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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mantaray: A Rust Package for Ray Tracing Ocean Surface Gravity Waves

Ocean surface gravity waves are an important component of air-sea interaction, influencing energy, momentum, and gas exchanges across the ocean-atmosphere interface. In specific applications such as refraction by ocean currents or bathymetry, ray tracing provides a computationally efficient way to gain insight into wave propagation. In this paper, we introduce Mantaray, an open-source software package implemented in Rust, with a Python interface, that solves the ray equations for ocean surface gravity waves. Mantaray is designed for performance, robustness, and ease of use. The package is modular to facilitate further development and can currently be applied to both idealized and realistic wave propagation problems (Fig. 1).

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

automesh: Automatic mesh generation in Rust

automesh is an open-source Rust software program that uses a segmentation, typically generated from a 3D image stack, to create a finite element mesh, composed either of hexahedral (volumetric) or triangular (isosurface) elements. automesh converts between segmentation formats (.npy, .spn) and mesh formats (.exo, .inp, .mesh, .stl, .vtk). automesh can defeature voxel domains, apply Laplacian and Taubin smoothing, and output mesh quality metrics. automesh uses an internal octree for fast performance.

Hovey, Chad Brian [Sandia National Laboratories (S↗
DOE OSTI2018

Rust in HPC

No abstract provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ninterp: N-dimensional interpolation Rust crate [SWR-25-25]

The ninterp crate provides multivariate interpolation over a regular, sorted, nonrepeating grid of any dimensionality. A variety of interpolation strategies are implemented, however more are likely to be added. Extrapolation beyond the range of the supplied coordinates is supported for 1-D linear interpolators, using the slope of the nearby points. There are hard-coded interpolators for lower dimensionalities (up to N = 3) for better runtime performance. All interpolation is handled through instances of the Interpolator enum, with the selected tuple variant containing relevant data. Interpolation is executed by calling Interpolator::interpolate.

Carow, Kyle↗
DOE OSTI2022

Characterizing the Defense Hierarchy of Populus trichocarpa

The U.S. Departments of Energy and Agriculture are developing bioenergy crops as a sustainable alternative to traditional fossil fuels. Plant disease threatens this effort by inhibiting the efficient production of plant feedstocks for bioenergy. In particular, biotrophic Melampsora leaf rust pathogens can increase morbidity in Populus trichocarpa and its hybrids (aka poplars), the primary woody feedstocks for bioenergy. Controlling rust disease in plantations is thus critical to the success of the poplar bioenergy program. Current efforts to control rust disease focus on plant genetic resistance. However, with novel pathogenic variation generated by the global movement of pathogens and their hybridization, it is increasingly clear that controlling rust disease in a long-lived plant like Populus depends on our ability to understand and manage not only genes for rust resistance, but also short-lived plant microorganisms that contribute to defense against rust (aka “defense mutualists”). Our proposed research thus seeks to develop an integrative, hierarchical model of P. trichocarpa defense that integrates genetic resistance and defense mutualists. Our overarching hypothesis is that defense against Melampsora rust is biologically degenerate, with major and minor plant resistance genes, plant defense compounds, and defense mutualists within the microbiome each contributing to rust resistance under different circumstances. The specific aims of our study will test the placement of each of these factors in the defense hierarchy. Ultimately, it is our hope that disease management strategies harnessing both resistance genes and naturally occurring defense mutualists of P. trichocarpa, and an appreciation of the ecological circumstances in which each is effective, will maximize plant resistance and productivity while minimizing impacts on the surrounding ecological landscape.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Contingency rules for pathogen competition and antagonism in a genetically based, plant defense hierarchy

Abstract Plant defense against pathogens includes a range of mechanisms, including, but not limited to, genetic resistance, pathogen‐antagonizing endophytes, and pathogen competitors. The relative importance of each mechanism can be expressed in a hierarchical view of defense. Several recent studies have shown that pathogen antagonism is inconsistently expressed within the plant defense hierarchy. Our hypothesis is that the hierarchy is governed by contingency rules that determine when and where antagonists reduce plant disease severity. Here, we investigated whether pathogen competition influences pathogen antagonism using Populus as a model system. In three independent field experiments, we asked whether competition for leaf mesophyll cells between a Melampsora rust pathogen and a microscopic, eriophyid mite affects rust pathogen antagonism by fungal leaf endophytes. The rust pathogen has an annual, phenological disadvantage in competition with the mite because the rust pathogen must infect its secondary host in spring before infecting Populus . We varied mite–rust competition by utilizing Populus genotypes characterized by differential genetic resistance to the two organisms. We inoculated plants with endophytes and allowed mites and rust to infect plants naturally. Two contingency rules emerged from the three field experiments: (a) Pathogen antagonism by endophytes can be preempted by host genes for resistance that suppress pathogen development, and (b) pathogen antagonism by endophytes can secondarily be preempted by competitive exclusion of the rust by the mite. Synthesis : Our results point to a Populus defense hierarchy with resistance genes on top, followed by pathogen competition, and finally pathogen antagonism by endophytes. We expect these rules will help to explain the variation in pathogen antagonism that is currently attributed to context dependency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Effects of Fe(III) Oxide Mineralogy and Phosphate on Fe(II) Secondary Mineral Formation during Microbial Iron Reduction

The bioreduction of Fe(III) oxides by dissimilatory iron-reducing bacteria may result in the formation of a suite of Fe(II)-bearing secondary minerals, including magnetite (a mixed Fe(II)/Fe(III) oxide), siderite (Fe(II) carbonate), vivianite (Fe(II) phosphate), chukanovite (ferrous hydroxy carbonate), and green rusts (mixed Fe(II)/Fe(III) hydroxides). In an effort to better understand the factors controlling the formation of specific Fe(II)-bearing secondary minerals, we examined the effects of Fe(III) oxide mineralogy, phosphate concentration, and the availability of an electron shuttle (9,10-anthraquinone-2,6-disulfonate, AQDS) on the bioreduction of a series of Fe(III) oxides (akaganeite, feroxyhyte, ferric green rust, ferrihydrite, goethite, hematite, and lepidocrocite) by Shewanella putrefaciens CN32, and the resulting formation of secondary minerals, as determined by X-ray diffraction, Mössbauer spectroscopy, and scanning electron microscopy. The overall extent of Fe(II) production was highly dependent on the type of Fe(III) oxide provided. With the exception of hematite, AQDS enhanced the rate of Fe(II) production; however, the presence of AQDS did not always lead to an increase in the overall extent of Fe(II) production and did not affect the types of Fe(II)-bearing secondary minerals that formed. The effects of the presence of phosphate on the rate and extent of Fe(II) production were variable among the Fe(III) oxides, but in general, the highest loadings of phosphate resulted in decreased rates of Fe(II) production, but ultimately higher levels of Fe(II) than in the absence of phosphate. In addition, phosphate concentration had a pronounced effect on the types of secondary minerals that formed; magnetite and chukanovite formed at phosphate concentrations of ≤1 mM (ferrihydrite), <~100 µM (lepidocrocite), 500 µM (feroxyhyte and ferric green rust), while green rust, or green rust and vivianite, formed at phosphate concentrations of 10 mM (ferrihydrite), ≥100 µM (lepidocrocite), and 5 mM (feroxyhyte and ferric green rust). These results further demonstrate that the bioreduction of Fe(III) oxides, and accompanying Fe(II)-bearing secondary mineral formation, is controlled by a complex interplay of mineralogical, geochemical, and microbiological factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗