable solar fuels production at practical solar reactor level in an industrial-scale concentrated solar tower (CST) using developed perovskites to achieve a hydrogen production rate ≥ 1 g h -1 .

08 HYDROGEN↗
DOE OSTI2025

Simultaneously Improving Process Efficiency and Mechanical Properties in Aluminum Alloys with Applied Magnetic Fields

In the aluminum heat treatment sector, large resistive batch furnaces are used for many heat treatment processes and are generally always running, operating at approximately 20–30% efficiency. The primary avenue for energy savings lies in shortening heat treatment times, which can be 24+ hours for solutionizing (450–625 °C) and aging treatments (150–250 ℃), depending on the alloy and specification requirements. The application of high magnetic fields (<9 T) has shown to significantly reduce aluminum alloy heat treatment times, while achieving enhanced mechanical properties. Here, we explore the effects of thermomagnetic processing on the heat treatment response of a commercial aluminum alloy A390.1. Heat treatment times were reduced by >80%, while achieving ~10% improvement in microhardness, and these improvements persisted after longer thermal exposures simulating use. The microstructural evolution and resulting properties will be discussed.

Kesler, Michael [ORNL] (ORCID:0000000161423128)↗
DOE OSTI2026

Oxygen Effects and Selective Oxidation of Group IV Alloying Additions in Group V-Based Refractory Multi-principal Element Alloys

Refractory multi-principal element alloys (RMPEAs) hold significant potential for advancing ultra-high-temperature technologies due to their remarkable strength retention under these conditions. However, the beneficial effects of alloying on microstructural evolution and mechanical properties are often obscured by the presence of interstitial alloying elements. The intricate interactions between substitutional alloying elements of Groups IV, V, and VI and interstitial content, whether intentional or incidental, remain poorly understood. In this study, we investigate the impact of dissolved oxygen on the microstructural evolution and stability of RMPEAs primarily composed of Group V elements, Nb and V, along with Group IV elements Ti, Zr, and Hf. We show that the strong affinity of oxygen for Group IV elements Zr and Hf leads to the internal precipitation of oxides during heat treatment, which competes with the homogenization of the as-cast dendritic microstructure. Here, we demonstrate that careful control and design of oxygen content and Group IV alloying additions are essential to minimize micro-segregation and prevent the detrimental precipitation of internal oxides. To address these challenges, we propose employing non-equilibrium solidification calculations to predict and optimize as-cast solidification structures, thereby reducing the reliance on homogenization processes.

Alloy design↗
DOE OSTI2024

Friction stir processing: A thermomechanical processing tool for high pressure die cast Al-alloys for vehicle light-weighting

This study uses friction stir processing (FSP) for thermomechanical processing of high-pressure die-casting (HPDC) to modify microstructure and improve mechanical properties. FSP is carried out on two different HPDC aluminum alloys: (a) general-purpose, high-iron, HPDC A380 alloy and (b) premium quality, low-iron HPDC Aural-5 alloy in thin wall, flat plate geometry. Subsequent mechanical testing shows ~30 % and ~65 % enhancement in yield strength and tensile ductility. In addition, FSP leads to ~10 times improvement in fatigue life for A380 alloy and ~70 % improvement in fracture toughness for Aural-5 alloy. These findings emphasize the capability of FSP to modify the microstructure of HPDC Al-alloys-based structural components so that they can demonstrate a good combination of strength, ductility, fracture toughness, and high fatigue properties for long-term durability and reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Concentric core-shell tracks and spectroscopic properties of SrTiO 3 under intense electronic excitation

A deeper understanding of the intrinsic link between the irradiation-induced microstructures and the corresponding spectroscopic properties is becoming increasingly attractive for the research fields of materials science, physics, and information technology. In this work, the structural damage response, spectroscopic features, and associated physical mechanisms of SrTiO 3 single crystals under swift heavy ion irradiation were comparatively analyzed using a combination of experimental and theoretical approaches. Corresponding to 0.11–5.00 MeV/u ion irradiation with electronic energy loss ranging from 4.0 to 29.3 keV/nm, the inelastic thermal spike calculations combined with molecular dynamics simulations are compared with the experimental observations, revealing the track fine structures (individual spherical defects with a disordered region, and discontinuous and continuous tracks consisting of an amorphous core and a disordered outer shell) and demonstrating the dominant effects of the deposition energy and lattice temperature on track damage formation and evolution; thus, two essential thresholds for defect formation of ~0.60 eV/atom and amorphous region formation of ~1.81 eV/atom were identified to better describe the concentric core-shell track structure. The enhanced nanohillock formation in the surface region is attributed to the combined action of kinetic and potential energy depositions, and the fluence dependence in regulating the hillock dimensions is also presented. The measured refractive index profiles along different crystal axes further indicate the anisotropy of irradiation-induced lattice expansion. With increasing ion fluence and damage level, the intrinsic bandgap gradually decreased, and the concentration of additional radiative recombination centers (1.81 eV, 2.31 eV, and 2.42 eV) accordingly increased in the SrTiO 3 crystal, providing the possibility of regulating related defects to achieve tunable/selective photon emission for more critical technological applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Shear deformation of pure-Cu and Cu/Nb nano-laminates using micromechanical testing

Solid phase processing by introducing shear deformation into materials can result in unique microstructure evolution and enhanced mechanical properties, especially for immiscible systems such as Cu/Nb. To better understand the correlation between microstructure and deformation behavior during shear, a dedicated testing design of stress localization at predicted sites is necessary. In this study, a specialized S-shaped specimen geometry is implemented to apply localized simple-shear loading in pure-Cu and Cu/Nb accumulative roll-bonded nanolaminates. The nanoscale microstructure and proximity of interfaces in Cu/Nb offer a ~2.8-fold increase in shear stresses than pure-Cu. In pure-Cu, the plastic instability causes shear banding and an in-plane lattice rotation. In Cu/Nb, a partial bending of the interfaces occurred, resulting in a localized lattice rotation. The adapted geometry for micro-scale specimens thus successfully captures the shear deformation at predicted sites in two distinct material systems and could potentially be a powerful technique to study the deformation mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental study of UV induced tensile properties deterioration and chemical aging of polyurea–POSS composites

Ultraviolet (UV) radiation present in natural sunlight degrades the chemical and mechanical properties of polymeric matrices in composites. Polyurea possess a unique set of chemical and mechanical properties due to its complex microstructure comprising of hard and soft segments (phases). The primary objective of the work presented here is to characterize the chemical and mechanical degradation of pure polyurea and polyurea - polyhedral oligomeric silsesquioxane (POSS) nanocomposites subjected to UV radiation exposure for 45 days. Control specimen in as-received condition (reference specimen) and UV-aged tensile test specimen are tested in the study. Analysis of Fourier transformed infrared (FTIR) spectra for UV-aged polyurea and polyurea-POSS composites reveals that the addition of POSS nanoparticles accelerate the deterioration of polyurea matrix by breaking the hard phase network and altering the mechanism of degradation for the soft phase present in polyurea microstructure. The deterioration of hard and soft segments cause a significant reduction in tensile properties of polyurea-POSS composites.

Materials Science↗
DOE OSTI2022

Impacts of annealing treatment on the microstructure of U-Mo monolithic fuel plates

The objective of this paper is to analyze the effect of different heat treatments on the fuel microstructure and texture in laboratory scale monolithic U-Mo fuel plates. Such analyses are relevant as they could possibly inform future fabrication methods for the optimization of microstructure and of material properties to ultimately improve fuel performance during irradiation. For this reason, detail characterization including Energy x-ray dispersive spectroscopy (EDS) and electron backscattered diffraction (EBSD) techniques were applied on U-Mo plates fabricated with different heat treatments to understand the influence of fabrication on fuel microstructure. Here, U-Mo fresh fuel specimens with or without annealing treatments (homogenization and stress release annealing) after rolling were analyzed in this work. In this study for the first time EBSD was systematically used to analyze the heat treatments influence on texture present in fresh fuel monolithic U-Mo fuel plates. Such changes were connected to other known microstructural and chemical changes. In this work minimization of molybdenum concentration variation and gamma phase decomposition of the U-Mo fuel core after homogenization was observed. Also, an increase in the interaction layer of the U-Mo fuel core with the Zr interlayer diffusion barrier was observed with the UZr 2 growing up to 1 µm in thickness. Such microstructural changes were aligned to the changes in texture and grain structure. Indeed, common texture observed in body-centered cubic (BCC) metal after cold rolling (e.g., α, ξ, γ fibers) were minimized by the annealing/homogenization process. Moreover, grain structure was influenced by the fabrication route with elongated grains formed in the rolled sample and equiaxed grains found after annealing. Such features are relevant since the formation of microstructural features formed during fuel fabrication can impact fuel performance in reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Thermal properties of HfN-MoW surrogate cermet fuel for nuclear thermal propulsion

Here, the thermal properties of MoW-HfN, a surrogate cermet for MoW-UN nuclear thermal propulsion fuel, were characterized over a wide range of elevated temperatures. Thermal diffusivity, coefficient of thermal expansion (CTE), and heat capacity were measured. Optical and scanning electron microscopy were performed to characterize the microstructure and draw structure–property correlations. The thermal diffusivity was obtained using the laser flash method. Diffusivity values ranged from about 0.18 cm 2 /s at 200°C to 0.15 cm 2 /s at 1800°C. The CTE was measured using push-rod dilatometry up to 1600°C, giving values between 6.0 and 9.0 μm/m. A scientific rationalization of the effective material properties is made using the rule-of-mixtures and other effective properties models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys

Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ab initio prediction of FeCr sigma (001)/FCC Fe(111) interfacial energy: Effect of interfacial doping of C, B, and N

Interfacial energy plays a crucial role for high-temperature material systems in determining their microstructure evolution and mechanical properties. Optimizing the interfacial energy through doping can improve the stability, performance, and overall functionality of these materials. Here, the present study delivers theoretical investigation on the sigma FeCr (001)//FCC Fe (111) interfacial energy using ab initio methods, focusing on the implications of interfacial doping with important interstitial elements carbon (C), boron (B), and nitrogen (N). The calculated interfacial energy without doping is 0.183J/m 2 . Upon doping, notable reductions in interfacial energy were observed. Doping with B decreased the interfacial energy to 0.046J/m 2 , with single C doping to 0.126J/m 2 , with two C doping to 0.098J/m 2 , and with a single N doping to 0.071J/m 2 . These findings not only shed light on the atomistic origin for the effect of stabilizing the microstructure of stainless steel at elevated temperatures by interstitial doping, but also present a systematic, thorough ab initio approach to predict interfacial doping properties and guide alloy design to enhance the performance and stability.

FeCr sigma phase↗
DOE OSTI2024

7 Innovations in high-rate composite manufacturing: integrating additive manufacturing with compression molding process

Advanced composites play a pivotal role in modern engineering, offering exceptional strength-to-weight ratios and tailored properties, essential for various industries. High-rate composite manufacturing techniques have rapid production capabilities, which are essential for meeting the demands of industries requiring cost-saving, efficiency, and quick turnaround times. This chapter explores the Additive Manufacturing- Compression Molding (AM-CM) system developed by Oak Ridge National Laboratory (ORNL) for advanced composites manufacturing. The AM-CM system integrates additive manufacturing with compression molding, facilitating the production of polymer composite parts with superior mechanical properties and meticulously controlled microstructures. This innovative system not only ensures precise material deposition but also operates as a fast composite manufacturing process, enhancing productivity and performance, which are needed attributes across industrial applications. Through comprehensive mechanical testing and microstructural analysis, AM-CM promotes remarkable fiber alignment and reduced porosity in composite parts compared to alternative thermoplastic high-rate composite manufacturing methods. Furthermore, AM-CM enables overmolding reinforcement using continuous carbon fiber and supports selective reinforcement through customizable toolpaths. It also facilitates the production of hybrid materials to achieve tailored mechanical properties. Future advancements in AM-CM technology aim to enhance process efficiency, broaden material versatility, and improve part performance. This involves exploring novel materials, advancing process monitoring, implementing automation technologies, and integrating artificial intelligence (AI) and machine learning (ML) for predictive modeling and real-time optimization in composite manufacturing. These developments will establish the AM-CM system as a transformative technology in composite manufacturing, driving innovation across industries.

Hassen, Ahmed [ORNL] (ORCID:0000000328521222)↗
DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effects of Heat Treatment on Microstructure, Thermal Transport and Stability in SRF Niobium

Efficient heat transfer and thermal stability are critical for preventing thermal quench and ensuring the high performance and reliability of superconducting radiofrequency (SRF) cavities, especially under high RF fields. In this article, we investigate thermal conductivity and heat capacity of high-purity cold-worked niobium used in SRF applications, with a particular focus on the effects of high-temperature heat treatments. Our measurements reveal a pronounced sensitivity of thermal transport and thermodynamic properties to the underlying microstructure such as grain size and dislocation density. The results show the critical role of controlled heat treatment processes in optimizing the thermal performance of SRF niobium, providing valuable insights for improving cavity fabrication and processing.

36 MATERIALS SCIENCE↗