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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

I.3.4.1.1 Overview of Advanced Characterization Within the Powertrain Materials Program (Task 4A1) (Oak Ridge National Laboratory); (Task 4A2) (Argonne National Laboratory); and (Task 4A3) (Pacific Northwest National Laboratory)

This report describes the activities performed during the third year of Thrust 4A, “Advanced Characterization,” within the DOE-EERE VTO PMCP. The goal of the PMCP, which was launched in October 2018, has been to accelerate design, development, demonstration, and deployment of new, cost-effective advanced alloy solutions via a modern ICME approach. The properties of these new materials are targeted to enable improvements in engine efficiency, lightweighting, and durability enhancement over the full range of on-road vehicle classes (e.g., Classes 1-8), including range extenders for future electric HD freight vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Fabrication and Testing of Solid-Solution Strengthened Corrosion Resistant Alloys For Service in Molten Fluoride Environments

The demand for higher system thermal efficiencies requires the operation of power generation cycles and heat conversion systems at progressively higher temperatures. As the system operating temperature increases, existing materials may not provide adequate mechanical properties or environmental compatibility or both. There is an increasing commercial interest in the development and deployment of liquid-fueled Molten Salt Reactors (MSRs). Hastelloy®N, the highest performing candidate MSR structural alloy, is not capable of operations at temperatures above 700°C, thus limiting the performance of these systems. Using an Integrated Computational Materials Engineering (ICME)-approach and small laboratory scale heats, ORNL developed a class of patented alloys covered by U.S. Patent 9, 435, 011 B2, “Creep-resistant, Cobalt-free alloys for high temperature, liquid-salt heat exchanger systems,” similar to Hastelloy®N in that they are primarily solid solution strengthened. In contrast to precipitation strengthened alloys, the microstructure of solid solution alloys and hence the high temperature mechanical properties are stable for extended periods of time allowing long reactor operating life. The new alloys have shown to possess good resistance to liquid fluorides at temperatures up to 850°C and have significantly improved creep properties when compared to Hastelloy®N. The purpose of the CRADA project was for ORNL to collaborate with Haynes International- a materials producer, MetalTek International- a foundry, and Kairos Power – an advanced reactor developer – to scale-up selected alloys, evaluate their properties, and identify one solid solution strengthened alloy that can meet the property requirements for the reactor being developed by Kairos Power and other similar liquid fluoride-salt cooled reactors. As part of the project, eight alloys were down-selected and fabricated in larger industrial scale heats by Haynes International. Resistance to molten salt was evaluated in flowing FLiNaK and FLiBe by Kairos Power using their Rotating Cage Loop (RCL) system. Accounting for iron deposition during these tests, the new alloys displayed very low net mass change showing excellent corrosion performance in molten salt. Creep properties evaluated at ORNL were found to be better than that of Hastelloy®N and 316 stainless steel. Long-term stabilities of the alloys evaluated by Haynes International showed that these alloys have excellent thermal stability in the temperature range 704.4-815.6°C, with the change in strength and ductility being less than 10-15% after a 4000-hour exposure at 815.6°C. Autogenously Gas Tungsten Arc Welding (GTAW) welded samples showed less than 10% change in yield strength / ultimate tensile strength / total elongation compared to the basemetal, indicating that the alloys have excellent weldability. Three parts were successfully investment-cast using one alloy with very little voiding showing feasibility of fabricating parts using the casting process. This project enabled extensive interaction between the material producer Haynes International, casting supplier MetalTek, and reactor developer Kairos Power. This facilitated testing of materials and components produced using the newly developed alloys by the end-user. This allowed the generation of critical dataset required for down-selection of a few promising alloys for further development. This data is also currently being shared with other reactor designers for them to evaluate the suitability of this alloy for their reactor design. The availability of this alloy will ultimately enable the design and development and deployment of MSRs with increased temperature of operation and thus, improved efficiencies.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Fabrication and Testing of Solid-Solution Strengthened Corrosion Resistant Alloys For Service in Molten Fluoride Environments

The demand for higher system thermal efficiencies requires the operation of power generation cycles and heat conversion systems at progressively higher temperatures. As the system operating temperature increases, existing materials may not provide adequate mechanical properties or environmental compatibility or both. There is an increasing commercial interest in the development and deployment of liquid-fueled Molten Salt Reactors (MSRs). Hastelloy®N, the highest performing candidate MSR structural alloy, is not capable of operations at temperatures above 700°C, thus limiting the performance of these systems. Using an Integrated Computational Materials Engineering (ICME)-approach and small laboratory scale heats, ORNL developed a class of patented alloys covered by U.S. Patent 9,435,011 B2, “Creep-resistant, Cobalt-free alloys for high temperature, liquid-salt heat exchanger systems,” similar to Hastelloy®N in that they are primarily solid solution strengthened. In contrast to precipitation strengthened alloys, the microstructure of solid solution alloys and hence the high temperature mechanical properties are stable for extended periods of time allowing long reactor operating life. The new alloys have shown to possess good resistance to liquid fluorides at temperatures up to 850°C and have significantly improved creep properties when compared to Hastelloy®N. The purpose of the CRADA project was for ORNL to collaborate with Haynes International- a materials producer, MetalTek International- a foundry, and Kairos Power – an advanced reactor developer – to scale-up selected alloys, evaluate their properties, and identify one solid solution strengthened alloy that can meet the property requirements for the reactor being developed by Kairos Power and other similar liquid fluoride-salt cooled reactors. As part of the project, eight alloys were down-selected and fabricated in larger industrial scale heats by Haynes International. Resistance to molten salt was evaluated in flowing FLiNaK and FLiBe by Kairos Power using their Rotating Cage Loop (RCL) system. Accounting for iron deposition during these tests, the new alloys displayed very low net mass change showing excellent corrosion performance in molten salt. Creep properties evaluated at ORNL were found to be better than that of Hastelloy®N and 316 stainless steel. Long-term stabilities of the alloys evaluated by Haynes International showed that these alloys have excellent thermal stability in the temperature range 704.4-815.6°C, with the change in strength and ductility being less than 10-15% after a 4000-hour exposure at 815.6°C. Autogenously Gas Tungsten Arc Welding (GTAW) welded samples showed less than 10% change in yield strength / ultimate tensile strength / total elongation compared to the basemetal, indicating that the alloys have excellent weldability. Three parts were successfully investment-cast using one alloy with very little voiding showing feasibility of fabricating parts using the casting process. This project enabled extensive interaction between the material producer Haynes International, casting supplier MetalTek, and reactor developer Kairos Power. This facilitated testing of materials and components produced using the newly developed alloys by the end-user. This allowed the generation of critical dataset required for down-selection of a few promising alloys for further development. This data is also currently being shared with other reactor designers for them to evaluate the suitability of this alloy for their reactor design. The availability of this alloy will ultimately enable the design and development and deployment of MSRs with increased temperature of operation and thus, improved efficiencies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Multiscale Inelastic Internal State Variable Corrosion Model

We present a corrosion internal state variable (ISV) damage model based upon the integrated computational materials engineering (ICME) hierarchical multiscale paradigm. Structure–property experiments for magnesium alloys were used where the only inputs were the volume fractions of each element of the periodic table. This macroscale ISV corrosion model finds its basis in Horstemeyer’s mechanical damage model, which includes three separate ISVs for damage nucleation, growth, and coalescence, as well as Walton’s inclusion of corrosion, which introduces five new ISVs for pit nucleation, growth, and coalescence, along with general corrosion and intergranular corrosion. While Walton’s corrosion ISVs are phenomenological in nature, herein we develop a multiscale physical basis for the corrosion ISVs. The parameters for the macroscale corrosion ISVs were garnered from the mesoscale Butler–Volmer equations. Pure magnesium with differing amounts of aluminum were used in corrosion tests to exemplify the different pitting, general corrosion, and intergranular corrosion rates, and the macroscale ISV model was calibrated with said data, in which the only inputs to the model are the volume percentages of the elements magnesium and aluminum. Although magnesium alloys were used to motivate and calibrate the model, the model is abstract enough to possibly capture other material systems as well.

Chemistry↗
DOE OSTI2022

The Evolution of AGN Activity in Brightest Cluster Galaxies

We present the results of an analysis of Wide-field Infrared Survey Explorer (WISE) observations of the full 2500 deg 2 South Pole Telescope (SPT)-Sunyaev–Zel'dovich cluster sample. We describe a process for identifying active galactic nuclei (AGN) in brightest cluster galaxies (BCGs) based on WISE mid-IR color and redshift. Applying this technique to the BCGs of the SPT-SZ sample, we calculate the AGN-hosting BCG fraction, which is defined as the fraction of BCGs hosting bright central AGNs over all possible BCGs. Assuming an evolving single-burst stellar population model, we find statistically significant evidence (>99.9%) for a mid-IR excess at high redshift compared to low redshift, suggesting that the fraction of AGN-hosting BCGs increases with redshift over the range of 0 < z < 1.3. The best-fit redshift trend of the AGN-hosting BCG fraction has the form (1 + z) 4.1±1.0 . These results are consistent with previous studies in galaxy clusters as well as as in field galaxies. One way to explain this result is that member galaxies at high redshift tend to have more cold gas. While BCGs in nearby galaxy clusters grow mostly by dry mergers with cluster members, leading to no increase in AGN activity, BCGs at high redshift could primarily merge with gas-rich satellites, providing fuel for feeding AGNs. If this observed increase in AGN activity is linked to gas-rich mergers rather than ICM cooling, we would expect to see an increase in scatter in the P cav versus L cool relation at z > 1. Last, this work confirms that the runaway cooling phase, as predicted by the classical cooling-flow model, in the Phoenix cluster is extremely rare and most BCGs have low (relative to Eddington) black hole accretion rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Development of creep resistant ferritic-martensitic steels for operation at 650°C

Creep resistant ferritic-martensitic steels capable of operating at 650°C have long been under development for electricity generation. This presentation will showcase the design and development of alloys CPJ-7 and JMP with approximately 420,000 h of in-house creep testing, the equivalent of ~50 years of cumulative creep tests. Approximately fifty different alloy compositions were manufactured as part of an ICME approach. CPJ-7 present superior creep properties compared to COST alloys for steam turbine and P91/92 for boiler applications. The prolonged creep life was attributed to slowing down the destabilization of fine precipitates at 650°C. The JMP steels were designed with higher Co for solid solution strengthening and increased W for matrix strength and stability. The creep life compared to CPJ-7 increased between 118 and 150% at 650°C depending on the stress. On a Larson-Miller plot, the performance of the JMP steels surpasses that of MARBN steel. Composition-microstructure-properties relationships will be discussed.

Detrois, Martin↗
DOE OSTI2025

Microscopic insights into the solvation of polyethylene glycol chains in water: A machine learning potential approach

Polyethylene glycol (PEG) is a structurally simple, nontoxic, and water-soluble polymer widely utilized in medical and pharmaceutical applications. Notably, when a PEG chain is immersed in water, the surrounding water molecules play a key role in driving conformational changes of this macromolecule. In this study, we explore the solvation behavior of PEG under mechanical strain using molecular dynamics simulations, with an interatomic potential obtained from machine learning. Our focus is on the transition from the favored coil-like conformation to an extended one under external force. Through analyses of radial distribution functions, hydrogen bonding, and solvation dynamics, we uncover how mechanical stretching influences the local hydration environment. Furthermore, we disentangle the enthalpic and entropic contributions to the conformational stability of PEG in water. Surprisingly, our neural network potential model identifies dewetting of PEG C-atoms, and not water H-bonding with PEG O-atoms, as the main enthalpic driving force for the coiling of PEG in water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

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

Thermodynamics and kinetics of precipitation and austenite reversion during aging of Ti-free grade 300 maraging steel manufactured by laser powder bed fusion (LPBF)

Austenite reversion and intermetallic precipitation are two key phase transformations in maraging steels which determine the strength and ductility. Usually, an optimum aging temperature/time is determined using trial and error approach with inputs from thermodynamic databases and a-posteriori microscopy/hardness evaluations. In this study, a thermo-kinetic model is developed to predict the precipitation and austenite reversion kinetics during various post-fabrication heat treatments in a Ti-free variant of grade 300 maraging steel manufactured using laser powder bed fusion. The thermo-kinetic model was based on the principles of Scheil solidification and the classical nucleation theory. The predictions from the thermo-kinetic model were verified using suitable microscopy techniques, and the results from the thermo-kinetic model agree well with the experimental measurements. Overall, results highlight the fact that an Integrated Computational Materials Engineering approach can be used to predict the precipitation and austenite reversion kinetics, and be used as an input for an efficient design of heat treatments for achieving optimum mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

We report Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and exploit materials design spaces. To date, most BO approaches to materials design have focused on the materials discovery problem as if it were a single expensive-to-query ‘black box’ in which the target is to optimize a single objective (i.e., material property or performance metric). Also, such approaches tend to be constraint agnostic. Here, we present a novel multi-information BO framework capable of actively learning materials design as a multiple objectives and constraints problem. We demonstrate this framework by optimally exploring a Refractory Multi-Principal-Element Alloy (MPEA) space, here specifically, the system Mo-Nb-Ti-V-W. The MPEAs are explored to optimize two density-functional theory (DFT) derived ductility indicators (Pugh’s Ratio and Cauchy pressure) while learning design constraints relevant to the manufacturing of high-temperature gas-turbine components. Alloys in the BO Pareto-front are analyzed using DFT to gain an insight into fundamental atomic and electronic underpinning for their superior performance, as evaluated within this framework.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An integrated modeling framework with open architecture for phase field simulation of multi-component alloys

An integrated modeling framework (PanPhaseField) has been developed, which enables a direct and fast coupling between CALPHAD calculations and large-scale phase field simulations for multi-component alloys. Further, it adopts an open architecture allowing for integration of user-defined phase field models in a plug-and-play manner by taking full advantage of the user-friendly graphical interface of Pandat software. The developed modeling platform becomes an enabling tool that can be used to simulate the evolution of spatially varying microstructures of industrial complex alloys for various engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure-process relationships in monolithic U-10Mo fuel foil single-pass rolling: A parametric simulation study

In this work, a previously validated coupling of Kinetic Monte Carlo (KMC) Potts Model and finite element method (FEM) simulations was implemented to investigate the effects of microstructural features in as-cast and homogenized monolithic U-10Mo foils on the emergent microstructure after rolling and reheating. Parameters that could potentially affect recrystallization behavior of the rolled U-10Mo foils were considered: grain size distribution, uranium carbide (UC) size distribution, UC volume fraction, spatial distribution of UC, and rolling reduction magnitude. Grain structure and the magnitude of rolling reduction have the strongest influence on recrystallization kinetics and the fabricated grain size distribution. The UC distribution had only a weak effect on the recrystallization kinetics and final microstructures. While particle-stimulated nucleation (PSN) occurred in simulation more frequently as grain size increased, its incidence did not appear to considerably affect the recrystallization kinetics or grain size distribution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Incorporating dynamic recrystallization into a crystal plasticity model for high-temperature deformation of Ti-6Al-4V

During hot deformation of (α + β) titanium alloys, the simultaneous action of strain and temperature in the (α + β) regime facilitates dynamic recovery, dynamic recrystallization (DRX), and phase transformations via non-equilibrium paths. DRX is manifested in the form of fine recrystallized α or β grains. Here, in the present study, a two-phase crystal plasticity finite element framework (CP-DRX) has been developed which incorporates DRX kinetics into the crystal plasticity (CP) model to predict the flow characteristics of Ti-6Al-4V alloys during thermo-mechanical processing. The CP slip system parameters, as well as elastic properties from both α and β phases of Ti, are calibrated for different strain rate conditions. An EBSD-informed two-phase microstructure representation has been utilized in the CP-DRX framework to explore the time-dependent evolution of DRX microstructure and crystal orientation for different strain rate conditions. The proposed CP-DRX can capture the evolution of crystal orientation and plastic flow stress–strain response of polycrystalline Ti-6Al-4V during the deformation process. Furthermore, the proposed model is able to capture the softening behavior, observed in average stress–strain response from experiments performed using a Gleeble thermomechanical simulator and predict the recrystallization texture.

(α+ β)-Titanium (Ti) Alloys↗
DOE OSTI2023

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. Here, this work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

36 MATERIALS SCIENCE↗