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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 OSTI2023

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

Materials Science↗
DOE OSTI2021

Computational exploration of biomedical HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr refractory high-entropy alloys

Refractory high entropy alloys (RHEAs) have been proven to be a potential candidate in the biomedical field due to their balanced mechanical properties and biocompatible composition. Recent experimental findings show that RHEAs like HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr have good mechanical properties such as high polarization and wear resistance than others which establish them as potential materials for biomedical application. In this work, we performed first-principles density functional theory calculations on the mechanical and thermal properties of HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr. The predicted lattice constant, density, Young's modulus, and Vickers hardness are consistent with the available experimental report, which verifies the accuracy of the applied model. The thermal coefficient of linear expansion of both RHEAs has been investigated by utilizing the Debye theory. The present methods could be applied to study other future RHEAs on exploration of their physical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploiting Commonly-Reported Age-Hardening Data and Discovering Systematics Across Metallic Alloys and Alloy Systems to Identify Corrosion’s Most Influential Factors

This work explored ways to data mine legacy literature and predict solid-solid precipitation in support of simpler assessment of corrosion propensity in metallic alloys. Of interest was locating the peak age watershed (maximum hardness or strength), beyond which lies the regime termed “overaging.” A diligent search of literature and reference books, discussions with SMEs, and application of various algorithms showed that none of the premises going in held up to scrutiny.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ZnO-Au x Cu 1-x Alloy and ZnO-Au x Al 1-x Alloy Vertically Aligned Nanocomposites for Low-Loss Plasmonic Metamaterials

Hyperbolic metamaterials are a class of materials exhibiting anisotropic dielectric function owing to the morphology of the nanostructures. In these structures, one direction behaves as a metal, and the orthogonal direction behaves as a dielectric material. Applications include subdiffraction imaging and hyperlenses. However, key limiting factors include energy losses of noble metals and challenging fabrication methods. In this work, self-assembled plasmonic metamaterials consisting of anisotropic nanoalloy pillars embedded into the ZnO matrix are developed using a seed-layer approach. Alloys of Au x Al 1-x or Au x Cu 1-x are explored due to their lower losses and higher stability. Optical and microstructural properties were explored. The ZnO-Au x Cu 1-x system demonstrated excellent epitaxial quality and optical properties compared with the ZnO-Au x Al 1-x system. Both nanocomposite systems demonstrate plasmonic resonance, hyperbolic dispersion, low losses, and epsilon-near-zero permittivity, making them promising candidates towards direct photonic integration.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hierarchical porous metals with deterministic 3D morphology and shape via de-alloying of 3D printed alloys

The present disclosure relates to a method for forming a three dimensional, hierarchical, porous metal structure with deterministically controlled 3D multiscale pore architectures. The method may involve providing a feedstock able to be applied in an additive manufacturing process, and using an additive manufacturing process to produce a three dimensional (3D) structure using the feedstock. The method may involve further processing the 3D structure through at least a de-alloying operation to form a metallic 3D structure having an engineered, digitally controlled macropore morphology with integrated nanoporosity.

Qi, Zhen↗
DOE OSTI2024

Summary of Alloy 617 and Alloy 709 Elevated Temperature Crack Growth Test Results from the Planned FY24 Crack Growth Test Activities

This report covers the results from crack growth rate testing performed at Idaho National Laboratory in the fiscal year 2024. It includes two creep crack growth rate tests and two creep-fatigue crack growth rate tests. Results are shown for each of these tests, included crack growth measured during the test using direct current potential drop, and measurements based off of the fracture surface. Load line displacement, as well as load, were also collected during the tests. Detailed analysis of the results will continue into the next fiscal year, however, a number of potential improvements to the equipment and testing procedures were identified that will increase testing reliability and throughput, as well as decrease complications with the analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

density functional theory↗