SEARCH · Search NASA

Results for “shape representation”

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.

SourceResearch connectionFrom yearThrough year

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

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Separable shape tensors for aerodynamic design

Airfoil shape design is a classical problem in engineering and manufacturing. In this work, we combine principled physics-based considerations for the shape design problem with modern computational techniques using a data-driven approach. Modern and traditional analyses of two-dimensional (2D) and three-dimensional (3D) aerodynamic shapes reveal a flow-based sensitivity to specific deformations that can be represented generally by affine transformations (rotation, scaling, shearing, and translation). We present a novel representation of shapes that decouples affine-style deformations over a submanifold and a product submanifold principally of the Grassmannian. As an analytic generative model, the separable representation, informed by a database of physically relevant airfoils, offers: (i) a rich set of novel 2D airfoil deformations not previously captured in the data, (ii) an improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal 2D shapes.

42 ENGINEERING↗

DOE OSTI2023

G2Aero: A Python package for separable shape tensors

G2Aero is a Python package for the design and deformation of discrete planar curves and tubular surfaces using a geometric data-driven approach. G2Aero utilizes a topology of product manifolds: the Grassmannian, $\mathcal{G}$($\mathcal{n}$, 2) - the set of 2-dimensional subspaces in $\mathbb{R}$ $\mathcal{n}$ - and the symmetric positive-definite (SPD) manifold, $\mathcal{S}^{2}_{++}$ - the set of 2x2 SPD matrices. The package provides a novel framework for representing separable deformations to shapes, which consist of stretching, scaling, rotating, and translating - also known as affine deformations - and a set of complementary deformations - which we refer to as undulation-type deformations. We focus on airfoil and blade design applications to emphasize the utility of the methods in an environment where the separation of affine and undulation-type deformations is critical. Notable functionalities of the framework for blade design include: 1) generating novel 2D (airfoil) shapes informed by a database of physically relevant airfoils, 2) building 3D blades by interpolating sequences of 2D airfoil cross-sections, and 3) generating blades with consistent perturbations along the blade span. We discuss the framework and provide examples in the context of wind energy applications, specifically wind turbine blade design. Figure 1 shows the wire frame obtained by interpolating airfoils defining the IEA 15-MW wind turbine blade and applying affine transformations corresponding to twist, chordal scaling, and bending. This, and all other figures in the paper, can be reproduced following examples and referencing supporting documentation provided in the G2Aero package.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

PERIODIC ORBIT FAMILIES IN THE GRAVITATIONAL FIELD OF IRREGULAR-SHAPED BODIES

The discovery of binary and triple asteroids in addition to the execution of space missions to minor celestial bodies in the past several years have focused increasing attention on periodic orbits around irregular-shaped celestial bodies. In the present work, we adopt a polyhedron shape model for providing an accurate representation of irregular-shaped bodies and employ the model to calculate their corresponding gravitational and effective potentials. We also investigate the characteristics of periodic orbit families and the continuation of periodic orbits. We prove a fact, which provides a conserved quantity that permits restricting the number of periodic orbits in a fixed energy curved surface about an irregular-shaped body. The collisions of Floquet multipliers are maintained during the continuation of periodic orbits around the comet 1P/Halley. Multiple bifurcations in the periodic orbit families about irregular-shaped bodies are also discussed. Three bifurcations in the periodic orbit family have been found around the asteroid 216 Kleopatra, which include two real saddle bifurcations and one period-doubling bifurcation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Comment on “Shape transition of unstrained flattest single-walled carbon nanotubes under pressure” [J. Appl. Phys. 115, 044512 (2014)]

Recently, Mu et al. [J. Appl. Phys. 115, 044512 (2014)] have developed an analytic approach to describe some special shapes of a single-wall carbon nanotube (SWCNT) under hydrostatic pressure. These authors have found approximate analytic expressions for the parametric equations of the tube cross section profile and its curvature at the convex-to-concave transition pressure using a shell-like 2D continuum model describing the shapes of such nanotubes. In this comment, we provide additional insight into this problem taking into account the exact analytic representation of the shapes that a SWCNT attains when subjected to hydrostatic pressure according to the very same continuum model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗

DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗

DOE OSTI2019

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Colby, Sean M↗

DOE OSTI2012

\boldmath{$^4He(e,e'p)X$ at $Q^2=2 (GeV/c)^2$ and $x_B=1.24$}

A detailed study of the $^4He(e,e'p)^3H$ reaction at large $Q^2$ and Bjorken x reveals a striking dependence of the shape of the missing energy spectrum on the missing momentum. A realistic simulation using only the kinematical constraints of the high resolution spectrometers(HRS) in Hall A of Jefferson Lab gives a very poor representation of the shape of the missing energy spectrum for the triton ground state. The large acceptances of the HRS yields a simulated width of 13 MeV for the $^3H$ ground state compared to an experimental width of 6 MeV. A significant improvement in the shape of the simulated spectrum with the data is achieved when dynamical relativistic theoretical cross sections are included in the simulation. Spectrum shapes and cross sections as a function of missing momentum will be compared to theoretical models using relativistic mean field wave functions for $^4He$ and $^3H$.

Iqbal, Sophia↗

DOE OSTI2015

Oak Ridge environmental information system (OREIS): formalizing the shapes of things on the Oak Ridge Reservation - 15393

As representations of object and area boundaries on Earth's surface, shapes are important components of geographic information system platforms. The US Department of Energy Office of Environmental Management in Oak Ridge, Tennessee recognizes the importance of shapes as boundary representations and has mandated the Oak Ridge Environmental Information System as the official repository of all geographic information system shapes related to CERCLA cleanup on the Oak Ridge Reservation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

PyFly: A fast, portable aerodynamics simulator

Here, we present a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM), namely PyFly. UVLM computes the aerodynamic loads applied on lifting surfaces while capturing the unsteady effects such as the added mass forces, the growth of bound circulation, and the wake while assuming that the flow separation location is known a priori. This method is based on discretizing the body surface into a lattice of vortex rings and relies on the Biot–Savart law to construct the velocity field at every point in the simulated domain. We introduce the pointwise approximation approach to simulate the interactions of the far-field vortices to overcome the computational burden associated with the classical implementation of UVLM. The computational framework uses the Python programming language to provide an easy to handle user interface while the computational kernels are written in Fortran. The mixed language approach enables high performance regarding solution time and great flexibility concerning easiness of code adaptation to different system configurations and applications. The computational tool predicts the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges) subject to incoming air. The aerodynamic simulator can also deal with enclosure effects, multi-body interactions, and B-spline representation of body shapes. Finally, we simulate different aerodynamic problems to illustrate the usefulness and effectiveness of PyFly.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Gaussian Lagrangian stochastic models for multi-particle dispersion

We have extended the “well-mixed” two-particle stochastic models for 3D Gaussian turbulence to n particles, and have performed calculations for clusters of n ≤ 6 particles. The particle joint motions are Gaussian and are constrained by pair-wise spatial correlations. This neglects non-Gaussian properties of the two-point velocity distribution and also neglects multi-point correlations. It also takes no account of intermittency. Although the models do not predict the growth of the separation of particles in the cluster satisfactorily, we find that they do give a good representation of the shape statistics for the cluster in comparison with direct numerical simulation results. We conclude that the pair-wise spatial structure of the turbulence accounts for most of the observed characteristics of the shape of multi-particle clusters in turbulence, and that non-Gaussian and multi-point features of the turbulence are of secondary importance.

42 ENGINEERING↗

DOE OSTI2015

Potential energy surfaces for the HBr{sup +} + CO{sub 2} → Br + HOCO{sup +} reaction in the HBr{sup +} {sup 2}Π{sub 3/2} and {sup 2}Π{sub 1/2} spin-orbit states

Quantum mechanical (QM) + molecular mechanics (MM) models are developed to represent potential energy surfaces (PESs) for the HBr{sup +} + CO{sub 2} → Br + HOCO{sup +} reaction with HBr{sup +} in the {sup 2}Π{sub 3/2} and {sup 2}Π{sub 1/2} spin-orbit states. The QM component is the spin-free PES and spin-orbit coupling for each state is represented by a MM-like analytic potential fit to spin-orbit electronic structure calculations. Coupled-cluster single double and perturbative triple excitation (CCSD(T)) calculations are performed to obtain “benchmark” reaction energies without spin-orbit coupling. With zero-point energies removed, the “experimental” reaction energy is 44 ± 5 meV for HBr{sup +}({sup 2}Π{sub 3/2}) + CO{sub 2} → Br({sup 2}P{sub 3/2}) + HOCO{sup +}, while the CCSD(T) value with spin-orbit effects included is 87 meV. Electronic structure calculations were performed to determine properties of the BrHOCO{sup +} reaction intermediate and [HBr⋯OCO]{sup +} van der Waals intermediate. The results of different electronic structure methods were compared with those obtained with CCSD(T), and UMP2/cc-pVTZ/PP was found to be a practical and accurate QM method to use in QM/MM direct dynamics simulations. The spin-orbit coupling calculations show that the spin-free QM PES gives a quite good representation of the shape of the PES originated by {sup 2}Π{sub 3/2}HBr{sup +}. This is also the case for the reactant region of the PES for {sup 2}Π{sub 1/2} HBr{sup +}, but spin-orbit coupling effects are important for the exit-channel region of this PES. A MM model was developed to represent these effects, which were combined with the spin-free QM PES.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

A 1D analysis of two high order MOC methods

The work presented here provides two different methods for evaluating angular fluxes along long characteristics. One is based off a projection of the 1D transport equation onto a complete set of Legendre polynomials, while the other uses the 1D integral transport equation to evaluate the angular flux values at specific points along each track passing through a cell. The Moment Long Characteristic (M-LC) method is shown to provide 2(P+1) spatial convergence and significant gains in accuracy with the addition of only a few spatial degrees of freedom. The M-LC method, though, is shown to be ill-conditioned at very high order and for optically thin geometries. The Point Long Characteristic (P-LC) method, while less accurate, significantly improves stability to problems with optically thin cells. The P-LC method is also more flexible, allowing for extra angular flux evaluations along a given track to give a more accurate representation of the shape along each track. This is at the expense of increasing the degrees of freedom of the system, though, and requires an increase in memory storage. This work concludes that both may be used simultaneously within the same geometry to provide the best mix of accuracy and stability possible. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Novel free-form hohlraum shape design and optimization for laser-driven inertial confinement fusion

The hohlraum shape attracts considerable attention because there is no successful ignition method for laser-driven inertial confinement fusion at the National Ignition Facility. The available hohlraums are typically designed with simple conic curves, including ellipses, parabolas, arcs, or Lame curves, which allow only a few design parameters for the shape optimization, making it difficult to improve the performance, e.g., the energy coupling efficiency or radiation drive symmetry. A novel free-form hohlraum design and optimization approach based on the non-uniform rational basis spline (NURBS) model is proposed. In the present study, (1) all kinds of hohlraum shapes can be uniformly represented using NURBS, which is greatly beneficial for obtaining the optimal available hohlraum shapes, and (2) such free-form uniform representation enables us to obtain an optimal shape over a large design domain for the hohlraum with a more uniform radiation and higher drive temperature of the fuel capsule. Finally, a hohlraum is optimized and evaluated with respect to the drive temperature and symmetry at the Shenguang III laser facility in China. The drive temperature and symmetry results indicate that such a free-form representation is advantageous over available hohlraum shapes because it can substantially expand the shape design domain so as to obtain an optimal hohlraum with high performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2010

Uncertainty quantification given discontinuous climate model response and a limited number of model runs.

Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of discontinuous model data with adjustable sharpness and structure.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity

Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Simultaneous material, shape and topology optimization

Using three design fields we develop an optimization environment that can simultaneously optimize material, shape and topology. We use the implicit representation of the boundaries with level-set functions that define the shape and topology. Differentiable R-functions allow us to combine these shapes and topology descriptions with Boolean operations. Additionally, we incorporate design dependent-stiffness materials with another design field. Notably, this framework accommodates design dependent loads, has the ability to introduce holes, and ensures the satisfaction of optimality criteria. It builds upon the fictitious domain, ersatz material, material interpolation and level-set methods. Additionally, it also borrows from parameterized density-based topology optimization methods. Since analytical sensitivities can be computed, we use efficient nonlinear programming algorithms to update the design instead of the Hamilton–Jacobi’s scheme of level-set methods. We illustrate the features of our framework by designing a cantilever beam with octet truss microlattice, a dam with design-dependent loads, and a composite clevis plate.

42 ENGINEERING↗