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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Electronic transport and phonon properties of maximally disordered alloys: From binaries to high-entropy alloys

Recent discoveries of multicomponent concentrated solid-solution alloys hold promise for enhanced properties—such as enhanced mechanical properties, radiation tolerance, high temperature strength, corrosion resistance and some novel functional properties, provide a new strategy for alloy design using extreme disorder. Yet, deep understanding of these intriguing properties is complicated by the very effects of disorder that make them interesting. All the desirable properties of these alloys ultimately originate from the disorder-induced properties of underlying electronic structure, lattice dynamics, and thermodynamics. Therefore, understanding the disorder-induced fundamental physical properties is prerequisite for the science-based design of this class of alloys for practical applications. Furthermore, we elucidate the role of extreme (maximal) substitutional disorder plays in the fundamental physics of disordered alloys and review the recently developed theoretical methodologies in modeling the basic physical properties, particularly electronic structure, magnetism, electrical transport, and lattice vibrations in multicomponent concentrated solid-solution alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data Fusion via Neural Network Entropy Minimization for Target Detection and Multi-Sensor Event Classification

Broadly applicable solutions to multimodal and multisensory fusion problems across domains remain a challenge because effective solutions often require substantive domain knowledge and engineering. The chief questions that arise for data fusion are in when to share information from different data sources, and how to accomplish the integration of information. The solutions explored in this work remain agnostic to input representation and terminal decision fusion approaches by sharing information through the learning objective as a compound objective function. The objective function this work uses assumes a one-to-one learning paradigm within a one-to-many domain which allows the assumption that consistency can be enforced across the one-to-many dimension. The domains and tasks we explore in this work include multi-sensor fusion for seismic event location and multimodal hyperspectral target discrimination. We find that our domain- informed consistency objectives are challenging to implement in stable and successful learning because of intersections between inherent data complexity and practical parameter optimization. While multimodal hyperspectral target discrimination was not enhanced across a range of different experiments by the fusion strategies put forward in this work, seismic event location benefited substantially, but only for label-limited scenarios.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗