scholarly journals Locally-synchronous, iterative solver for Fourier-based homogenization

2021 ◽  
Author(s):  
R. Glüge ◽  
H. Altenbach ◽  
S. Eisenträger
Keyword(s):  
Linear System ◽  
Complete Solution ◽  
Iterative Solvers ◽  
Iterative Solver ◽  
System Matrix ◽  
Solution Vector ◽  
Homogenization Problem ◽  
Homogenization Approach ◽  
Large Linear System ◽  
Constitutive Tensor

AbstractWe use the algebraic orthogonality of rotation-free and divergence-free fields in the Fourier space to derive the solution of a class of linear homogenization problems as the solution of a large linear system. The effective constitutive tensor constitutes only a small part of the solution vector. Therefore, we propose to use a synchronous and local iterative method that is capable to efficiently compute only a single component of the solution vector. If the convergence of the iterative solver is ensured, i.e., the system matrix is positive definite and diagonally dominant, it outperforms standard direct and iterative solvers that compute the complete solution. It has been found that for larger phase contrasts in the homogenization problem, the convergence is lost, and one needs to resort to other linear system solvers. Therefore, we discuss the linear system’s properties and the advantages as well as drawbacks of the presented homogenization approach.

Adaptive complex frequency with V-cycle GMRES for preconditioning 3D Helmholtz equation

Geophysics ◽  
2021 ◽  
pp. 1-40
Author(s):  
Wenhao Xu ◽  
Yang Zhong ◽  
Bangyu Wu ◽  
Jinghuai Gao ◽  
Qing Huo Liu
Keyword(s):  
Linear System ◽  
Helmholtz Equation ◽  
3D Models ◽  
Numerical Results ◽  
Spatial Sampling ◽  
Gmres Method ◽  
Complex Frequency ◽  
Iterative Solver ◽  
Helmholtz Equations ◽  
Sampling Density

Solving the Helmholtz equation has important applications in various areas, such as acoustics and electromagnetics. Using an iterative solver together with a proper preconditioner is key for solving large 3D Helmholtz equations. The performance of existing Helmholtz preconditioners usually deteriorates when the minimum spatial sampling density is small (approximately four points per wavelength [PPW]). To improve the efficiency of the Helmholtz preconditioner at a small minimum spatial sampling density, we have adopted a new preconditioner. In our scheme, the preconditioning matrix is constructed based on an adaptive complex frequency that varies with the minimum spatial sampling density in terms of the number of PPWs. Furthermore, the multigrid V-cycle with a GMRES smoother is adopted to effectively solve the corresponding preconditioning linear system. The adaptive complex frequency together with a GMRES smoother can work stably and efficiently at different minimum spatial sampling densities. Numerical results of three typical 3D models show that our scheme is more efficient than the multilevel GMRES method and shifted Laplacian with multigrid full V-cycle and a symmetric Gauss-Seidel smoother for preconditioning the 3D Helmholtz linear system, especially when the minimum spatial sampling density is large (approximately 120 PPW) or small (approximately 4 PPW).

scholarly journals Multigrid solvers for immersed finite element methods and immersed isogeometric analysis

2019 ◽  
Vol 65 (3) ◽  
pp. 807-838 ◽  
Author(s):  
F. de Prenter ◽  
C. V. Verhoosel ◽  
E. H. van Brummelen ◽  
J. A. Evans ◽  
C. Messe ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Methods ◽  
Isogeometric Analysis ◽  
Convergence Rates ◽  
Computational Cost ◽  
Iterative Solvers ◽  
System Matrix ◽  
B Splines ◽  
Immersed Finite Element ◽  
Immersed Finite Element Methods

AbstractIll-conditioning of the system matrix is a well-known complication in immersed finite element methods and trimmed isogeometric analysis. Elements with small intersections with the physical domain yield problematic eigenvalues in the system matrix, which generally degrades efficiency and robustness of iterative solvers. In this contribution we investigate the spectral properties of immersed finite element systems treated by Schwarz-type methods, to establish the suitability of these as smoothers in a multigrid method. Based on this investigation we develop a geometric multigrid preconditioner for immersed finite element methods, which provides mesh-independent and cut-element-independent convergence rates. This preconditioning technique is applicable to higher-order discretizations, and enables solving large-scale immersed systems at a computational cost that scales linearly with the number of degrees of freedom. The performance of the preconditioner is demonstrated for conventional Lagrange basis functions and for isogeometric discretizations with both uniform B-splines and locally refined approximations based on truncated hierarchical B-splines.

A fast algorithm for regularized focused 3D inversion of gravity data using randomized singular-value decomposition

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. G25-G34 ◽  
Author(s):  
Saeed Vatankhah ◽  
Rosemary Anne Renaut ◽  
Vahid Ebrahimzadeh Ardestani
Keyword(s):  
Linear System ◽  
Singular Value Decomposition ◽  
Fast Algorithm ◽  
Large Scale ◽  
Gravity Data ◽  
Singular Values ◽  
Singular Value ◽  
System Matrix ◽  
Large Scale System ◽  
Value Decomposition

We develop a fast algorithm for solving the under-determined 3D linear gravity inverse problem based on randomized singular-value decomposition (RSVD). The algorithm combines an iteratively reweighted approach for [Formula: see text]-norm regularization with the RSVD methodology in which the large-scale linear system at each iteration is replaced with a much smaller linear system. Although the optimal choice for the low-rank approximation of the system matrix with [Formula: see text] rows is [Formula: see text], acceptable results are achievable with [Formula: see text]. In contrast to the use of the iterative LSQR algorithm for the solution of linear systems at each iteration, the singular values generated using RSVD yield a good approximation of the dominant singular values of the large-scale system matrix. Thus, the regularization parameter found for the small system at each iteration is dependent on the dominant singular values of the large-scale system matrix and appropriately regularizes the dominant singular space of the large-scale problem. The results achieved are comparable with those obtained using the LSQR algorithm for solving each linear system, but they are obtained at a reduced computational cost. The method has been tested on synthetic models along with real gravity data from the Morro do Engenho complex in central Brazil.

scholarly journals A new iterative solver for the time-harmonic wave equation

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. E57-E63 ◽  
Author(s):  
C. D. Riyanti ◽  
Y. A. Erlangga ◽  
R.-E. Plessix ◽  
W. A. Mulder ◽  
C. Vuik ◽  
...  
Keyword(s):  
Wave Equation ◽  
Linear System ◽  
Time Domain ◽  
Multigrid Method ◽  
Harmonic Wave ◽  
Computational Time ◽  
Full Time ◽  
Iterative Solver ◽  
Time Harmonic ◽  
The Time Domain

The time-harmonic wave equation, also known as the Helmholtz equation, is obtained if the constant-density acoustic wave equation is transformed from the time domain to the frequency domain. Its discretization results in a large, sparse, linear system of equations. In two dimensions, this system can be solved efficiently by a direct method. In three dimensions, direct methods cannot be used for problems of practical sizes because the computational time and the amount of memory required become too large. Iterative methods are an alternative. These methods are often based on a conjugate gradient iterative scheme with a preconditioner that accelerates its convergence. The iterative solution of the time-harmonic wave equation has long been a notoriously difficult problem in numerical analysis. Recently, a new preconditioner based on a strongly damped wave equation has heralded a breakthrough. The solution of the linear system associated with the preconditioner is approximated by another iterative method, the multigrid method. The multigrid method fails for the original wave equation but performs well on the damped version. The performance of the new iterative solver is investigated on a number of 2D test problems. The results suggest that the number of required iterations increases linearly with frequency, even for a strongly heterogeneous model where earlier iterative schemes fail to converge. Complexity analysis shows that the new iterative solver is still slower than a time-domain solver to generate a full time series. We compare the time-domain numeric results obtained using the new iterative solver with those using the direct solver and conclude that they agree very well quantitatively. The new iterative solver can be applied straightforwardly to 3D problems.

scholarly journals Differential transcendence of solutions of systems of linear differential equations based on total reduction of the system

2020 ◽  
pp. 24-24
Author(s):  
Ivana Jovovic
Keyword(s):  
Linear System ◽  
Differential Equations ◽  
Complex Field ◽  
System Matrix ◽  
Operator Equations ◽  
Total Reduction ◽  
Commuting Operators ◽  
Constant Coefficients ◽  
Linear Differential ◽  
System Of Operator Equations

In this paper we consider total reduction of the nonhomogeneous linear system of operator equations with constant coefficients and commuting operators. The totally reduced system obtained in this manner is completely decoupled. All equations of the system differ only in the variables and in the nonhomogeneous terms. The homogeneous parts are obtained using the generalized characteristic polynomial of the system matrix. We also indicate how this technique may be used to examine differential transcendence of the solution of the linear system of the differential equations with constant coefficients over the complex field and meromorphic free terms.

Trilinos Solvers Scalability on a MFiX-Trilinos Framework Applied to Fluidized Bed Simulations

2020 ◽  
Author(s):  
Arturo Rodriguez ◽  
V. M. Krushnarao Kotteda ◽  
Luis F. Rodriguez ◽  
Vinod Kumar ◽  
Jorge A. Munoz
Keyword(s):  
Linear System ◽  
Fluidized Bed ◽  
Large Scale ◽  
Fossil Fuel ◽  
State Of The Art ◽  
Iterative Solvers ◽  
Flow Solver ◽  
Nonlinear Solvers ◽  
Fuel Reactor ◽  
Preconditioned Iterative

Abstract MFiX is a multiphase open-source suite that is developed at the National Energy Technology Laboratories. It is widely used by fossil fuel reactor communities to simulate flow in a fluidized bed reactor. It does not have advanced linear iterative solvers even though it spends 70% of the run time in solving the linear system. Trilinos contains algorithms and enabling technologies for the solution of large-scale, sophisticated multi-physics engineering and scientific problems. The library developed at Sandia National Laboratories has more than 60 packages. It consists of state-of-the-art preconditioners, nonlinear solvers, direct solvers, and iterative solvers. The packages are performant and portable on various hybrid computing architectures. To improve the capabilities of MFiX, we developed a framework, MFiX-Trilinos, to integrate the advanced linear solvers in Trilinos with the FORTRAN based multiphase flow solver, MFiX. The framework changes the semantics of the array in FORTRAN and C++ and solve the linear system with packages in Trilinos and returns the solution to MFiX. The preconditioned iterative solvers considered for the analysis are BiCGStab and GMRES. The framework is verified on various fluidized bed problems. The performance of the framework is tested on the Stampede supercomputer. The wall time for multiple sizes of fluidized beds is compared.

An Industrial Application of Thermal Convection Analysis

2016 ◽  
Vol 13 (02) ◽  
pp. 1640005 ◽  
Author(s):  
H. Kanayama
Keyword(s):  
Linear System ◽  
Thermal Convection ◽  
Conjugate Gradient ◽  
Characteristic Curve ◽  
Iterative Solvers ◽  
Interface Problem ◽  
Coupling Analysis ◽  
Material Derivative ◽  
Decomposition System ◽  
Minimal Residual

A coupling analysis of thermal convection problems is performed in this work. By approximating the material derivative along the trajectory of fluid particle, the characteristic curve (CC) method can be considered. The most attractive advantage of this method is the symmetry of the linear system, which enables some classic symmetric linear iterative solvers, like the conjugate gradient (CG) method or the minimal residual (MINRES) method, to be used to solve the interface problem of the domain decomposition system. An application to industrial problems is demonstrated to show the effectiveness of our approach.

Predictor Feedback of Uncertain Multi-Input Systems

2020 ◽  
pp. 267-292
Author(s):  
Yang Zhu ◽  
Miroslav Krstic
Keyword(s):  
Linear System ◽  
Output Feedback ◽  
State Feedback ◽  
System Matrix ◽  
Infinite Dimensional ◽  
Finite Dimensional ◽  
Unknown System ◽  
Distributed Actuator

This chapter discusses the predictor feedback for uncertain multi-input systems. This is based on the predictor feedback framework for uncertainty-free multi-input systems in the tenth chapter. The chapter addresses four combinations of the five uncertainties that come from a finite-dimensional multi-input linear system with distributed actuator delays. These uncertainties include the following types: unknown and distinct delays, unknown delay kernels, unknown system matrix, unmeasurable finite-dimensional plant state, and unmeasurable infinite-dimensional actuator state. The chapter then examines the adaptive state feedback under unknown as well as uncertain delays, delay kernels, and parameters. It also explores robust output feedback under unknown delays, delay kernels, and PDE or ODE states.

Sign in / Sign up

Export Citation Format

Share Document