Search NASA⌕ Search

SEARCH · Search NASA

Results for “smoothing methods”

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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Inverse methods for assessing ship-of-opportunity networks and estimating circulation and winds from tropical expendable bathythermograph data

Inverse methods for estimating the surface ciculation of the equatorial Pacific by combining a linear reduced-gravity shallow-water model with the Tropical Ocean-Global Atmosphere ship-of-opportunity expendable bathythermograph (TOGA SOP XBT) observing program are examined. It is demonstrated that a simple linear model of the upper circulation of the equatorial Pacific can be successfully used as a weak constraint when smoothing the TOGA SOP XBT data. A circulation is sought as the weighted least squares fit to the dynamics and the data. The solution method is an expansion in representer functions, and the generalized inverse problem is thereby reduced from a functional problem to an algebraic problem for the coefficients of the representer. A specific inverse calculation using synthetic forcing and data is presented.

Bennett, Andrew F.↗
NASA NTRS2012

Modular Flooring System

The modular flooring system (MFS) was developed to provide a portable, modular, durable carpeting solution for NASA fs Robotics Alliance Project fs (RAP) outreach efforts. It was also designed to improve and replace a modular flooring system that was too heavy for safe use and transportation. The MFS was developed for use as the flooring for various robotics competitions that RAP utilizes to meet its mission goals. One of these competitions, the FIRST Robotics Competition (FRC), currently uses two massive rolls of broadloom carpet for the foundation of the arena in which the robots are contained during the competition. The area of the arena is approximately 30 by 72 ft (approximately 9 by 22 m). This carpet is very cumbersome and requires large-capacity vehicles, and handling equipment and personnel to transport and deploy. The broadloom carpet sustains severe abuse from the robots during a regular three-day competition, and as a result, the carpet is not used again for competition. Similarly, broadloom carpets used for trade shows at convention centers around the world are typically discarded after only one use. This innovation provides a green solution to this wasteful practice. Each of the flooring modules in the previous system weighed 44 lb (.20 kg). The improvements in the overall design of the system reduce the weight of each module by approximately 22 lb (.10 kg) (50 %), and utilize an improved "module-to-module" connection method that is superior to the previous system. The MFS comprises 4-by-4-ft (.1.2-by- 1.2-m) carpet module assemblies that utilize commercially available carpet tiles that are bonded to a lightweight substrate. The substrate surface opposite from the carpeted surface has a module-to-module connecting interface that allows for the modules to be connected, one to the other, as the modules are constructed. This connection is hidden underneath the modules, creating a smooth, co-planar flooring surface. The modules are stacked and strapped onto durable, commercially available drywall carts for storage and/or transportation. This method of storage and transportation makes it very convenient and safe when handling large quantities of modules.

Thate, Robert↗
DOE OSTI2021

Combined Dimensional and Topology Optimization of Interior Permanent Magnet Synchronous Machine Rotors Using a Permanent Magnet Function Interpolation Method

This paper presents a magneto-structural combined dimensional and topology optimization technique for interior permanent magnet synchronous machine (IPMSM) rotors. Dimensional changes to the permanent magnet (PM) location or size are accomplished by interpolating or projecting a smoothed Heaviside rectangular function representing the presence of PM material onto the IPMSM rotor design domain mesh. A density based Solid Isotropic with Material Penalization (SIMP) topology optimization approach is then used to vary the presence of electrical steel in mesh elements to form flux barriers around the PM. The proposed method enforces a defined shape for the PM without requiring the mesh in the design domain to be deformed. Three examples are presented to demonstrate the technique: two flat bar IPMSM and one V-shaped IPMSM.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Cold Seeded Epitaxy and Flexomagnetism in GdAuGe Membranes Exfoliated From Graphene/Ge(111)

Remote and van der Waals epitaxy are promising approaches for synthesizing single crystalline membranes for flexible electronics and discovery of new properties via extreme strain; however, a fundamental challenge is that most materials do not wet the graphene surface. We develop a cold seed approach for synthesizing smooth intermetallic films on graphene that can be exfoliated to form few nanometer thick single crystalline membranes. Our seeded GdAuGe films have narrow X-ray rocking curve widths of 9–24 arc seconds, which is 2 orders of magnitude lower than their counterparts grown by typical high temperature methods, and have atomically sharp interfaces observed by transmission electron microscopy. Upon exfoliation and rippling, strain gradients in GdAuGe membranes induce an antiferromagnetic to ferri/ferromagnetic transition. Finally, our smooth, ultrathin membranes provide a clean platform for discovering new flexomagnetic effects in quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nev- ertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularities. In this paper, we explore a different angle: how deep neural networks can adapt to varying degrees of smoothness in functions and nonuni- form data distributions across different locations and scales. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation methods. This class encompasses a range of function types, such as functions with uniform regularities and discontinuous functions. We develop nonparametric approximation and estimation theories for this class using deep ReLU networks. Our results show that deep neural networks are adaptive to the nonuniform smoothness of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Advances in Automation of Overset Structured Volume Mesh Generation and Domain Connectivity

Automation of overset structured surface mesh generation has recently been accomplished by the creation of face, edge, and node meshes based on Boundary Representation solids as the geometry input. The current work continues the automation effort in overset volume mesh generation and domain connectivity based on the auto-generated surface meshes. All near-body curvilinear volume meshes are automatically generated using hyperbolic methods. Automation of this step is enabled by appropriate surface grid point distribution, and selection of boundary-splay and smoothing parameters based on concave and convex surface features. The off-body domain is covered by two automatically generated grid systems. The first contains a single Cartesian mesh with a uniform core enclosing all near-body volume meshes and stretched layers that extend to the far field, while the second consists of a set of small stretched Cartesian grids covering pockets of off-body orphan points. With high quality mesh overlap mostly guaranteed by the surface meshing scheme, orphan points that need to be covered by the second Cartesian mesh system are located away from the fine grid spacing region near the wall. Using line-segment and ray-pierce tests against the surface grids, hole-cutting is accomplished on both near and off-body volume grids resulting in appropriate clearances from the wall. The complete mesh generation automation process is demonstrated on five test cases where flow solutions are also computed and compared with solutions obtained using other methods.

TTT↗
DOE OSTI2012

Mesoscale Simulation of Proton Transport in Proton Exchange Membranes

Previous efforts to model proton transport through fuel cell membranes have largely focused on disparate length scales: molecular dynamics at the atomistic level and fuel cell stack engineering approaches at the macroscale. A new multiscale approach to bridge these extremes is proposed in this work which combines concepts from coarse-grained (CG) modeling with smoothed particle hydrodynamics (SPH) to capture the qualitative morphology and transport behavior of a proton exchange membrane at the length scale of tens of nanometers. This method allows for connection to atomistic simulations via the inclusion of transport coefficients from molecular dynamics and coarse-grained forces derived for the polymer backbone, side chain, proton, and water interactions. Information pertaining to macroscopic conductivity is obtained by volume averaging based on the flux and chemical potential fields within the membrane. Proton transport is effectively coarse-grained via introduction of a composition variable associated with each interacting site which carries the field information. By combining this technique with local electrostatics and coordinate dependent diffusion constants, the effects of double layer formation within the water pores and the influence of proximity to sulfonate groups on transport is recovered. The combined CG-SPH method is validated and subsequently applied to an equilibrated hydrated Nafion structure with a box length of 40 nm. The resulting conductivities calculated for the material agree very well with trends from experiment and provide insight into the complex interplay of morphology, proton distribution, and diffusion coefficients at a length scale that can be expanded beyond feasible atomistic molecular dynamics simulations to capture the effects of mesoscopic morphology on proton conduction.

Jorn, Ryan↗
DOE OSTI2012

Nuclear Metal Fuel Casting Modeling (Annual Report 2012)

In fiscal year 2012, FLOW3D was used to develop an understanding of the metallic nuclear fuel casting processes. There were two major accomplishments for FY12, 1) a rank-order list of the most important material properties to measure, 2) simulation of alternative mold-fill methods. We performed a design of experiments study to quantify several material properties and their impact on effective mold fill, for both circular and annular fuel rod geometries. The most important fuel material property to be measured is the wetting angle of the molten metal on the coated mold material (coated graphite). The second most important property to measure is the surface tension of the molten metal in argon gas at the design mold pressure. The magnitude of these material properties had the largest effect on whether the mold casting processes produced large voids in the fuel rods. The nominal mold fill method used in the above study was the gravity assisted top-fill method of the Idaho National Laboratory Bench Scale Casting facility. Alternative mold fill methods were investigated and using a bottom-fill method shows promise. In the bottom-fill method, the molten metal is injected into the bottom of the mold and the argon cover gas is allowed to escape through vents at the top of the mold. This allows for a smooth, uniform fill of the mold, where as the top-fill geometry has a counter-flow condition of the molten metal and argon cover gas. The mold is initially filled with argon, and using a top fill method, the molten metal must flow downward into the mold and the argon is displaced by the metal and must flow upward out of the mold. This counter-flow condition creates large voids in the cast because bubbles of argon get trapped as the metal flows into the mold. We also investigated various venting schemes for the top fill casting process and this also shows some promise to mitigate the void formation. In fiscal year 2013, we will continue to investigate mold fill approaches. We will use the results of the FY2012 work and begin investigating the segregation of binary alloys for these casting approaches and recommend separate effects testing requirements. If data becomes available from the BSC, we will calibrate the computational models and identify optimum casting parameters. Finally, we will study the feasibility of extending the calibrated model to ternary flow problem by developing appropriate scaled models.

1.02.02.02↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2016

An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations

In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

Here we propose a generalized space-time domain decomposition approach for the physics-informed neural networks (PINNs) to solve nonlinear partial differential equations (PDEs) on arbitrary complex-geometry domains. The proposed framework, named eXtended PINNs ( X P I N N s ), further pushes the boundaries of both PINNs as well as conservative PINNs (cPINNs), which is a recently proposed domain decomposition approach in the PINN framework tailored to conservation laws. Compared to PINN, the XPINN method has large representation and parallelization capacity due to the inherent property of deployment of multiple neural networks in the smaller subdomains. Unlike cPINN, XPINN can be extended to any type of PDEs. Moreover, the domain can be decomposed in any arbitrary way (in space and time), which is not possible in cPINN. Thus, XPINN offers both space and time parallelization, thereby reducing the training cost more effectively. In each subdomain, a separate neural network is employed with optimally selected hyperparameters, e.g., depth/width of the network, number and location of residual points, activation function, optimization method, etc. A deep network can be employed in a subdomain with complex solution, whereas a shallow neural network can be used in a subdomain with relatively simple and smooth solutions. We demonstrate the versatility of XPINN by solving both forward and inverse PDE problems, ranging from one-dimensional to three-dimensional problems, from time-dependent to time-independent problems, and from continuous to discontinuous problems, which clearly shows that the XPINN method is promising in many practical problems. The proposed XPINN method is the generalization of PINN and cPINN methods, both in terms of applicability as well as domain decomposition approach, which efficiently lends itself to parallelized computation. The XPINN code is available on h t t p s : / / g i t h u b . c o m / A m e y a J a g t a p / X P I N N s .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Shock-ramp analysis test problem

Quasi-isentropic (ramp) compression is now a well-established experimental method and so are the analysis techniques to give Lagrangian sound speed, pressure, and density along the sample material's isentrope. A shock followed by ramp compression is a natural extension to investigate, for example, shock melt and refreeze on compression, or isentropes of states off the Hugoniot or principal isentrope. In practice, graded-density impactors produce initial shocks, compression by shaped laser pulses may be unable to produce a smooth pressure increase from zero, and incidental perturbations on the drive pulse may also give rise to shocks, so robust shock-ramp analysis methods will be needed. Appropriate analysis methods are needed for shock-ramp experiments, based on those for quasi-isentropic compression, and these require validation. This paper describes three different analyses of a shock-ramp test problem, including an assessment of their estimated errors. The methods tested were based on hydrodynamic characteristics or integration backward in space. All methods gave the known Lagrangian sound speed to within ~1%, and pressure and volume to within less than 2% and 1%, demonstrating that the analysis methods of isentropic compression experiments can be confidently extended to the analysis of shock and ramp compression.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Bootstrap AMG for spectral clustering

Graph Laplacian is a popular tool for analyzing graphs, particularly in graph partitioning and clustering. Given a notion of similarity (via an adjacency matrix), graph clustering refers to identifying different groups such that vertices in the same group are more similar compared to vertices across different groups. Data clustering can be reformulated in terms of a graph clustering problem when the given set of data is represented as a graph, also known as similarity graph. In this context, eigenvectors of the graph Laplacian are often used to obtain a new geometric representation of the original data set that generally enhances cluster properties and improves cluster detection. Here, we apply a bootstrap algebraic multigrid (AMG) method that constructs a set of vectors associated with the graph Laplacian. These vectors, referred to as algebraically smooth ones, span a low-dimensional Euclidean space, which we use to represent the data, enabling cluster detection both in synthetic and in realistic well-clustered graphs. We show that, in the case of a good quality bootstrap AMG, the computed smooth vectors employed in the construction of the final AMG operator, which by construction is spectrally equivalent to the originally given graph Laplacian, accurately approximate the space in the lower portion of the spectrum of the preconditioned operator. Thus, our approach can be viewed as a spectral clustering technique associated with the generalized spectral problem (Laplace operator versus the final AMG operator), and hence, it can be seen as an extension of the classical spectral clustering that employs a standard eigenvalue problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

J-variational inequalities and zeroes of a family of maximal monotone operators by sunny generalized nonexpansive retraction

In this paper, using generalized resolvents and retractions onto shrinking closed subsets, we present new extragradient algorithms for finding the solution of a J-variational-like inequality, the zero points of a family of maximal monotone operators, and the solution set of mixed equilibrium problems for a J-α-inverse-strongly monotone-like operator in a 2-uniformly convex and 2-uniformly smooth Banach space. By introducing the new definitions, we prove strong convergence of generated iterates in the extragradient methods. Using FMINCON optimization toolbox in MATLAB, we give some numerical examples and compare them with several existence results in the literature to illustrate the usability of our results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Muon reconstruction in the Daya Bay water pools

Muon reconstruction in the Daya Bay water pools would serve to verify the simulated muon fluxes and offer the possibility of studying cosmic muons in general. This reconstruction is, however, complicated by many optical obstacles and the small coverage of photomultiplier tubes (PMTs) as compared to other large water Cherenkov detectors. The PMTs’ timing information is useful only in the case of direct, unreflected Cherenkov light. This requires PMTs to be added and removed as an hypothesized muon trajectory is iteratively improved, to account for the changing effects of obstacles and direction of light. Therefore, muon reconstruction in the Daya Bay water pools does not lend itself to a general fitting procedure employing smoothly varying functions with continuous derivatives. Here, we describe an algorithm which overcomes these complications. It employs the method of Least Mean Squares to determine an hypothesized trajectory from the PMTs’ charge-weighted positions. This initially hypothesized trajectory is then iteratively refined using the PMTs’ timing information. Reconstructions with simulated data reproduce the simulated trajectory to within about 5° in direction and about 45 cm in position at the pool surface, with a bias that tends to pull tracks away from the vertical by about 3°.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Quantifying Temperature Dependence of Electronic Band Gaps and Optical Properties in SnO 2 and SnO via First-Principles Simulations

Tin metal oxides SnO x ( x = 1, 2) have gained interest as gas-sensing materials. For their applications as high-temperature sensors, however, a better understanding of their temperature dependence sensing responses is needed. Here, we comparatively quantify the temperature-dependent electronic band gaps and optical properties of SnO 2 and SnO using first-principles calculations. Without considering the temperature effect, SnO 2 and SnO are predicted to have direct and indirect band gaps of 2.18 and 1.66 eV, respectively, at the PBE + U -GGA level that we employed. The temperature effect on the electronic and optical properties is captured by taking account of the electron–phonon interaction. Band gap renormalization with temperature is calculated via the Allen–Heine–Cardona theory. For both oxides, we find a monotonic decrease in the electronic band gap such that renormalization at zero point (0 K) is ~–0.17 and ~–0.52 eV at 1000 K. These results are also analyzed by employing an analytical equation that helps characterize the band gap shift with temperature. In addition, the optical properties at finite temperatures are simulated using the frozen-phonon method that combines electron–phonon coupling with the momentum matrix. As temperature increases, the optical property spectra are smoothed because of the smearing effect, which diminishes optical constants at shorter wavelengths. Our results are of interest for high-temperature functional materials in applications of optical detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Thermodynamical stability of the Bardeen black hole

We analyze the stability of the regular magnetic Bardeen black hole both thermodynamically and dynamically. For the thermodynamical analysis we consider a microcanonical ensemble and apply the turning point method. This method allows to decide a change in stability (or instability) of a system, requiring only the assumption of smoothness of the area functional. The dynamical stability is asserted using criteria based on the signs of the Lagrangian and its derivatives. It turns out from our analysis that the Bardeen black hole is both thermodynamically and dynamically stable.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A generalized Poisson solver for first-principles device simulations

Electronic structure calculations of atomistic systems based on density functional theory involve solving the Poisson equation. In this paper, we present a plane-wave based algorithm for solving the generalized Poisson equation subject to periodic or homogeneous Neumann conditions on the boundaries of the simulation cell and Dirichlet type conditions imposed at arbitrary subdomains. In this way, source, drain, and gate voltages can be imposed across atomistic models of electronic devices. Dirichlet conditions are enforced as constraints in a variational framework giving rise to a saddle point problem. The resulting system of equations is then solved using a stationary iterative method in which the generalized Poisson operator is preconditioned with the standard Laplace operator. The solver can make use of any sufficiently smooth function modelling the dielectric constant, including density dependent dielectric continuum models. For all the boundary conditions, consistent derivatives are available and molecular dynamics simulations can be performed. The convergence behaviour of the scheme is investigated and its capabilities are demonstrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗