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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Energy landscape in Ni-Co-Cr and related alloys

Among multi-principal element alloys, the NiCoCr middle-entropy alloy has an outstanding combination of strength and ductility at both low and elevated temperatures. Equiatomic NiCoCr is a single-phase alloy with the face centered cubic (fcc) crystal structure. A low stacking fault energy in the fcc matrix is a cause of a relatively low creep in this alloy. The hexagonal close-packed (hcp) structure differs from the fcc by a stacking of atomic layers. The energy difference between the hcp and fcc structures is known to correlate with the stacking fault energy in the fcc phase. We compute formation and relative structural energies versus composition in the Ni-Co-Cr ternary and related quaternary systems, discuss possibilities of compositional adjustments, and compare theoretical predictions with experiment. We acknowledge funding by NASA’s Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.

Multiscale↗
NASA NTRS2015

Methods for Fabricating Gradient Alloy Articles with Multi-Functional Properties

Systems and methods for fabricating multi-functional articles comprised of additively formed gradient materials are provided. The fabrication of multi-functional articles using the additive deposition of gradient alloys represents a paradigm shift from the traditional way that metal alloys and metal/metal alloy parts are fabricated. Since a gradient alloy that transitions from one metal to a different metal cannot be fabricated through any conventional metallurgy techniques, the technique presents many applications. Moreover, the embodiments described identify a broad range of properties and applications.

Hofmann, Douglas C.↗
DOE OSTI2020

Dynamic re-equilibration controlled multi-step transformations in (Mn, Fe) 2 (P, Si) alloys

In (Mn, Fe) 2 (P, Si) alloys crystallographic first-order phase transformations enable strong coupling of magnetic and entropic properties, potentially leading to high-efficiency energy generation and refrigeration applications. Although hysteresis losses that limit these applications can be reduced through careful control of alloy composition, compositional tuning can also unfavorably influence transformation temperatures and magnetocaloric coupling strength. Hence, exploration of additional processing variables enabling independent control of transformation properties is crucial. In this work, we investigate the role of thermal history as an additional processing variable, exploiting thermally-activated mechanisms to control properties of non-diffusive transformations in (Mn, Fe) 2 (P, Si) alloys. In so doing, we report an unusual transformation-splitting phenomenon following annealing at intermediate times, where a single well-defined magneto-structural transformation evolves towards a multi-step transformation with individual steps occurring at multiple distinct temperatures. Here, on longer annealing at the same temperatures, single-step transformation behavior is recovered. Through additional magnetic and crystallographic characterization, we show that the thermal history-controlled multi-step behavior results from sluggish thermally-activated diffusion. The two-step transformation corresponds to non-equilibrium bimodal composition distributions in the transforming phase, and these develop through a dynamic re-equilibration process as the alloy passes relatively slowly between different thermal equilibria. Together, these results suggest that thermal history primarily controls the transformation properties of (Mn, Fe) 2 (P, Si) alloys indirectly through the composition of one or more transforming phases. Additional investigations are needed to develop thermal history processing for decoupling hysteresis control from other transformation properties.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Ordering in liquid and its heredity impact on phase transformation of Mg-Al-Ca alloys

It is a long-sought goal to achieve desired mechanical properties through tailoring phase formation in alloys, especially for complicated multi-phase alloys. In fact, unveiling nucleation of competitive crystalline phases during solidification hinges on the nature of liquid. Here we employ ab initio molecular dynamics simulations (AIMD) to reveal liquid configuration of the Mg-Al-Ca alloys and explore its effect on the transformation of Ca-containing Laves phase from Al 2 Ca to Mg 2 Ca with increasing Ca/Al ratio (r Ca/Al ). There is structural similarity between liquid and crystalline phase in terms of the local arrangement environment, and the connection schemes of polyhedras. The forming signature of Mg 2 Ca, as hinted by the topological and chemical short-range order originating from liquid, ascends monotonically with increasing r Ca/Al . However, Al 2 Ca crystal-like order increase at first and then decrease at the crossover of r Ca/Al = 0.74, corresponding to experimental composition of phase transition from Al 2 Ca to Mg 2 Ca. The origin of phase transformation across different compositions lies in the dense packing of atomic configurations and preferential bonding of chemical species in both liquid and solid. The present finding provides a feasible scenario for manipulating phase formation to achieve high performance alloys by tailoring the crystal-like order in liquid.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accurate prediction of short-range order and its effect on thermodynamic, structural, and electronic properties of disordered alloys: exemplified in archetypical Cu 3 Au

Electronic-structure methods based on density-functional theory (DFT) were used to quantify the effect of chemical short-range order (SRO) on thermodynamic, structural, and electronic properties of archetypal face-centered-cubic (fcc) Cu3Au alloy. We showed that SRO can be tuned to alter bonding and lattice dynamics (i.e., phonons) and detail how these properties are changed with SRO. Thermodynamically favorable SRO significantly improved the phase stability of fcc Cu3Au from -0.0343 eV-atom -1 to –0.0682 eV-atom -1 . We used our DFT-based linear-response theory to predict SRO and its electronic origin, and accurately estimate the observed transition temperature, ordering instability (L1 2 ), and Warren-Cowley SRO parameters, in agreement with experiments. The accurate prediction of real-space SRO gives an edge over computationally and resource intensive approaches such as monte-carlo methods or experiments, which will enable large scale molecular dynamic simulations by providing supercells with optimized SRO. Here we also analyzed phonon dispersion and estimated the vibrational entropy change (from 9kB at 300 K to 6kB at 100 K) in fcc Cu3Au. We established from SRO analysis that exclusion of chemical interactions may lead to a skewed view of true properties in chemically complex alloys. The first-principles methods described in this work are generally applicable to any arbitrary solid-solution alloys, including multi-principal-element alloys, therefore, holds promise for designing technologically useful materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Down-selection of Innovative Fusion Materials

The goal of this proposal is to develop and fundamentally understand the microstructure of refractory multi-component alloys (MCA) as fusion-relevant plasma facing and structural materials, a step to down-select innovative fusion materials prior to the process of advancing their technology readiness level (TRL). Developing materials is paramount to enable fusion as energy source since no existing material is capable of coping with such extreme conditions. We will aim at understanding the role of chemistry and microstructure in these complex multi-component alloys systematically comparing their performance with pure tungsten materials in terms of mechanical properties and manufacturability. Such understanding will open opportunities to optimize MCAs for fusion applications and has a potential of attracting industry partnership.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

First-principles prediction of incipient order in arbitrary high-entropy alloys: exemplified in Ti 0.25 CrFeNiAl

High-temperature disordered multi-component alloys, including high-entropy alloys, experience either segregation or partially-ordered phases to reach low-temperature phases. For Ti 0.25 CrFeNiAl x (0 ≤ x ≤ 1), experiments suggest a partially-ordered B2 phase, whereas CALculation of PHAse Diagrams (CALPHAD) predicts a region of L21+B2 coexistence. We employ first-principles KKR-CPA to assess stability of phases with arbitrary order and a KKR-CPA linear-response theory to predict atomic short-range order (SRO) in the disorder phase that reveals the competing long-range ordered (LRO) phases in a given Bravais lattice. The favorable SRO provides a specific concentration-waves (site occupation probabilities and partially-ordered unit cells) and estimated energy gains that can then be assess directly by KKR-CPA formation enthalpies. Our results are in good agreement with experiments and CALPHAD in Al-poor regions (x ≤ 0.75) and with CALPHAD in Al-rich region (1≥ x >0.75). Our first-principles KKR-CPA and SRO-based concentration-wave analysis is shown to be a powerful and fast method to assess competing LRO phases in complex solid-solution alloys, and our results suggests more careful experiments in Al-rich region are needed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of Predictive Model for Accurate Rupture Time from Multi-Axial Creep in Alloy 709 with Physics-Based Simulations

A physics-based model is developed to predict multiaxial creep behavior in Alloy 709 (A709), an advanced austenitic stainless steel intended for high-temperature applications such as Sodium Fast Reactors (SFRs). Compared to conventional stainless steels like 316H, A709 offers superior high-temperature performance; however, comprehensive data on its multiaxial creep response remain limited. To address this gap, a crystal plasticity finite element (CPFE) framework is used to simulate the deformation and failure mechanisms of A709 under multiaxial loading conditions. The model incorporates an extended Hu-Cocks dislocation creep formulation that accounts for precipitation effects, along with the Sham–Needleman model to capture grain boundary cavitation-driven failure. These advanced constitutive models enable a detailed understanding of the interplay between microstructural evolution and macroscopic creep response. Furthermore, the study evaluates the predictive accuracy of various effective stress measures in estimating creep rupture life, leveraging simulated multiaxial creep data. The findings provide critical insights into the applicability of different stress measures for engineering design and life prediction of A709 components operating under complex loading conditions. This work contributes to improving the reliability of high-temperature structural components by advancing predictive modeling capabilities for advanced austenitic steels.

Alloy 709↗
DOE OSTI2021

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Surmounting the interband threshold limit by the hot electron excitation of multi-metallic plasmonic AgAuCu NPs for UV photodetector application

We report multi-metallic alloy NPs composed of various elemental compositions can surmount the interband threshold limit of mono-metallic NPs and thus can offer a promising route to boost up the performance limit of conventional ultraviolet photodetectors (PDs). In this work, a hybrid UV-PD configuration has been demonstrated by combining the multi-metallic plasmonic alloy NPs of AgCu, AuCu, and AgAuCu on a GaN photoactive layer in order to exploit the improved photo carrier injection by the strong hot electrons and LSPR. Among various devices, the tri-metallic AgAuCu NP PD demonstrates the highest performance with a remarkably high photocurrent of 1.47 x 10 -2 A at 1 V with fast rise (T r ) and fall (T f ) times of 170 and 700 ms with a very stable current. This leads to the superior figure-of-merit parameters of the PD with a photoresponsivity of 4.3 x 10 6 mA W-1, detectivity of 3.52 x 10 12 Jones and EQE of 1.39 x 10 6 %, which is ~16 times higher than the bare GaN PD. This ranks the AgAuCu PD as one of the best GaN based UV photodetectors. The photocurrent enhancement and excellent figure-of-merit can be attributed to the significantly increased photo carrier injection by the efficient hot electron generation via the LSPR and improved interfacial barrier characteristics by the tri-metallic elemental synergy.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the alloy composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely low mean absolute errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive experimental dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery and design of novel SMAs with targeted properties. We are not aware of any current approaches capable of predicting SMA transformation behavior over such a wide range of compositions and processing conditions.

Shape memory alloys↗
DOE OSTI2025

Data-driven multi-element substitution of TiFe alloys for tunable thermodynamics and enhanced activation behaviour for hydrogen storage

Due to their high volumetric hydrogen storage capacity under moderate storage conditions, TiFe alloys have been widely investigated as candidates for practical solid-state hydrogen storage. Partially substituting Ti or Fe sites can improve the key characteristics of TiFe alloys, such as the first hydrogen absorption step (activation) and the equilibrium hydrogen pressure (thermodynamic properties). However, the selection of substitution elements has heavily relied on intuition and trial-and-error. Also, conventional substitution strategies have mainly focused on single-element substitution within the TiFe alloy, limiting the design space and tunability for target applications. Here, to address this limitation, we report a multi-element substitution strategy motivated by an efficient, data-driven machine learning (ML) approach combined with corroborating density functional theory (DFT) calculations. Our models successfully predict experimentally measured hydride stability in five selected alloys using only compositional descriptors. Most importantly, the multi-element substitution leads to enhanced activation properties compared to pure TiFe, achieving near room-temperature activation behaviour. This work provides a method for on-demand tuning of hydrogen storage and activation properties, which may have broad implications for data-driven discovery of energy storage materials.

Cho, YongJun [Korea Advanced Institute Science and↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely small errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery of novel SMAs with targeted properties.

Shape Memory Alloys↗
DOE OSTI2024

Rejuvenation as the origin of planar defects in the CrCoNi medium entropy alloy

Abstract High or medium- entropy alloys (HEAs/MEAs) are multi-principal element alloys with equal atomic elemental composition, some of which have shown record-breaking mechanical performance. However, the link between short-range order (SRO) and the exceptional mechanical properties of these alloys has remained elusive. The local destruction of SRO by dislocation glide has been predicted to lead to a rejuvenated state with increased entropy and free energy, creating softer zones within the matrix and planar fault boundaries that enhance the ductility, but this has not been verified. Here, we integrate in situ nanomechanical testing with energy-filtered four-dimensional scanning transmission electron microscopy (4D-STEM) and directly observe the rejuvenation during cyclic mechanical loading in single crystal CrCoNi at room temperature. Surprisingly, stacking faults (SFs) and twin boundaries (TBs) are reversible in initial cycles but become irreversible after a thousand cycles, indicating SF energy reduction and rejuvenation. Molecular dynamics (MD) simulation further reveals that the local breakdown of SRO in the MEA triggers these SF reversibility changes. As a result, the deformation features in HEAs/MEAs remain planar and highly localized to the rejuvenated planes, leading to the superior damage tolerance characteristic in this class of alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding Twinning and Deformation in High Entropy Alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this project, we developed a machine learning framework, namely PREDICT ( PR edict properties from E xisting D atabase I n C omplex alloys T erritory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of dose rate on the phase stability of a CrFeNiMn alloy

We report CrFeNi-based multi-principal element alloys (MPEAs) are being considered for nuclear applications because of their potentially superior mechanical properties and irradiation resistance. While the alloys appear stable against phase decomposition during ion irradiation performed at high dose rate, previous electron irradiation and thermal annealing studies suggested that some of them may be susceptible to phase decomposition. Therefore, to clarify the role of dose rate during ion irradiation on phase decomposition, a CrFeNiMn alloy was irradiated using Fe ions at 500 °C with an intermediate dose rate of 10 -4 dpa/s. Transmission electron microscopy and atom probe tomography observations revealed a high number density of NiMn precipitates, cavities, and dislocation loops, which had not previously been observed after ion irradiation at higher dose rates.

36 MATERIALS SCIENCE↗