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Results for “Integrated Computational Material and Engineering (ICME)”

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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Fundamental Creep Behavior Model of Gr.91 Alloy by ICME Approach (Final Report)

The current project mainly focused on the fundamental creep cracking mechanism of the Grade 91 system during the operation conditions in the advanced technologies FE power plants and build the link among Composition-Processing Parameters-Phase Stability-Microstructure-Creep Resistance. A model based on computational thermodynamics and diffusion kinetics will be developed to provide guidance on how to improve the creep resistance of the alloy system. The PI mainly investigate the Gr.91 base alloy and weldment with the Integrated Computational Materials Engineering (ICME) approach. The long-term goal of the proposed program is to develop a model for creep and fatigue resistant alloys with different elemental systems, compositions, and processing parameters of weldment and heat-treatment. In addition, new creep and fatigue resistant alloys are to be designed based on the predictions from the model. The computational as well as experimental results include three sections: 1) The compositional optimization and secondary phases evaluation regarding the creep resistance in Grade 91 steel through the CALPHAD approach. In this section, the formation of the critical secondary phases, M23C6, MX and Z-phase were well predicted based on many conditions like the temperature as well as compositions. Meanwhile, the role of different alloying elements was also considered as they will directly affect the formation of the secondary phases mentioned above based on the compositional evolution of the alloying elements. 2) An investigation of Creep Resistance in Grade 91 Steel through Computational Thermodynamics. We systematically considered the evolution of the four critical temperatures, Ac3, Ac1, the threshold of M23C6 and Z-phase, on basis of the computational approach. Meanwhile, the equilibrium cooling as well as Scheil simulations were also considered to perform further predictions based on the different cooling rates. 3) Creep lifetime and microstructural evolution of the Grade 91 steel. In this part, mainly experimental approach was performed on the real creep test from the beginning of the commercial Gr.91 alloys. The alloys were normalized, tempered, welded (Gleeble) and PWHT and finally subjected to the creep lifetime analyses. Finally, all the samples after each process were further characterized based on the OM, XRD and SEM technique to draw final conclusions.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Development and Integration of Predictive Models for Manufacturing and Structural Performance of Carbon Fiber Composites in Automotive Applications (Final Report)

The goal of this project is to develop integrated, state-of-the-art, computational modeling tools based on an integrated computational materials engineering (ICME) methodology that are critically needed to enable structural carbon fiber (CF) applications in automobiles. These tools are designed to predict the manufacturing and structural performance of CF composites, including stochastic effects. During the first phase of the project, the manufacturing and structural performance tools, including a stochastic driver, were developed, calibrated, and validated against coupon and component level test results. For this project, from the manufacturing side, the development efforts were focused on high-pressure resin transfer molding (HP-RTM), a potentially game-changing manufacturing technology that is capable of reaching the 3-5-minute cycle time that is necessary for volume manufacturing of composites. On the structural performance side, the crashworthiness of CF structures was studied. Accordingly, computational tools were developed and validated. The resulting differences between the numerical predictions and the experimental results in the first phase of the project were required to be less than 15%. During the second phase of the project, the manufacturing and performance tools were integrated by mapping the manufacturing outcomes (e.g., fiber angles, residual stresses, degree of cure, and defects) into the structural models. Also, using the tools developed in this project, an automotive assembly currently manufactured in steel (2016 GM Malibu) was redesigned to be manufactured using advanced CF composites with the objective of comparing the performance of the CF assembly with the developed ICME model predictions. The weight, performance and cost of the CF assembly were compared with the baseline steel assembly as well. The CF underbody design was shown to be 30% lighter than the corresponding steel design. The CF underbody assembly withstood the side pole impact with less than half the intrusion for the steel assembly. Excellent correlations within 10% for both peak impact load and intrusion were observed between the numerical predictions and experimental results. Based on the cost models developed in the project, the cost increase per Kg saved was determined to be $22. The project team has prepared a total of 28 publications in various journals and conferences and broadly communicated the project results for the benefit of the composite industry. The project team has also prepared a total of 10 patent applications that were submitted to the US Patent and Trademark office.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗
DOE OSTI2020

Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Finally, challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗
DOE OSTI2023

Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this article, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. In conclusion, our numerical results show that MLMC can accelerate UQ tasks around 2.23x, compared to the classical Monte Carlo (MC) method, which is widely known as ensemble average in the CPFEM literature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bayesian optimization with active learning of design constraints using an entropy-based approach

Abstract The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A critical review on additive manufacturing of refractory alloys from a data analytics perspective- beyond nickel-based superalloys

Refractory alloys (RAs) are promising materials due to their exceptional physicochemical properties, but most research remains at the laboratory scale. For broader adoption, advancements in manufacturing are essential. Because their high stability makes conventional methods like machining and casting difficult, additive manufacturing (AM) is emerging as an effective approach for fabricating refractory alloy components. However, AM's repeated non-equilibrium thermal cycles introduce undesired features (e.g. defects, anisotropic microstructures, and residual stresses), which are magnified due to RAs’ unique properties. This paper comprehensively reviews the state-of-the-art methods of AM for refractory alloys. It explores data analytics techniques to establish design rules based on multi-fidelity experimental and computational methods. Furthermore, it investigates integrated, collaborative efforts to harmonise standalone databases, information, knowledge, and predictive models at multi-physics, multi-stage, and multi-scale. Unlike the existing literature that focuses primarily on material systems or process fundamentals, this work provides an integrated perspective on AM of refractory alloys from a data analytics standpoint, highlighting the roles of integrated computational materials engineering (ICME), verification, validation, and uncertainty quantification (VV&UQ), and digital twin-driven qualification in overcoming data scarcity and accelerating rapid qualification.

Additive manufacturing↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
DOE OSTI2021

USAMP Low-Cost Mg Sheet Component Development and Demonstration Project

The overarching objective of this USAMP project was to develop and demonstrate door panels made from magnesium (Mg) sheet with a cost penalty over conventional steel stampings of no more than $\$5.50$/kg saved. The technical approach integrated experiments with advanced computational tools based on Integrated Computational Materials Engineering (ICME) methods to develop new alloy chemistries and their thermomechanical processing that promise improved formability and lower forming temperatures. A penultimate task before finally forming the stampings was to incorporate actual microstructure into models that would enable formability simulations. This approach would, for the first time, account for individual magnesium grains moving in an anisotropic fashion unlike that for aluminum or steel that have isotropic properties upon which the current simulation tools are based. In separate activities, new coatings and lubricants to facilitate forming and improved corrosion protection and joining strategies, were developed to ensure that the door could be produced with stated product requirements. A technical cost model, which included parts production, assembly, and paint for a door specifically designed for Mg sheet, showed the cost penalty to be between $\$4.26$ to $\$6.60$/kg saved, which enveloped the project’s cost targets. The cost of the coated Mg sheet was identified as the key driver for the cost penalty. The mass of the Mg-intensive door was 7.9 kg, which was 54% less than the baseline steel door.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Low-Cost Magnesium Alloy Sheet Component Development and Demonstration Project

The overarching objective of this USAMP project was to develop and demonstrate door panels made from magnesium (Mg) sheet with a cost penalty over conventional steel stampings of no more than $5.50/kg saved. The technical approach integrated experiments with advanced computational tools based on Integrated Computational Materials Engineering (ICME) methods to develop new alloy chemistries and their thermomechanical processing that promise improved formability and lower forming temperatures. A penultimate task before finally forming the stampings was to incorporate actual microstructure into models that would enable formability simulations. This approach would, for the first time, account for individual magnesium grains moving in an anisotropic fashion unlike that for aluminum or steel that have isotropic properties upon which the current simulation tools are based. In separate activities, new coatings and lubricants to facilitate forming and improved corrosion protection and joining strategies, were developed to ensure that the door could be produced with stated product requirements. A technical cost model, which included parts production, assembly, and paint for a door specifically designed for Mg sheet, showed the cost penalty to be between 4.26 USD to 6.60 USD/kg saved, which enveloped the project’s cost targets. The cost of the coated Mg sheet was identified as the key driver for the cost penalty. The mass of the Mg-intensive door was 7.9 kg, which was 54% less than the baseline steel door.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗
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 OSTI2022

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

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↗