SEARCH · Search NASA
Results for “geometrical”
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.
Geometric quantum adiabatic methods for quantum chemistry
Not Available
Robust Two-Qubit Geometric Phase Gates using Amplitude and Frequency Ramping
Explore the source record for details and available documents.
Development of a scalar-based geometric parameterization approach for the crystal structure landscape of dithienylethene-based crystalline solids
Dithienylethenes (DTEs) are a promising class of organic photoswitches that can be used to create crystalline solids with properties controlled by light. However, the ability of DTEs to adopt multiple conformations, only one of which is photoactive, complicates the rational design of these materials. Herein, the synthesis and structural characterization of 19 crystalline solids containing a single DTE molecule are described. A novel D – D analysis of the molecular geometries obtained from rotational potential energy surface calculations and the ensemble of experimental structures were used to construct a crystal landscape for DTE. Of the 19 crystal structures, 17 contained photoinactive DTE rotamers and only 2 were photoactive. These results highlight the challenges associated with the design of these materials. Overall, the D – D analysis described herein provides rapid, effective and intuitive means of linking the molecular structure to photoactivity that could be applied more broadly to afford a general strategy for producing photoactive diarylethene-based crystalline solids.
3D Printed Alumina for Geometrically-Complex Electronic Substrates With High-Performance Printed Conductors
Not Available
Efficient Data Collection in IoT Networks Using Trajectory Encoded With Geometric Shapes
Not provided.
Local Latent Representation Based on Geometric Convolution for Particle Data Feature Exploration
Not provided.
Resonant ultrasound spectroscopy for anisotropic materials with misaligned geometric and material axes.
Explore the source record for details and available documents.
Geometric deep learning of RNA structure
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
Direct geometric probe of singularities in band structure
A quantum system’s energy landscape may have points where multiple energy surfaces are degenerate and that exhibit singular geometry of the wave function manifold, with major consequences for the system’s properties. Ultracold atoms in optical lattices have been used to indirectly characterize such points in the band structure. Here we measured the non-Abelian transformation produced by transport directly through the singularities. We accelerated atoms along a quasi-momentum trajectory that enters, turns, and then exits the singularities at linear and quadratic band-touching points of a honeycomb lattice. Measurements after transport identified the topological winding numbers of these singularities to be 1 and 2, respectively. Our work introduces a distinct method for probing singularities that enables the study of non-Dirac singularities in ultracold-atom quantum simulators.
Geometric Interpretation of the Cluster Location Problem Part I: Theory
We present a new framing of the seismic location problem using principles drawn from differential geometry. Our interpretation relies upon the common assumption that travel times observed across a network are continuous, differentiable functions of source location. In consequence, travel‐time functions constitute a differentiable map between the source region and a Riemannian manifold. The manifold is said to be the image of the source region embedded in a generally high‐dimension travel‐time vector space. A cluster of events in the source region has an image of discrete points on the manifold, that, except in the simplest cases, cannot be viewed directly. However, it is possible to project the image of a cluster into a tangent space of the manifold for direct visualization. The projection operator can be computed directly from the data without a velocity model, but produces a distorted rendering of the cluster geometry. With a model we can predict the distortions and correct them to estimate cluster geometry. We develop these points with the simplest possible example, one for which direct visualization of the manifold is possible, using the example as an introduction to the relevant concepts from differential geometry in a familiar setting. The tangent space, a local linearization of the manifold, plays a key role. We develop a metric to estimate the limits of linearization, that is, to determine when the curvature of the manifold invalidates the linear assumption. We also examine the interplay of model error, inadequate network geometry, and pick error. We then generalize our results from the simple case to the general case of 3D source regions observed by general networks. Although we do suggest a new “project and correct” method for location, we do not develop it into a practical algorithm. In conclusion, our intention rather is to highlight new analytical methods grounded in differential geometry.
Geometric surprises in the Python's lunch conjecture
A bulge surface, on a time reflection-symmetric Cauchy slice of a holographic spacetime, is a non-minimal extremal surface that occurs between two locally minimal surfaces homologous to a given boundary region. According to the python’s lunch conjecture of Brown et al., the bulge’s area controls the complexity of bulk reconstruction, in the sense of the amount of post-selection that needs to be overcome for the reconstruction of the entanglement wedge beyond the outermost extremal surface. We study the geometry of bulges in a variety of classical spacetimes, and discover a number of surprising features that distinguish them from more familiar extremal surfaces such as Ryu-Takayanagi surfaces: they spontaneously break spatial isometries, both continuous and discrete; they are sensitive to the choice of boundary infrared regulator; they can self-intersect; and they probe entanglement shadows, orbifold singularities, and compact spaces such as the sphere in AdS _p× S^q p × S q . These features imply, according to the python’s lunch conjecture, novel qualitative differences between complexity and entanglement in the holographic context. We also find, surprisingly, that extended black brane interiors have a non-extensive complexity; similarly, for multi-boundary wormhole states, the complexity pleateaus after a certain number of boundaries have been included.
Optimization of Geometric Perturbations on a Rod Moving Through a High Explosive Target
After completing a study to ensure the simulation results were converged, several high resolution 3D Smoothed Particle Hydrodynamic (SPH) simulations of copper rods impacting a high explosive (LX14) target were performed. This was then formulated into an optimization problem: I wanted to find the optimum shape and location of a perturbation on the rod that would maximize its erosion after it left the target. The shape of the perturbation was modeled as a 2D Gaussian bump and parameterized by its location along the rod axis (z 0 ) and amplitude (A). The final mass of the coherent part of the rod as it leaves the target was used as a metric to represent the erosion of the rod, and the optimization was formulated to maximize this metric with respect to the aforementioned design variables. Due to the expensive nature of the high-fidelity 3D SPH simulations, a surrogate model needed to be chosen so that many function calls to the optimizer would be feasible. Thus, a strategic full factorial sampling plan was chosen to build a dataset, which consisted of 24 high-fidelity simulations. Two surrogate models, a third order polynomial regression model and a Gaussian Process Model, were analyzed using a 14%/86% test/train holdout technique. The root mean square and R2 score of the test set was used to determine the best model, and the third order polynomial regression model was chosen as the surrogate model. Finally, the Nelder-Mead Simplex and Basin-hopping optimization algorithms were implemented, and it was found that the two algorithms gave slightly different optimum values. Nelder-Mead gave an optimum point of [z* 0 ;A*] = [9:9;0:4] and Basin-Hopping gave an optimum value of x* = [z* 0 ;A*] = [9:2;0:1].
Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation
Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.
Transient Deformation in Additively Manufactured 316L Stainless Steel Lattices Characterized with in-situ X-ray Phase Contrast Imaging: The Complete Dataset for Three Geometrical Lattices
Metallic lattice structures are being considered for shock mitigation applications due to their superior mechanical properties, energy absorption capability and lightweight characteristics inherent of the additive manufacturing process. In this study, shock compression experiments coupled to x-ray phase contrast imaging (PCI) were conducted on 316L stainless steel lattices. Meso-scale simulations incorporating the as-built lattice structure characterized by computed tomography were used to simulate PCI radiographs in CTH for direct comparison to experimental data. The methodology presented here offers robust validation for constitutive properties to further our understanding of lattice compaction at application-relevant strain rates.
A Geometrical Feature of a Travelling Concave Detonation Front
Experiments show that when two HE materials placed adjacently, a fast-propagating detonation in one of the HE regions can sometimes initiate a detonation in the other HE. Direct numerical simulations support this phenomenon. Let A and B stand for the two HE materials. In a typical case, the driving detonation in HE A has a propagation speed faster than the CJ velocity in HE B, and the induced detonation in HE B is necessarily concave.