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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A defect-resistant Co–Ni superalloy for 3D printing

Abstract Additive manufacturing promises a major transformation of the production of high economic value metallic materials, enabling innovative, geometrically complex designs with minimal material waste. The overarching challenge is to design alloys that are compatible with the unique additive processing conditions while maintaining material properties sufficient for the challenging environments encountered in energy, space, and nuclear applications. Here we describe a class of high strength, defect-resistant 3D printable superalloys containing approximately equal parts of Co and Ni along with Al, Cr, Ta and W that possess strengths in excess of 1.1 GPa in as-printed and post-processed forms and tensile ductilities of greater than 13% at room temperature. These alloys are amenable to crack-free 3D printing via electron beam melting (EBM) with preheat as well as selective laser melting (SLM) with limited preheat. Alloy design principles are described along with the structure and properties of EBM and SLM CoNi-base materials.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Superalloy Disk With Dual-Grain Structure Spin Tested

Advanced nickel-base disk alloys for future gas turbine engines will require greater temperature capability than current alloys, but they must also continue to deliver safe, reliable operation. An advanced, nickel-base disk alloy, designated Alloy 10, was selected for evaluation in NASA s Ultra Safe Propulsion Project. Early studies on small test specimens showed that heat treatments that produced a fine grain microstructure promoted high strength and long fatigue life in the bore of a disk, whereas heat treatments that produced a coarse grain microstructure promoted optimal creep and crack growth resistance in the rim of a disk. On the basis of these results, the optimal combination of performance and safety might be achieved by utilizing a heat-treatment technology that could produce a fine grain bore and coarse grain rim in a nickel-base disk. Alloy 10 disks that were given a dual microstructure heat treatment (DMHT) were obtained from NASA s Ultra-Efficient Engine Technology (UEET) Program for preliminary evaluation. Data on small test specimens machined from a DMHT disk were encouraging. However, the benefit of the dual grain structure on the performance and reliability of the entire disk still needed to be demonstrated. For this reason, a high temperature spin test of a DMHT disk was run at 20 000 rpm and 1500 F at the Balancing Company of Dayton, Ohio, under the direction of NASA Glenn Research Center personnel. The results of that test showed that the DMHT disk exhibited significantly lower crack growth than a disk with a fine grain microstructure. In addition, the results of these tests could be accurately predicted using a two-dimensional, axisymmetric finite element analysis of the DMHT disk. Although the first spin test demonstrated a significant performance advantage associated with the DMHT technology, a second spin test on the DMHT disk was run to determine burst margin. The disk burst in the web at a very high speed, over 39 000 rpm, in line with the predicted location and speed. Furthermore, significant growth of the disk was observed before failure, in line with predictions, clearly demonstrating the reliability and safety of the DMHT technology. Although successful spin testing in Ultra Safe's Nickel Disk Program represents a significant milestone for DMHT technology, realistic engine operation will require repeated loading of a DMHT nickel disk. For this reason, a cyclic spin test study of DMHT nickel disk technology has been proposed to start in fiscal year 2003 under NASA's Aviation Safety Program. The goal of this program will be to determine the fatigue failure mechanism in DMHT nickel disks, thereby demonstrating the reliability and safety of DMHT technology under repetitive loading conditions encountered in realistic engine operation.

Gayda, John↗
DOE OSTI2017

Designing nickel base alloys for microstructural stability through low γ-γ' interfacial energy and lattice misfit

In this paper, an extended stability alloy design strategy for multicomponent γ-γ' nickel-base alloys with near-zero lattice misfit and as low as possible interfacial energy was investigated by isothermal annealing of two experimental alloys at 900 °C for times up to 256 h. The coarsening behavior of the spherical γ' precipitates and the phase compositions determined by atom probe tomography were utilized to exploit a modified Lifshitz-Slyozov-Wagner relationship to estimate the interfacial energies. The estimated interfacial energies are much lower than predicted by a CALPHAD-based software as well as those typically reported for multicomponent γ-γ' nickel alloys. Finally, despite successfully minimizing the interfacial energy and γ-γ' lattice misfit, these factors alone were not sufficient to impart high temperature extended stability through reduced coarsening kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗
DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗
DOE OSTI2022

Evaluating the intrinsic resistance to balling of alloys: A High-throughput physics-informed and data-enabled approach

To date, the vast majority of work on metal additive manufacturing (AM) has been framed in terms of the need to tune processing conditions for a particular AM technology in order to print conventional alloys, oftentimes developed for fabrication methods other than AM. This approach overlooks the fact that historically, many engineering alloy system has been designed with a particular processing route in mind, e.g., ingot metallurgy, powder metallurgy, rapid quenching, etc. There are thus significant opportunities to design alloys specifically for AM. A key challenge is that alloy design requires performance metrics that can be optimized by exploring the alloy chemistry space. Here, we present a study in which we examine how intrinsic thermophysical properties can be used to estimate performance metrics related to the behavior of a solidifying metal droplet under AM-relevant conditions. By identifying these intrinsic properties, it is possible to directly incorporate ‘intrinsic printability’, specifically ‘intrinsic resistance to balling’, into AM-focused alloy design.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Physics-informed, empirically constrained machine learning for designing Fe-9Cr alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. It was demonstrated that the domain knowledge-based empirical constraints not only inhibit overfitting but also allow training more accurate and reliable ML models with improved transparency of the output interpretation. In this study, alloy tensile properties were interpreted with three competing virtual-microstructure models.</span>

Romanov, Vyacheslav↗
DOE OSTI2021

Design of heterogeneous structured Al alloys with wide processing window for laser-powder bed fusion additive manufacturing

Required microstructural attributes of an alloy vary with structural applications. The microstructural fine-tuning capability of laser-powder bed fusion (L-PBF) additive manufacturing (AM) enables application specific manufacture of the components. Such manufacture with L-PBF AM requires alloys that exhibit wide processing window and are amenable to multiple deformation mechanisms. However, high hot cracking susceptibility of Al alloys poses a barrier to such printability-performance synergy. In this work we show that an integration of, a) grain refinement through heterogeneous nucleation, and b) eutectic solidification, leads to crack-free parts at wide range of process parameters, microstructural heterogeneity, and hierarchy in the Al-Ni-Ti-Zr alloy. Such an integration targets hot cracking at multiple stages of solidification in L-PBF as opposed to the contemporary alloy design strategies that target hot-cracking at only specific stages of solidification. The Al-Ni-Ti-Zr alloy exhibits excellent printability and a high as-built tensile performance. Due to the wide processing window and amenability to multiple deformation mechanisms, the alloy microstructure and subsequently the performance, can be fine-tuned. Here, such strategy opens the gateway for application-specific manufacture of Al alloys with L-PBF AM and establishes a fundamental shift in current methodologies for design of these alloys for L-PBF AM.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Guided Design of Alloys

Using multiscale materials modeling, we are designing stronger alloys for the airspace applications. We focus on improving mechanical properties of materials at operating temperature. We employ basic science and fundamental theory for guided design of the next-generation alloys. Among the examples, we consider precipitated superalloys with a local phase transformation strengthening. We acknowledge funding by NASA’s Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.

multiscale↗
DOE OSTI2024

Towards the holistic design of alloys with large language models

Large language models are very effective at solving general tasks, but can also be useful in materials design and extracting and using information from the scientific literature and unstructured corpora. For instance, in the domain of alloy design and manufacturing, they can expedite the materials design process and enable the inclusion of holistic criteria.

36 MATERIALS SCIENCE↗