Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “DOMAIN STRUCTURE”

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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Controlling the stability of both the structure and velocity of domain walls in magnetic nanowires

For magnetic nanowire devices, the precise control of both domain wall (DW) motion and pinning behaviour is essential for reliable functional performance. The domain wall velocity and wall structure are typically sensitive to the driving field or spin-polarized current, and the pinning behaviour depends on the walls' structure and chirality, leading to variability in behaviour. Here, a systematic study combining experimental measurements and micromagnetic simulations of planar nanowires with small fixed-angle structural modulations on both edges was undertaken to study the domain wall reversal regime. A phase diagram for the reversal field as a function of modulation amplitude was obtained that shows that three DW reversal regime. A range of field and modulation amplitudes were identified in which stable DW reversal occurs, where the wall velocity is constant as a function of field and the wall structure is stable, which is well suited to applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

High-resolution crystal structure reveals a HEPN domain at the C-terminal region of S. cerevisiae RNA endonuclease Swt1

Highlights: • Crystal structure of the C-terminal (CT) domain of Swt1 was determined at 2.3 Å. • Structure of the CT domain was identified as HEPN domain superfamily member. • Low-resolution envelope of Swt1 full-length in solution was analyzed by SAXS. • The middle and CT domains gave good fit to SAXS structural model. - Abstract: Swt1 is an RNA endonuclease that plays an important role in quality control of nuclear messenger ribonucleoprotein particles (mRNPs) in eukaryotes; however, its structural details remain to be elucidated. Here, we report the crystal structure of the C-terminal (CT) domain of Swt1 from Saccharomyces cerevisiae, which shares common characteristics of higher eukaryotes and prokaryotes nucleotide binding (HEPN) domain superfamily. To study in detail the full-length protein structure, we analyzed the low-resolution architecture of Swt1 in solution using small angle X-ray scattering (SAXS) method. Both the CT domain and middle domain exhibited a good fit upon superimposing onto the molecular envelope of Swt1. Our study provides the necessary structural information for detailed analysis of the functional role of Swt1, and its importance in the process of nuclear mRNP surveillance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Amphiphilic Co-Solvents Modulate the Structure of Membrane Domains

Biofuels are an increasing part of the sustainable energy picture. This makes it a societal and economic imperative to optimize biofuel production. Mitigating the toxic effects of amphiphilic co-solvents is one way to improve the efficiency of biofuel production. Amphiphiles partition into cellular membranes, leading to membrane thinning, destabilization, loss of membrane potential, and, ultimately, cell death. However, this picture of solvent toxicity misses the disruptive impact of co-solvents on lateral membrane organization, which is increasingly recognized as critical for membrane protein sorting and oligomerization. The alteration or disruption of membrane domains has deleterious effects on cellular processes. Here, in this work, we pursue the hypothesis that membrane lateral organization is disrupted by the presence of co-solvents at concentrations lower than those which lead to full membrane destabilization. The disruption occurs due to an increasing interfacial tension between the co-existing phases, resulting in conformational changes to minimize the interfacial length-to-area ratio. This represents an unrecognized mode of solvent-induced stress and a new target for interventions to improve fermentation yields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2003

Effects of Wing Leading Edge Penetration with Venting and Exhaust Flow from Wheel Well at Mach 24 in Flight

A baseline solution for CFD Point 1 (Mach 24) in the STS-107 accident investigation was modified to include effects of: (1) holes through the leading edge into a vented cavity; and (2) a scarfed, conical nozzle directed toward the centerline of the vehicle from the forward, inboard corner of the landing gear door. The simulations were generated relatively quickly and early in the investigation because simplifications were made to the leading edge cavity geometry and an existing utility to merge scarfed nozzle grid domains with structured baseline external domains was implemented. These simplifications in the breach simulations enabled: (1) a very quick grid generation procedure; and (2) high fidelity corroboration of jet physics with internal surface impingements ensuing from a breach through the leading edge, fully coupled to the external shock layer flow at flight conditions. These simulations provided early evidence that the flow through a two-inch diameter (or larger) breach enters the cavity with significant retention of external flow directionality. A normal jet directed into the cavity was not an appropriate model for these conditions at CFD Point 1 (Mach 24). The breach diameters were of the same order or larger than the local, external boundary-layer thickness. High impingement heating and pressures on the downstream lip of the breach were computed. It is likely that hole shape would evolve as a slot cut in the direction of the external streamlines. In the case of the six-inch diameter breach the boundary layer is fully ingested. The intent of externally directed jet simulations in the second scenario was to approximately model aerodynamic effects of a relatively large internal wing pressure, fueled by combusting aluminum, which deforms the corner of the landing gear door and directs a jet across the windside surface. These jet interactions, in and of themselves, were not sufficiently large to explain observed aerodynamic behavior.

Gnoffo, Peter A.↗
DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structure of the Receptor Binding Domain of EnvP(b)1, an Endogenous Retroviral Envelope Protein Expressed in Human Tissues

EnvP(b)1 is an endogenous retroviral envelope gene found in human and other primate genomes. We report EnvP(b)1 sequences in primate genomes consistent with an integration event between 40 and 71 million years ago. Using a highly specific polyclonal antiserum raised against the putative receptor binding domain (RBD) of human EnvP(b)1, we detected expression in human placenta, ovaries, and thymus. We found that EnvP(b)1 is proteolytically processed, and using cell-cell fusion assays in multiple primate cell lines, we demonstrated that extant EnvP(b)1 proteins from a variety of primate genomes are fusogenic. This work supports the idea that EnvP(b)1 is under purifying selection and its fusogenic activity has been maintained for over 40 million years. We determined the structure of the RBD of human EnvP(b)1, which defines structural similarities with extant leukemia viruses, despite little sequence conservation. This structure highlights a common scaffold from which novel receptor binding specificities likely evolved. The evolutionary plasticity of this domain may underlie the diversity of related Envs in circulating viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Spatially adaptive stochastic methods for fluid–structure interactions subject to thermal fluctuations in domains with complex geometries

We develop stochastic mixed finite element methods for spatially adaptive simulations of fluid–structure interactions when subject to thermal fluctuations. To account for thermal fluctuations, we introduce a discrete fluctuation–dissipation balance condition to develop compatible stochastic driving fields for our discretization. We perform analysis that shows our condition is sufficient to ensure results consistent with statistical mechanics. We show the Gibbs–Boltzmann distribution is invariant under the stochastic dynamics of the semi-discretization. To generate efficiently the required stochastic driving fields, we develop a Gibbs sampler based on iterative methods and multigrid to generate fields with O(N) computational complexity. Our stochastic methods provide an alternative to uniform discretizations on periodic domains that rely on Fast Fourier Transforms. To demonstrate in practice our stochastic computational methods, we investigate within channel geometries having internal obstacles and no-slip walls how the mobility/diffusivity of particles depends on location. Our methods extend the applicability of fluctuating hydrodynamic approaches by allowing for spatially adaptive resolution of the mechanics and for domains that have complex geometries relevant in many applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗