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Results for “Iterative 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.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Diagonal and circulant or skew-circulant splitting preconditioners for spatial fractional diffusion equations

We propose three new preconditioners: diagonal and optimal-circulant splitting preconditioner, diagonal and skew-circulant splitting preconditioner, and diagonal and optimal-skew-circulant splitting preconditioner for solving the diagonal-plus-Toeplitz linear system discretized from the spatial fractional diffusion equations. Theoretical analysis shows that these three preconditioners can make the eigenvalues of the preconditioned matrices be clustered around 1, especially when the grids of the discretizations are refined. These results coincide with the one about the diagonal and circulant splitting preconditioner constructed recently by Bai et al. (Numer Linear Algebra Appl 24:e2093, 2017). Numerical experiments exhibit that the proposed preconditioners can significantly improve the convergence of the Krylov subspace iteration methods like GMRES and BiCGSTAB, and they outperform the diagonal and circulant splitting preconditioner as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2012

The influence of external fields on the energy of two interacting electrons in a quantum dot

In this paper, the effects of both an external electric field and an external magnetic field on the energy of two interacting electrons in a two-dimensional parabolic quantum dot are investigated for various quantum states (n,|m|) in the framework of the asymptotic iteration method. It is seen that the energy eigenvalues increase monotonically with increasing electric and magnetic field strengths in the weak- and strong-field regimes. However, the behavior is slightly different in the two regimes. Whereas the energy values increase linearly in the low-electric-field regime, they increase much more than linearly in the strong-field regime. This paper demonstrates that it is possible to obtain the energy eigenvalues of two electrons in a two-dimensional parabolic quantum dot not only for cases with and without an electric field and with and without a magnetic field, but also for cases where magnetic and electric fields (either strong or weak) are present simultaneously. - Highlights: Black-Right-Pointing-Pointer The energy eigenvalues of two electrons in a 2D quantum dot in external electric and magnetic fields are discussed. Black-Right-Pointing-Pointer The effects of the fields on the energies are obtained for strong and weak fields. Black-Right-Pointing-Pointer The energies increase monotonically with increasing field. Black-Right-Pointing-Pointer The increase in the energies in weak fields has a different behavior from that in strong fields. Black-Right-Pointing-Pointer The method used here is very efficient for discovering the effects of external fields on different states.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

CFD modeling of multiphase reacting flow in blast furnace shaft with layered burden

The ironmaking blast furnace is a counter-current chemical reactor which includes the ascending gas flow and the counter-current descending porous bed (burden). A Computational Fluid Dynamics (CFD) model has been developed to simulate the multiphase reacting flow in blast furnace shaft. The gas flow dynamics, burden movement, chemical reactions, heat and mass transfer between the gas phase and burden phase are included in the CFD model. The blast furnace burden consists of alternative layers of iron ore and coke. We propose a novel methodology in order to efficiently model the effects of alternative burden layer structure on gas flow, heat transfer, mass transfer and chemical reactions. Different reactions and heat transfer characteristics are applied for difference types of layer. In addition, the layered CFD model accurately predicts the Cohesive Zone (CZ) shape where the melting of solid burden taking place. Furthermore, we determine the shape and location of the CZ by an iterative method based on the ore temperature distribution. The theoretical formation and the methodology of the CFD model are presented and the model is applied to simulate industry blast furnaces. Finally, the proposed method can be applied to investigate the blast furnace shaft process and other moving bed system with periodic burden structure configuration.

42 ENGINEERING↗
DOE OSTI2022

Effective properties of semitransparent radiative cooling materials with spectrally variable properties

Radiative cooling is a promising solid-state, non-vapor-compression technology for passive refrigeration and air conditioning. Although this phenomenon occurs naturally, achieving a significant amount of cooling to make it a technically and economically viable technology requires highly engineered, spectrally selective radiative surfaces. These characteristics make radiative cooling difficult to estimate, particularly when it is integrated with other systems such as photovoltaic panels or building envelopes. The complexity further increases when the substrate also participates in the radiative cooling (along with the radiative coating). Energy estimation is becoming increasingly critical because of the recent focus on the semitransparent radiative coatings that transmit a variety of colors to enhance the aesthetic appeal of the system. In this work, we propose an iterative method to calculate the effective radiative properties, which provides the same net radiative cooling that would be observed using the spectral properties at both the coating and substrate surfaces. Compared to traditional methods that rely on either computationally expensive full spectral analysis or methods for averaging each radiative surface parameter locally, our proposed method focuses on calculating effective properties that provide the same the net cooling effect as a full spectral analysis by accounting the emissivity, absorptivity, and transmissivity collectively, thereby providing an overall estimation error of less than 0.2%. We believe that this study will be beneficial to the engineering communities that employ complex simulation codes and require lumped solar and thermal radiation related parameters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A scalable block-preconditioning strategy for divergence-conforming B-spline discretizations of the Stokes problem

The recently introduced divergence-conforming B-spline discretizations allow the construction of smooth discrete velocity–pressure pairs for viscous incompressible flows that are at the same time inf–sup stable and pointwise divergence-free. When applied to the discretized Stokes problem, these spaces generate a symmetric and indefinite saddle-point linear system. The iterative method of choice to solve such system is the Generalized Minimum Residual Method. This method lacks robustness, and one remedy is to use preconditioners. For linear systems of saddle-point type, a large family of preconditioners can be obtained by using a block factorization of the system. In this paper, we show how the nesting of “black-box” solvers and preconditioners can be put together in a block triangular strategy to build a scalable block preconditioner for the Stokes system discretized by divergence-conforming B-splines. Lastly, besides the known cavity flow problem, we used for benchmark flows defined on complex geometries: an eccentric annulus and hollow torus of an eccentric annular cross-section.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Here, this work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterative method, a lower-fidelity model, or a projection-based reduced-order model, for example. The proposed statistical model comprises the sum of a deterministic regression-function model and a stochastic noise model. The method constructs the regression-function model by applying regression techniques from machine learning (e.g., support vector regression, artificial neural networks) to map features (i.e., error indicators such as sampled elements of the residual) to a prediction of the approximate-solution error. The method constructs the noise model as a mean-zero Gaussian random variable whose variance is computed as the sample variance of the approximate-solution error on a test set; this variance can be interpreted as the epistemic uncertainty introduced by the approximate solution. This work considers a wide range of feature-engineering methods, data-set-construction techniques, and regression techniques that aim to ensure that (1) the features are cheaply computable, (2) the noise model exhibits low variance (i.e., low epistemic uncertainty introduced), and (3) the regression model generalizes to independent test data. Finally, numerical experiments performed on several computational-mechanics problems and types of approximate solutions demonstrate the ability of the method to generate statistical models of the error that satisfy these criteria and significantly outperform more commonly adopted approaches for error modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Transfer reaction code with nonlocal interactions

Here, we present a suite of codes (NLAT for nonlocal adiabatic transfer) to calculate the transfer cross section for single-nucleon transfer reactions, (d,N) or (N,d), including nonlocal nucleon-target interactions, within the adiabatic distorted wave approximation. For this purpose, we implement an iterative method for solving the second order nonlocal differential equation, for both scattering and bound states. The final observables that can be obtained with NLAT are dif- ferential angular distributions for the cross sections of A(d,N)B or B(N,d)A. Details on the implementation of the T-matrix to obtain the final cross sections within the adiabatic distorted wave approximation method are also provided. This code is suitable to be applied for deuteron induced reactions in the range of E d = 10–70 MeV, and provides cross sections with 4% accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An efficient transport solver for tokamak plasmas

A simple approach to efficiently solve a coupled set of 1-D diffusion-type transport equations with a stiff transport model for tokamak plasmas is presented based on the 4th order accurate Interpolated Differential Operator scheme along with a nonlinear iteration method derived from a root-finding algorithm. Here, numerical tests using the Trapped Gyro-Landau-Fluid model show that the presented high order method provides an accurate transport solution using a small number of grid points with robust nonlinear convergence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

TTDFT: A GPU accelerated Tucker tensor DFT code for large-scale Kohn-Sham DFT calculations

We present the Tucker tensor DFT (TTDFT) code which uses a tensor-structured algorithm with graphic processing unit (GPU) acceleration for conducting ground-state DFT calculations on large-scale systems. The Tucker tensor DFT algorithm uses a localized Tucker tensor basis computed from an additive separable approximation to the Kohn-Sham Hamiltonian. The discrete Kohn-Sham problem is solved using Chebyshev filtered subspace iteration method that relies on matrix-matrix multiplications of a sparse symmetric Hamiltonian matrix and a dense wavefunction matrix, expressed in the localized Tucker tensor basis. These matrix-matrix multiplication operations, which constitute the most computationally intensive step of the solution procedure, are GPU accelerated providing ~8-fold GPU-CPU speedup for these operations on the largest systems studied. In conclusion, the computational performance of the TTDFT code is presented using benchmark studies on aluminum nano-particles and silicon quantum dots with system sizes ranging up to ~7,000 atoms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Precision measurement of relative permittivity of aluminum oxide for a high power resonant waveguide window with low return loss

An iterative method of estimating permittivity and window thickness with high precision is developed for half wavelength resonant waveguide windows for use in a lower hybrid launcher for DIII-D. The high Q factor of half-wavelength windows results in extreme sensitivity of return loss on small deviations in permittivity. Permittivity and loss tangent measurements of Morgan AL-995 99.5% aluminum oxide ceramic using a waveguide resonant cavity per ASTM D2520-13 determine variation of permittivity with frequency. Measurement of window resonant frequency in conjunction with the slope of permittivity from waveguide cavity measurements determine required thickness of a resonant window within 5 MHz of the desired resonant frequency. Methods to improve measurement accuracy of resonant peaks on a vector network analyzer are discussed. A sensitivity study to dimensional variations in window or sample rod geometry, and shift in resonant frequency due to RF heating of the window material is conducted.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

A stochastic approach to uncertainty in the equations of MHD kinematics

The magnetohydrodynamic (MHD) kinematics model describes the electromagnetic behavior of an electrically conducting fluid when its hydrodynamic properties are assumed to be known. In particular, the MHD kinematics equations can be used to simulate the magnetic field induced by a given velocity field. While prescribing the velocity field leads to a simpler model than the fully coupled MHD system, this may introduce some epistemic uncertainty into the model. If the velocity of a physical system is not known with certainty, the magnetic field obtained from the model may not be reflective of the magnetic field seen in experiments. Additionally, uncertainty in physical parameters such as the magnetic resistivity may affect the reliability of predictions obtained from this model. By modeling the velocity and the resistivity as random variables in the MHD kinematics model, we seek to quantify the effects of uncertainty in these fields on the induced magnetic field. We develop stochastic expressions for these quantities and investigate their impact within a finite element discretization of the kinematics equations. We obtain mean and variance data through Monte Carlo simulation for several test problems. Toward this end, we develop and test an efficient block preconditioner for the linear systems arising from the discretized equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Computing resonant modes of accelerator cavities by solving nonlinear eigenvalue problems via rational approximation

Here, we present an efficient and reliable algorithm for solving a class of nonlinear eigenvalue problems arising from the modeling of particle accelerator cavities. The eigenvalue nonlinearity in these problems results from the use of waveguides to couple external power sources or to allow certain excited electromagnetic modes to exit the cavity. We use a rational approximation to reduce the nonlinear eigenvalue problem first to a rational eigenvalue problem. We then apply a special linearization procedure to turn the rational eigenvalue problem into a larger linear eigenvalue problem with the same eigenvalues, which can be solved by existing iterative methods. By using a compact scheme to represent both the linearized operator and the eigenvectors to be computed, we obtain a numerical method that only involves solving linear systems of equations of the same dimension as the original nonlinear eigenvalue problem. We refer to this method as a compact rational Krylov (CORK) method. We implemented the CORK method in the Omega3P module of the Advanced Computational Electromagnetic 3D Parallel (ACE3P) simulation suite and validated it by comparing the computed cavity resonant frequencies and damping Q factors of a small model problem to those obtained from a fitting procedure that uses frequency responses computed by another ACE3P module called S3P. We also used the CORK method to compute trapped modes damped in an ideal eight 9-cell SRF cavity cryomodule. This was the first time it was possible to compute these modes directly. The damping Q factors of the computed modes match well with those measured in experiments and the difference in resonant frequencies is within the range introduced by cavity imperfection. Therefore, the CORK method is an extremely valuable tool for computational cavity design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Numerically approximating centrality for graph ranking guarantees

Many real-world datasets can be represented as graphs. Using iterative solvers to approximate graph centrality measures allows us to obtain a ranking vector on the nodes of the graph, consisting of a number for each vertex in the graph identifying its relative importance. In this study the centrality measures we use are Katz Centrality and PageRank. Given an approximate solution, we use the residual to accurately estimate how much of the ranking matches the ranking given by the exact solution. Using probabilistic matrix norms, we obtain bounds on the accuracy of the approximation compared to the exact solution with respect to the highly ranked nodes and apply numerical analysis to the computation of centrality with iterative methods. This relates the numerical accuracy of the linear solver to the data analysis accuracy of finding the correct ranking. In particular, we answer the question of which pairwise rankings are reliable given an approximate solution to the linear system. Experiments on many real-world undirected and directed networks up to several million vertices and several hundred million edges validate our theory and show that we are able to accurately estimate large portions of the approximation. We also analyze the difference between global centrality scores and personalized scores (w.r.t. specific seed vertices). By analyzing convergence error, we develop confidence in the ranking schemes of data mining. We show we are able to accurately guarantee ranking of vertices with an approximation to centrality metrics faster than current methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Robust Numerical Treatment of Solid-Phase Diffusion in Pseudo Two-Dimensional Lithium-Ion Battery Models

Solid-phase diffusion in active materials of lithium-ion batteries significantly affects charging and safety-related behavior of lithium-ion batteries. Therefore, it is essential to develop an efficient and robust numerical algorithm for solving solid-phase diffusion equations in physics-based battery models. In this work, we discuss the origins of numerical instabilities that can occur when solving the solid-phase diffusion equations using iterative methods. Then, in order to resolve such issues, we propose a simple numerical treatment to the surface flux term of discretized solid-phase diffusion equations. To demonstrate its numerical robustness, the proposed method is implemented into a pseudo two-dimensional (P2D) physics-based battery model and simulations are conducted at wide ranges of operating conditions. Even with extremely poor initial guesses for the Li+ concentrations of the active materials, computations using the proposed method do not diverge and the their computational speeds are comparable to those with conventional initial guesses. Comprehensive tests of the proposed method are also performed with a dynamic current profile based on US06 driving profile and a multi-stage charging profile with very high initial C-rate (12C).

battery modeling↗
DOE OSTI2015

Model for Deriving the Direct Normal and Diffuse Horizontal Irradiance from the Global Tilted Irradiance

We developed a separation model for deriving the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the global tilted irradiance (GTI). The model is based on the DIRINT model, but substitutes the use of the GTI in place of the global horizontal irradiance (GHI) for determining the global clearness index (Kt). Additionally, an iterative method is used to adjust Kt to improve the derived values of the DNI and the DHI. The model is referred to as the GTI-DIRINT model. When using for model input the GTI measured with a pyranometer tilted at a small angle from the horizontal (10°), the deviations between the measured DNI and DHI and the GTI-DIRINT-modeled DNI and DHI were essentially the same as those for the DIRINT model when using the GHI for model input. Increasing the tilt angle from horizontal (as great as 44° for Eugene, Oregon) increased the deviations between the modeled and measured DNI and DHI, but our results were still reasonable. The GTI-DIRINT model provides reasonable values of DNI and DHI from the GTI, and if they are subsequently used with the Perez tilted surface model to model the GTI for orientations within 30° of the azimuth and tilt of the GTI used for model input, the result is as good or better than may be obtained when using a measured DNI and DHI, and without their significant instrumentation cost.

14 SOLAR ENERGY↗
DOE OSTI2020

Improved Fast Randomized Iteration Approach to Full Configuration Interaction

In this work, we present three modifications to our recently introduced fast randomized iteration method for full configuration interaction (FCI-FRI) and investigate their effects on the method’s performance for Ne, H 2 O, and N 2 . The initiator approximation, originally developed for full configuration interaction quantum Monte Carlo, significantly reduces statistical error in FCI-FRI when few samples are used in compression operations, enabling its application to larger chemical systems. The semistochastic extension, which involves exactly preserving a fixed subset of elements in each compression, improves statistical efficiency in some cases but reduces it in others. We also developed a new approach to sampling excitations that yields consistent improvements in statistical efficiency and reductions in computational cost. We discuss possible strategies based on our findings for improving the performance of stochastic quantum chemistry methods more generally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Mesoscale Phase-Field Modeling of Charge Transport in Nanocomposite Electrodes for Lithium-ion Batteries

A phase-field model is developed to investigate the influence of microstructure, thermodynamic and kinetic properties, and charging conditions on charged particle transport in nanocomposite electrodes. Two sets of field variables are used to describe the microstructure. One is comprised of the order parameters describing size, orientation and spatial distributions of nanoparticles, and the other is comprised of the concentrations of mobile species. A porous nanoparticle microstructure filled with electrolyte is taken as a model system to test the phase-field model. Inhomogeneous and anisotropic dielectric constants and mobilities of charged particles, and stresses associated with lattice deformation due to Li-ion insertion/extraction are considered in the model. Iteration methods are used to find the elastic and electric fields in an elastically and electrically inhomogeneous medium. The results demonstrate that the model is capable of predicting charge separation associated with the formation of a double layer at the electrochemical interface between solid and electrolyte, and the effect of microstructure, inhomogeneous and anisotropic thermodynamic and kinetic properties, charge rates, and stresses on voltage versus current density and capacity during charging and discharging.

Hu, Shenyang Y↗