Search NASA⌕ Search

SEARCH · Search NASA

Results for “space link”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cross-family and phage-specific gene requirements for Klebsiella infection revealed by scalable RB-TnSeq genetic screens.

Bacteriophages are being cataloged at an accelerating pace and are recognized as key players in nutrient and energy cycling across ecosystems. Yet the bacterial genetic determinants that govern phage-host specificity and infection success remain poorly understood, particularly in clinically and ecologically important genera such as Klebsiella where prior receptor characterization has been almost entirely limited to capsulated strains. Here we used a randomly barcoded, genome-wide, loss-of-function transposon mutant library (RB-TnSeq) of Klebsiella sp. M5al, a naturally acapsular, nitrogen-fixing rhizobacterium, to generate the first systematic, cross-family map of phage receptor gene dependencies in Klebsiella. Challenging the library against 25 double-stranded DNA phages spanning five families in 213 parallel assays, we identified 42 bacterial genes associated with phage infection, of which 15 had no prior association with phage infection in any bacterial system. Disruption of surface receptor biosynthesis genes conferred cross-resistance across multiple phage families, while intracellular gene disruptions had predominantly phage-specific effects. Clonal validation of eight genes confirmed LPS outer core biosynthesis genes as primary receptor determinants alongside additional host factors spanning outer membrane transport, cofactor biosynthesis, and two-component signaling. Comparative analysis across all 25 phages revealed that phage genus rather than family is the stronger predictor of host gene dependency profiles, a finding with direct implications for the functional annotation of uncharacterized phage isolates and rational phage cocktail design. Together, these findings provide a community resource for linking phage genomic diversity to functional host interaction space in this ecologically and clinically important genus.

Gittrich, Marissa R↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Counterweight for Long Reach Robotic Arm

Maintenance for vital systems proves challenging due to their location: tucked up and behind components. Pinpointing the precise location of errors presents difficulty when relying solely on the human eye. The solution to this issue arises in the form of a Long Reach Robotic Arm. An adequately balanced robot with several individually controlled links contains the capability to maneuver around obstructing components to locate the points of failure with accuracy. The design process for such a robot begins with the writing of a program. The program intends to visualize how the Long Reach Robotic Arm will move in 2D space by printing a select number of lines representing each arm link to the screen and controlling each link through the keyboard. This code substantially simplifies the design process for the multi-linked arm. The next step in the Long Reach Robotic Arm design process is the development of the arm and its counterweight system. The clear division of this work allows two interns to work on this project: one assumes responsibility for the arm, and the other for the counterweight. I undertook the design for the counterbalance. My half of the project is essential for maintaining safety as an extended multi-linked robot arm presents the challenge of preserving stability. The best method to supply a balance to the robot, even in extension, is a mobile counterweight with a compact design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Active deep kernel learning of molecular properties from structural embeddings

As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular properties. This paper presents an active learning approach for molecular discovery using deep kernel learning (DKL), demonstrated on the QM9 dataset. DKL links structural embeddings directly to properties, creating organized latent spaces that prioritize relevant property information. By iteratively recalculating embedding vectors in alignment with target properties, DKL uncovers concentrated maxima representing key molecular properties and reveals unexplored regions with potential for innovation. This approach underscores DKL’s potential in advancing molecular research and discovery.

Artificial neural networks↗
DOE OSTI2021

In-situ proton radiation testing of 2.4 micron wavelength extended InGaAs photodiodes at dry ice and room temperatures

We have successfully tested 290 µm diameter, 2.4 micron wavelength, Extended InGaAs photodiodes coupled with single mode fiber using 50 MeV Protons at both dry ice temperature (-72 °C) and room temperature (20 °C). The devices were reverse biased at 100 mV during the radiation run and their leakage current was continuously monitored insitu during the exposure. These devices find multiple applications in space for spectroscopy & sensing, inter-satellite optical communication links, rapid Doppler shift LIDAR, as well as inter-planetary and Earth-to-Moon communication links. Several photodiodes were tested using 50 MeV Protons with an average flux level of 2.11 × 10 7 protons/cm 2 /s, for a total fluence of 1.0 × 10 11 protons/cm 2 and total dose of 20 krad (water). Pre- and post-radiation results were also measured for leakage current vs. voltage, responsivity (quantum efficiency), and bandwidth of the Extended InGaAs photodiodes. Finally, all devices were found to be fully functional at normal operating conditions and at both dry ice and room temperature.

2.4 micron wavelength↗
DOE OSTI2021

Effects of Strain Rate and Temperature on the Mechanical Properties of Simulated Silica Ionogels

Ionogels are hybrid materials formed by impregnating the pore space of a solid matrix with a conducting ionic liquid. By combining the properties of both component materials, ionogels can act as self-supporting electrolytes in Li batteries. In this study, molecular dynamics simulations are used to investigate the dependence of mechanical properties of silica ionogels on solid fraction, temperature, and pore width. Comparisons are made with corresponding aerogels. We find that the solid matrix fraction increases the moduli and strength of the ionogel. This varies nonlinearly with temperature and strain rate, according to the contribution of the viscous ionic liquid to resisting deformation. Additionally, owing to the temperature and strain sensitivity of the ionic liquid viscosity, the mechanical properties approach a linear mixing law at high temperature and low strain rates. The median pore width of the solid matrix plays a complex role, with its influence varying qualitatively with deformation mode. Narrower pores increase the relevant elastic modulus under shear and uniaxial compression but reduce the modulus obtained under uniaxial tension. Conversely, shear and tensile strength are increased by narrowing the pore width. All of these pore size effects become more pronounced as the silica fraction increases. Pore size effects, similar to the effects of temperature and strain rate, are linked to the ease of fluid redistribution within the pore space during deformation-induced changes in the geometry of the pores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Euler characteristic as a basis for teaching topology concepts to crystallographers

The simple Euler polyhedral formula, expressed as an alternating count of the bounding faces, edges and vertices of any polyhedron, V – E + F = 2, is a fundamental concept in several branches of mathematics. Obviously, it is important in geometry, but it is also well known in topology, where a similar telescoping sum is known as the Euler characteristic χ of any finite space. The value of χ can also be computed for the unit polyhedra (such as the unit cell, the asymmetric unit or Dirichlet domain) which build, in a symmetric fashion, the infinite crystal lattices in all space groups. In this application χ has a modified form (χ m ) and value because the addends have to be weighted according to their symmetry. Although derived in geometry (in fact in crystallography), χ m has an elegant topological interpretation through the concept of orbifolds. Alternatively, χ m can be illustrated using the theorems of Harriot and Descartes, which predate the discovery made by Euler. Those historical theorems, which focus on angular defects of polyhedra, are beautifully expressed in the formula of de Gua de Malves. In a still more general interpretation, the theorem of Gauss–Bonnet links the Euler characteristic with the general curvature of any closed space. This article presents an overview of these interesting aspects of mathematics with Euler's formula as the leitmotif. Finally, a game is designed, allowing readers to absorb the concept of the Euler characteristic in an entertaining way.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Case for Electron-Astrophysics

Abstract The smallest characteristic scales, at which electron dynamics determines the plasma behaviour, are the next frontier in space and astrophysical plasma research. The analysis of astrophysical processes at these scales lies at the heart of the research theme of electron-astrophysics. Electron scales are the ultimate bottleneck for dissipation of plasma turbulence, which is a fundamental process not understood in the electron-kinetic regime. In addition, plasma electrons often play an important role for the spatial transfer of thermal energy due to the high heat flux associated with their velocity distribution. The regulation of this electron heat flux is likewise not understood. By focussing on these and other fundamental electron processes, the research theme of electron-astrophysics links outstanding science questions of great importance to the fields of space physics, astrophysics, and laboratory plasma physics. In this White Paper, submitted to ESA in response to the Voyage 2050 call, we review a selection of these outstanding questions, discuss their importance, and present a roadmap for answering them through novel space-mission concepts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Strong, Yet Split Hydrogen Bonding with Ice Rules in Delafossite (H/D)RhO 2

Despite remaining enigmatic, strong hydrogen bonding provides an advanced design handle for tailoring the properties of functional materials. Here, in this study, 3 R –(H/D)RhO 2 delafossites (prepared by ion exchange of Na + from NaRhO 2 ) contain H/D in linear coordination with O, linking Rh III O 2 layers. Bragg and real-space X-ray and neutron scattering analysis, vibrational and solid-state NMR spectroscopy, and density functional theory (DFT)–based electronic structure calculations have been employed to understand the nature of the hydrogen bonding. Despite short distances between H/D and the two O to which they are bonded, a clear double-minimum corresponding to a shorter and longer (H/D)–O distance is established. The triangular lattices formed by H/D appear to display ice-like disorder, corroborated by low-temperature heat capacity measurements.

NMR spectroscopy↗
DOE OSTI2024

Km‐Scale Simulations of Mesoscale Convective Systems Over South America—A Feature Tracker Intercomparison

Mesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth's water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object-based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluate MCSs in kilometer-scale simulations with observational-based MCSs over 3 years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems in space and time and the treatment of how MCSs are linked in time. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Adaptive Uncertainty Quantification for Stochastic Hyperbolic Conservation Laws

Here, we propose a predictor-corrector adaptive method for the study of hyperbolic partial differential equations (PDEs) under uncertainty. Constructed around the framework of stochastic finite volume (SFV) methods, our approach circumvents sampling schemes or simulation ensembles while also preserving fundamental properties, in particular hyperbolicity of the resulting systems and conservation of the discrete solutions. Furthermore, we augment the existing SFV theory with a priori convergence results for statistical quantities, in particular push-forward densities, which we demonstrate through numerical experiments. By linking refinement indicators to regions of the physical and stochastic spaces, we drive anisotropic refinements of the discretizations, introducing new degrees of freedom where deemed profitable. To illustrate our proposed method, we consider a series of numerical examples for nonlinear hyperbolic PDEs based on Burgers’ and Euler’s equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Novel Spatio-Temporal Regime Tracking Method for Impact Simulations

In this proposal, we present a novel method of tracking the rheological regimes activated during impact cratering events that will allow researchers to gain new insights into cratering mechanics. Rheology describes the stress-strain response of rocks to different conditions. Planetary impact cratering events often occur on too large of a scale to be feasibly captured in controlled experiments. Instead, these dynamic events are primarily studied using multi-physics codes equipped with complex material models that enable calculations of the impact event at scale. However, determining which physical processes are required for the problem of interest is challenging. Because the dominant rheological regimes change with space and time during crater formation, it is difficult to link numerical simulations with observable features of craters at the end of the event. The basis of this work is the implementation of numerical flags that track the activation of each rheological regime throughout impact simulations. We demonstrate this with the ‘Rock Model’ implemented in the CTH shock-physics code. We will use this rheology tracking method to ’zoom in’ on a specific region within an event and track the conditions the rock experiences over time. This work will develop community benchmarks to validate and distribute our implemented rheological models. We will focus on improving the melt models used by the planetary impact modeling community by developing an EOS-aware rheological transition from solid to melt. Through analysis of cell and tracer-particle based tracking data, we will study the effects of different rheologies on modeled outcomes, particularly on the volume and distributions of impacts melts. We will also use this method to link observable features with the rheological mechanisms responsible for them. The deliverables (peer-reviewed papers) from the proposed work are (i) tests of the implemented rheologic processes and demonstrations of the tracking flags; (ii) the first calculations of the spatio-temporal evolution of the dominant rheologies during impact cratering events; and (iii) application to delivery of impactor iron during basin-scale impacts.

58 GEOSCIENCES↗
DOE OSTI2024

A Theory for the Balance between Warm Rain and Ice Crystal Processes of Precipitation in Mixed-Phase Clouds

Abstract Mixed-phase clouds contain both supercooled cloud liquid and ice crystals. In principle, precipitation may be initiated either by the liquid phase or by the ice phase. Ice crystals may grow by vapor diffusion to become snow (“ice crystal process”), forming “cold” precipitation. Equally, cloud droplets, when large enough, coalesce to form “warm” precipitation by the “warm rain process.” Warm rain could be supercooled and freeze as “warm” graupel. In the present paper, a new simplified theoretical analysis is provided to examine the microphysical system consisting of three species of hydrometeor, namely, cloud liquid, “cold ice” (crystals, snow), and “warm rain” (frozen or supercooled). This is obtained by nondimensionalizing and simplifying the evolution equations for the mass of each species. Analytical formulas are given for equilibria. Feedback analysis shows that the sign of the feedback is linked to the abundance of precipitation, with a neutral surface in the 3D phase space. The system’s precipitation amount explodes while in the initial unstable regime, crossing the neutral surface and approaching the equilibrium point that is a stable attractor. Positive and negative feedbacks are elucidated. In a standard case, the cold ice mass is about 1000 times larger than the warm rain mass. To illustrate the physical behavior of the theory, sensitivity tests are performed with respect to environmental conditions (e.g., aerosol, updraft speed) and microphysical parameters (e.g., riming and sedimentation rates for cold ice). Cold ice prevails, especially in fast ascent, due to its low bulk density, favoring slow sedimentation and a wide cross-sectional area for riming. Significance Statement The theory elucidates how the ice phase can prevail in the precipitation from any mixed-phase clouds with supercooled cloud liquid and crystals. The ice phase radically suppresses cloud liquid by riming when active and “wins” the competition against coalescence. This prevalence of ice is shown to arise from the low bulk density of snow. The cloud is viewed as a system of negative and positive feedbacks that prevail in realms of stability and instability in a 3D phase space.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Synthesis and structural characterization of the new Zintl phases Eu 10 Mn 6 Bi 12 and Yb 10 Zn 6 Sb 12

Two new ternary compounds, Eu 10 Mn 6 Bi 12 and Yb 10 Zn 6 Sb 12 , were synthesized and structurally characterized. The synthesis was achieved either through reactions in sealed niobium tubes or in alumina crucibles by combining the elements in excess molten Sb. Their structures were elucidated using single-crystal X-ray diffraction, and they were determined to crystallize in the orthorhombic space group Cmmm (no. 65) with the Eu 10 Cd 6 Bi 12 structure type. Akin to the archetype phase, both Mn and Zn sites contain about 25% of vacancies. Furthermore, the anionic substructure of the title phases can be described as [M 6 Pn 12 ] (M = Zn, Mn; Pn = Sb, Bi) double layers composed of the corner and edge-sharing [MPn 4 ] tetrahedra, linked by [Pn 2 ] 4– dumbbells. Eu 2+ /Yb 2+ cations fill the space between the layers, with the valence electron counts adhering closely to the Zintl–Klemm rules, i.e., both Eu 10 Mn 6 Bi 12 and Yb 10 Zn 6 Sb 12 are expected to be valence-precise compounds. Analysis of the electronic structure and transport properties of Yb 10 Zn 6 Sb 12 indicate semimetallic behavior with relatively low Seebeck coefficient and resistivity that slightly decreases as a function of temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2026

JIMWLK on a quantum computer

We propose a method for solving the Jalilian-Marian-Iancu-McLerran-Weigert-Leonidov-Kovner (JIMWLK) evolution equation on quantum computers. Our approach exploits the reformulation of the JIMWLK equation as a Lindblad master equation governing the rapidity evolution of the hadronic density matrix, as established in prior work. To render the problem tractable for quantum simulation, we introduce several approximations: the two-dimensional transverse plane is reduced to a one-dimensional radial lattice by assuming azimuthal symmetry of the jump operators; the gauge group is restricted to SU(2); and the infinite Wilson lines of the JIMWLK equation are replaced by finite Wilson links along the light-cone direction. The resulting bosonic Hilbert space is truncated using the electric field basis familiar from Hamiltonian lattice gauge theory, with states restricted to angular momenta 𝑗 ≤ 𝑗 max . We derive the matrix elements of the JIMWLK Lindblad jump operators in this basis. As a benchmark, we demonstrate rapid convergence of the fundamental dipole expectation value with 𝑗 max for both pure and mixed Gaussian initial density matrices. For the simplest truncation, 𝑗 max =1/2, we implement the Lindblad evolution using a quantum simulation algorithm verified with the Qiskit statevector simulator by decomposing the non-unitary evolution operator into a linear combination of unitaries. This work establishes a concrete pathway toward quantum simulation of high-energy quantum chromodynamics evolution equations, with direct relevance to the physics program of the Electron-Ion Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Predictive Control to Further Reduce DC-Link Capacitor Current Stress for Segmented Inverter

Two three-phase interleaved inverters have been used in traction drive applications to reduce the current stress in a DC link capacitor bank. In such applications, either carrier-based or space vector modulation is used to select the optimum switching sequences, and the results show 50% less capacitor current than that of a single three-phase inverter. The switching state selection process for this inverter is tedious, and there has been no research to find the optimal switching state. To overcome this challenge, this research employed a simple finite set model predictive control to select the optimum switching sequence for a dual three-phase interleaved topology, called a segmented inverter. The results show that the predictive control algorithm can provide a simple solution and can reduce the current stress by 27% compared with traditional modulation techniques for the segmented inverters.

Ribeiro, Pedro↗