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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Linear and nonlinear dynamic analysis by boundary element method

An advanced implementation of the direct boundary element method (BEM) applicable to free-vibration, periodic (steady-state) vibration and linear and nonlinear transient dynamic problems involving two and three-dimensional isotropic solids of arbitrary shape is presented. Interior, exterior, and half-space problems can all be solved by the present formulation. For the free-vibration analysis, a new real variable BEM formulation is presented which solves the free-vibration problem in the form of algebraic equations (formed from the static kernels) and needs only surface discretization. In the area of time-domain transient analysis, the BEM is well suited because it gives an implicit formulation. Although the integral formulations are elegant, because of the complexity of the formulation it has never been implemented in exact form. In the present work, linear and nonlinear time domain transient analysis for three-dimensional solids has been implemented in a general and complete manner. The formulation and implementation of the nonlinear, transient, dynamic analysis presented here is the first ever in the field of boundary element analysis. Almost all the existing formulation of BEM in dynamics use the constant variation of the variables in space and time which is very unrealistic for engineering problems and, in some cases, it leads to unacceptably inaccurate results. In the present work, linear and quadratic isoparametric boundary elements are used for discretization of geometry and functional variations in space. In addition, higher order variations in time are used. These methods of analysis are applicable to piecewise-homogeneous materials, such that not only problems of the layered media and the soil-structure interaction can be analyzed but also a large problem can be solved by the usual sub-structuring technique. The analyses have been incorporated in a versatile, general-purpose computer program. Some numerical problems are solved and, through comparisons with available analytical and numerical results, the stability and high accuracy of these dynamic analysis techniques are established.

Ahmad, Shahid↗
DOE OSTI2021

Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State Ansatz

We use a neural-network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We use recurrent neural networks to describe the large deviations of the dynamical activity of model glasses, kinetically constrained models in two dimensions. We present the first finite size-scaling analysis of the large-deviation functions of the two-dimensional Fredrickson-Andersen model, and explore the spatial structure of the high-activity sector of the South-or-East model. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Genomic region associated with run timing has similar haplotypes and phenotypic effects across three lineages of Chinook salmon

Abstract Conserving life‐history variation is a stated goal of many management programs, but the most effective means by which to accomplish this are often far from clear. Early‐ and late‐migrating forms of Chinook salmon ( Oncorhynchus tshawytscha ) face unequal pressure from natural and anthropogenic forces that may alter the impacts of genetic variation underlying heritable migration timing. Genomic regions of chromosome 28 are known to be strongly associated with migration variation in adult Chinook salmon, but it remains unclear whether there is consistent association among diverse lineages and populations in large basins such as the Columbia River. With high‐throughput genotyping (GT‐seq) and phenotyping methods, we examined the association of genetic variation in 28 markers (spanning GREB1L to ROCK1 of chromosome 28) with individual adult migration timing characteristics gleaned from passive integrated transponder recordings of over 5000 Chinook salmon from the three major phylogeographic lineages that inhabit the Columbia River Basin. Despite the strong genetic differences among them in putatively neutral genomic regions, each of the three lineages exhibited very similar genetic variants in the chromosome 28 region that were significantly associated with adult migration timing phenotypes. This is particularly notable for the interior stream‐type lineage, which exhibits an earlier and more constrained freshwater entry than the other lineages. In both interior stream‐type and interior ocean‐type lineages of Chinook salmon, heterozygotes of the most strongly associated linkage groups had largely intermediate migration timing relative to homozygotes, and results indicate codominance or possibly marginal partial dominance of the allele associated with early migration. Our results lend support to utilization of chromosome 28 variation in tracking and predicting run timing in these lineages of Chinook salmon in the Columbia River.

Willis, Stuart C.↗
NASA NTRS1989

A methodology for formulating a minimal uncertainty model for robust control system design and analysis

In the design and analysis of robust control systems for uncertain plants, the technique of formulating what is termed an M-delta model has become widely accepted and applied in the robust control literature. The M represents the transfer function matrix M(s) of the nominal system, and delta represents an uncertainty matrix acting on M(s). The uncertainty can arise from various sources, such as structured uncertainty from parameter variations or multiple unstructured uncertainties from unmodeled dynamics and other neglected phenomena. In general, delta is a block diagonal matrix, and for real parameter variations the diagonal elements are real. As stated in the literature, this structure can always be formed for any linear interconnection of inputs, outputs, transfer functions, parameter variations, and perturbations. However, very little of the literature addresses methods for obtaining this structure, and none of this literature addresses a general methodology for obtaining a minimal M-delta model for a wide class of uncertainty. Since have a delta matrix of minimum order would improve the efficiency of structured singular value (or multivariable stability margin) computations, a method of obtaining a minimal M-delta model would be useful. A generalized method of obtaining a minimal M-delta structure for systems with real parameter variations is given.

Belcastro, Christine M.↗
NASA NTRS1969

Remote atmospheric probing by ground to ground line of sight optical methods

The optical effects arising from refractive-index variations in the clear air are qualitatively described, and the possibilities are discussed of using those effects for remotely sensing the physical properties of the atmosphere. The effects include scintillations, path length fluctuations, spreading of a laser beam, deflection of the beam, and depolarization. The physical properties that may be measured include the average temperature along the path, the vertical temperature gradient, and the distribution along the path of the strength of turbulence and the transverse wind velocity. Line-of-sight laser beam methods are clearly effective in measuring the average properties, but less effective in measuring distributions along the path. Fundamental limitations to the resolution are pointed out and experiments are recommended to investigate the practicality of the methods.

Lawrence, R. S.↗
DOE OSTI2017

Model mismatch analysis and compensation for modal phase measuring deflectometry

The correspondence residuals due to the discrepancy between the reality and the shape model in use are analyzed for the modal phase measuring deflectometry. Slope residuals are calculated from these discrepancies between the modal estimation and practical acquisition. Since the shape mismatch mainly occurs locally, zonal integration methods which are good at dealing with local variations are used to reconstruct the height residual for compensation. Finally, results of both simulation and experiment indicate the proposed height compensation method is effective, which can be used as a post-complement for the modal phase measuring deflectometry.

47 OTHER INSTRUMENTATION↗
NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗
DOE OSTI2022

Minimizing thickness variation in monolithic U-10Mo fuel foil and Zr interlayer during hot rolling: A microstructure-based finite element method analysis

Low-enriched uranium alloyed with 10 wt. % molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium fuel for the United States’ high performance research reactors. The monolithic U-10Mo fuel plate consists of a metallic U-10Mo fuel foil with a 25 µm Zr interlayer and a relatively thick cladding of aluminum alloy 6061. The Zr interlayer is typically applied during the hot co-rolling process, and this process dictates the uniformity of the Zr interlayer. Thickness variation observed in the U-10Mo and Zr interlayer has been attributed to several sources: the initial grain size of the U-10Mo castings, can materials, rolling temperature, inhomogeneous molybdenum content, and porosity in the cast U-10Mo. This thickness variation limits the ability to meet the dimensional specification; thus, a better understanding of the factors causing the nonuniform thickness is needed. In this work, we used a novel, microstructure-based finite element method to model the hot rolling process to address these concerns. Grain microstructures in U-10Mo were tessellated and explicitly considered in the finite element model. Each grain was assigned a random material property to mimic the grain strength variations induced by different grain orientations. Simulations were performed using six steel can thicknesses, four grain sizes, and with or without a Zr interlayer to investigate the influences of those variables on the thickness nonuniformity. The simulation results showed that a thinner steel can and finer U-10Mo grain size reduce thickness variations in both the U-10Mo fuel foil and Zr interlayer. The direct findings from the simulations and analysis can be used to optimize the hot rolling schedule, reduce fabrication defects, and meet the dimensional specifications. The proposed microstructure-based finite element model can be also coupled with experimental microstructure characterization data, images, and models to simulate multi-pass hot rolling.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Interfacial geometry and D-variation effects in two-phase systems

Numerical solutions of the governing diffusion equation for two-phase concentration dependent diffusion coefficients are examined. Solutions were also calculated for planar, cylindrical, and spherical geometries to compare the effect of interface geometries with those caused by concentration-dependent diffusion coefficients, and two methods of averaging D were considered to determine the best averaging method for different types of D-variations. The effects of interface-location criteria on mass conservation and convergence of interface location, diffusion coefficient variation in the alpha and beta-phases of a two-phase binary alloy system, effect of D(alpha) variation in a cylindrical couple on beta-phase thickness, and geometry and D-variation effects on the degree of homogenization were determined. It is concluded that typical D(alpha)-variations can have a greater influence on the kinetics of interdiffusion than the geometry.

Tenney, D. R.↗
DOE OSTI2018

Systems of Semilinear Parabolic Variational Inequalities with Time-Dependent Convex Obstacles

We consider a system of seminlinear parabolic variational inequalities with time-dependent convex obstacles. We prove the existence and uniqueness of its solution. We also provide a stochastic representation of the solution and show that it can be approximated by the penalization method. Our proofs are based upon probabilistic methods from the theory of Markov processes and the theory of backward stochastic differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Variational Excitations in Real Solids: Optical Gaps and Insights into Many-Body Perturbation Theory

We present an approach to studying optical band gaps in real solids in which quantum Monte Carlo methods allow for the application of a rigorous variational principle to both ground and excited state wave functions. In tests that include small, medium, and large band gap materials, optical gaps are predicted with a mean absolute deviation of 3.5% against experiment, less than half the equivalent errors for typical many-body perturbation theories. The approach is designed to be insensitive to the choice of density functional, a property we exploit in order to provide insight into how far different functionals are from satisfying the assumptions of many-body perturbation theory. Here, we explore this question most deeply in the challenging case of ZnO, where we show that, although many commonly used functionals have shortcomings, there does exist a one-particle basis in which perturbation theory’s zeroth-order picture is sound. Insights of this nature should be useful in guiding the future application and improvement of these widely used techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1977

Spatially resolved excitation temperature measurements in a hypersonic flow using the hook method

Following an analysis of the hook equations for variation of refractivity along a spectrograph slit a series of experiments is reported, in which CO2 was used as the test gas, and the flow variations were spatially resolved. The hooked spectra were used to measure the ground state number densities of the impurity atom of CrI present in the flow. The excitation temperature of the CrI 1-eV level was obtained from the separation of the hooks of the CrI 425.4-nm transition resonance line and simultaneously the 434.4-nm CrI eV lower level line, from the shock to the body of the hypersonic shock tunnel flow. This excitation temperature is compared with theoretical calculations of the CO2 gas temperature. Some further applications of the hook type methods to gas diagnostics are discussed.

Sandeman, R. J.↗
DOE OSTI2024

The collisional particle-in-cell method for the Vlasov–Maxwell–Landau equations

We introduce an extension of the particle-in-cell method that captures the Landau collisional effects in the Vlasov–Maxwell–Landau equations. The method arises from a regularisation of the variational formulation of the Landau equation, leading to a discretisation of the collision operator that conserves mass, charge, momentum and energy, while increasing the (regularised) entropy. The collisional effects appear as a fully deterministic effective force, thus the method does not require any transport–collision splitting. The scheme can be used in arbitrary dimension, and for a general interaction, including the Coulomb case. We validate the scheme on scenarios such as the Landau damping, the two-stream instability and the Weibel instability, demonstrating its effectiveness in the numerical simulation of plasma.

Bailo, Rafael (ORCID:0000000180183799)↗
DOE OSTI2019

Analysis of the shifted boundary method for the Stokes problem

The analysis of stability and accuracy of the shifted boundary method is developed for the Stokes flow equations. The key feature of the shifted boundary method, an embedded finite element method, is the shifting of the location where boundary conditions are applied from the true to a surrogate boundary, and an appropriate modification (shifting) of the value of the boundary conditions. An inf–sup condition is proved for the variational formulation associated to the shifted boundary method and we derive, by way of Strang’s second lemma, an optimal error estimate in the natural SBM norm. Here we also derive an L 2 -error estimate for the velocity field, by means of an extension of the Aubin–Nitsche approach to embedded, non-consistent, mixed finite element methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A higher-order Lagrangian discontinuous Galerkin hydrodynamic method for solid dynamics

We present a new multidimensional high-order Lagrangian discontinuous Galerkin (DG) hydrodynamic method that supports hypoelastic and hyperelastic strength models for simulating solid dynamics with higher-order elements. We also present new one-dimensional test problems that have an analytic solution corresponding to a hyperelastic–plastic wave. A modal DG approach is used to evolve fields relevant to conservation laws. These fields are approximated high-order Taylor series polynomials. The stress fields are represented using nodal quantities. The constitutive models used to calculate the deviatoric stress are either a hypoelastic–plastic, infinitesimal strain hyperelastic–plastic, or finite strain hyperelastic–plastic model. These constitutive models require new methods for calculating high-order polynomials for the velocity gradient and deformation gradient in an element. The plasticity associated with the strength model is determined using a radial return method to with a J 2 yield criterion and perfect plasticity. The temporal evolution of the governing equations is achieved with the total variation diminishing Runge–Kutta (TVD RK) time integration method. A diverse suite of 1D and 2D test problems are calculated. Here, the new 1D piston test problems, which have analytic solutions for each elastic–plastic model, are presented and calculated to demonstrate the stability and formal accuracy of the various models with the new Lagrangian DG method. 2D test problems are calculated to demonstrate the stability and robustness of the new Lagrangian DG method on multidimensional problems with high-order elements, which have faces that can bend.

42 ENGINEERING↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Ice Accretion Roughness Variations on a Hybrid CRM65-Midspan Wing Model

Ice accretion roughness measurements were performed in the Icing Research Tunnel (IRT) at NASA Glenn Research Center for the Hybrid CRM65-Midspan model in a range of icing conditions. The Hybrid CRM65-Midspan model was chosen for this investigation because 1) the model exhibits high sweep relative to models previously explored in the roughness investigations, 2) the model has leading edge characteristics similar to wing shapes currently used in mid-size commercial airliners, and 3) the sweep and thickness ratios relate better to hybrid lifting body designs for N+2 and N+3 vehicles than other models available. The investigation consisted of multiple sets of tests which focused on 1) 0-angle of attack cases replicating the conditions employed by Anderson et al. (1998) using both Appendix C and SLD cloud conditions, 2) cases based on the “Max Scallop” case by Broeren et al. (2016) and a “High Temperature” case with cloud properties similar to the “Max Scallop” case. Additional tests were performed 1) based on the “Max Scallop” case with variations in freestream static temperature and 2) using test section speeds near 10,000-hold flight conditions. The point clouds were characterized using the approach of McClain and Kreeger (2013) for the ice roughness variations and using the approach of McClain (2016) for the mean ice thickness variations. The resulting roughness and mean thickness variations generally follow the temporal scaling previously identified using on airfoil models without sweep, but the collapse of the time progression profiles is not as tight as found for past measurements on models without sweep. LEWICE and modified panel-method predictions were used explore spatial roughness variations and to compare to the roughness correlations developed by McClain et al. (2021) for the “Max Scallop” cases.

Icing↗
DOE OSTI2017

Reversible electrochemical modification of the surface of a semiconductor by an atomic-force microscope probe

A technique for reversible surface modification with an atomic-force-microscope (AFM) probe is suggested. In this method, no significant mechanical or topographic changes occur upon a local variation in the surface potential of a sample under the AFM probe. The method allows a controlled relative change in the ohmic resistance of a channel in a Hall bridge within the range 20–25%.

36 MATERIALS SCIENCE↗