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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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A cross scale investigation of galena oxidation and controls on mobilization of lead in mine waste r
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Investigation of γ - ( U , Z r ) structural properties and its interfacial properties with liquid sodium using ab initio molecular dynamics
In this study, the elastic properties, structural parameters, sound velocity, and Debye temperature of γ–(U,Zr) were computed using ab initio molecular dynamics (AIMD) at temperatures between 1000 K and 1400 K and for Zr content between 0 at. % and 100 at. %. UZr is used as a metallic fuel for Sodium Fast Reactors (SFRs). The study of the mechanical and thermal behavior of these alloys leads to a better data-informed fuel design. The bulk modulus, shear modulus, Young's modulus, and Poisson's ratio were calculated from the elastic constants and their dependence on Zr content and temperature was investigated, comparing the results with previous computational work and the available experimental data in the literature. Interfacial properties between UZr (up to 32 at. % which typically exists in nuclear fuel) and liquid sodium are also of interest due to the presence of a sodium bond between the fuel and the cladding in metallic nuclear fuel. The interfacial energy between γ–(U,Zr) and liquid sodium, the surface tension of liquid sodium, and the work of adhesion were computed at different temperatures and Zr concentrations. It was demonstrated that γ–(U,Zr) is completely wetted by liquid sodium at all the investigated temperatures and Zr concentrations. Finally, this work provides the basis for the determination of interfacial resistances in SFRs and their implementation into heat transfer fuel performance simulations, which will be the subject of future work.
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.
Type III seesaw with R-parity violation in light of m (CDF)
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An extended R Ψ m ( 2 ) ( Δ S 2 ) correlator for detecting and characterizing the Chiral Magnetic Wave
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Evaluating the tail of the distribution: the economic contributions of frequently awarded government R&D recipients
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Implementing bubbly mercury material model (R-P model) in the pulse simulation to predict strain on the Spallation Neutron Source target vessel with gas injection
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oreo: An R package for large amplitude oscillatory analysis
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Structure basis of protein kinase R autophosphorylation
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