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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mechanical properties of an Al 10 Nb 20 Ta 15 Ti 30 V 5 Zr 20 A2/B2 refractory superalloy and its constituent phases

Refractory superalloys (RSAs) are a new class of high-temperature structural materials consisting of a nanometer-sized mixture of two coherent A2 (disordered BCC) and B2 (ordered) phases. While these RSAs typically exhibit superior mechanical properties at elevated temperatures, they suffer from limited plasticity at lower temperatures, often attributed to the continuous ordered B2 matrix in these two-phase alloys. To knowledgably control the microstructure and mechanical properties of these alloys, the properties of the constitutive phases in RSAs should be known. Currently this information is absent. Here, in the present work, the microstructure and mechanical properties (at 20–1200 °C) of a candidate Al 0.5 NbTa 0.8 Ti 1.5 V 0.2 Zr RSA and two single-phase alloys, which compositions correspond to the compositions of the A2 and B2 phases in the selected RSA, are reported. Interestingly, and quite unexpectedly, the intrinsic mechanical behavior of the constituent A2 and B2 phases, deciphered for the first time in the present study, revealed that they are softer and substantially more ductile than the parent two-phase RSA. Furthermore, the ordered B2 phase exhibits a deformation twinning mechanism leading to twinning induced plasticity (TWIP) coupled with a low APB energy and activity of 1/2<111> dislocations. These mechanisms are thought to be responsible for the high plasticity of the ordered B2 phase. The results presented here question the current assumption that the ordered B2 phase in these RSAs has limited plasticity and strongly indicate that the nature of the B2/A2 interface boundaries and coherency strengthening play an important role in controlling the mechanical properties of the two-phase RSA.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the role of geometrically necessary dislocations in void formation and growth in response to shock loading conditions in wrought and additively manufactured Ta

This study investigates the role of geometrically necessary dislocations (GNDs) and microstructure on void nucleation and growth in wrought and additively manufactured (AM) tantalum subjected to high-strain rate loading. Multi-modal 3D data was collected using TriBeam tomography to calculate GND densities and their spatial relationship to voids. A microstructural comparison between the wrought and AM samples identified distinct void shapes and locations, with intragranular voids and more spherical voids frequently observed in the AM dataset. Results indicate that voids preferentially form at both high-angle grain boundaries and low-angle subgrain boundaries, the latter of which are frequently observed in the AM material. Through a radial distribution analysis of all voids in the datasets, significant GND localization to near-void-surface regions was observed in both samples. 3D crystal plasticity simulations were employed to extend the experimental observations, revealing higher void growth rates in [111] oriented grains when compared to [001] grains. The simulations also suggest that GNDs can be generated as part of the void growth process, with more GND accumulation for growth in a [111] grain than a [001] grain. These findings provide valuable insights into the links between nanoscale void nucleation, mesoscale void growth, and microstructural effects in dynamically loaded tantalum.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Elastic properties of a Sc–Zr–Nb–Ta–Rh–Pd high-entropy alloy superconductor

Here, we report a comprehensive study on the elastic properties of a hexanary high-entropy alloy superconductor (ScZrNbTa) 0.685 [RhPd] 0.315 at room and cryogenic temperatures, by Resonant Ultrasound Spectroscopy experiments. The derived elastic constants are bulk modulus K=132.7 GPa, Young’s modulus E=121.0 GPa, shear modulus G=44.9 GPa, and Poisson’s ratio v= 0.348 for room temperature. The Young’s and shear moduli are ~ 10% larger than those in NbTi superconductor with similar T c , while the ductility is comparable. Moreover, the mechanical performance is further enhanced at cryogenic temperature. Our work confirms the advantageous mechanical properties of high-entropy alloy superconductors and suggests the application prospects.

36 MATERIALS SCIENCE↗