A Krylov subspace projection method for simultaneous solution of Helmholtz problems at multiple frequencies

2003 ◽  
Vol 192 (41-42) ◽  
pp. 4609-4640 ◽  
Author(s):  
Marcus M. Wagner ◽  
Peter M. Pinsky ◽  
Assad A. Oberai ◽  
Manish Malhotra
Keyword(s):  
Projection Method ◽  
Krylov Subspace ◽  
Subspace Projection ◽  
Simultaneous Solution ◽  
Multiple Frequencies

A Novel Macromodel based on Krylov Subspace Projection Method for Micromixers with Serpentine Channels

2018 ◽  
Vol 19 (3-4) ◽  
pp. 275-280 ◽  
Author(s):  
Xueye Chen ◽  
Shuai Zhang
Keyword(s):  
Numerical Simulation ◽  
Galerkin Method ◽  
Projection Method ◽  
Computational Efficiency ◽  
Krylov Subspace ◽  
Orthogonal Basis ◽  
Subspace Projection ◽  
Relative Deviation ◽  
Simulation Results ◽  
Arbitrary Location

AbstractA novel macromodel based on Krylov subspace projection method for micromixers with serpentine channels is presented. The physical equations are discretized using Galerkin method. The orthogonal basis is obtained and the discrete matrix is assembled with Arnoldi procedure based on Krylov subspace projection. The obtained macromodel can be used to calculate the concentration of the sample at arbitrary location of serpentine micromixers. The maximal relative deviation is 2 % between macromodel and only numerical simulation. The computational efficiency of the macromodel will be improved significantly with the numbers of serpentine channels increasing. Simulation results demonstrated that the macromodel is flexible, effective and easily operated for rapid design and computation of serpentine micromixers.

InSAR Phase Filtering via Joint Subspace Projection Method: Application in Change Detection

2014 ◽  
Vol 11 (10) ◽  
pp. 1817-1820 ◽  
Author(s):  
Azzedine Bouaraba ◽  
Aichouche Belhadj-Aissa ◽  
Dirk Borghys ◽  
Marc Acheroy ◽  
Damien Closson
Keyword(s):  
Change Detection ◽  
Projection Method ◽  
Subspace Projection

scholarly journals Occlusive Face Recognition using the Selective Subspace Projection Method

2008 ◽  
Vol 18 (1) ◽  
pp. 48-52
Author(s):  
Young-Gil Kim ◽  
Young-Jun Song ◽  
Dong-Woo Kim ◽  
Jae-Hyeong Ahn
Keyword(s):  
Face Recognition ◽  
Projection Method ◽  
Subspace Projection

An Efficient Algorithm to Construct an Orthonormal Basis for the Extended Krylov Subspace

2014 ◽  
Vol 4 (3) ◽  
pp. 267-282
Author(s):  
Akira Imakura
Keyword(s):  
Efficient Algorithm ◽  
Orthonormal Basis ◽  
Krylov Subspace ◽  
Computational Cost ◽  
Projection Methods ◽  
Matrix Equations ◽  
Subspace Projection ◽  
Matrix Computations ◽  
Large Matrix ◽  
Traditional Algorithm

AbstractSubspace projection methods based on the Krylov subspace using powers of a matrix A have often been standard for solving large matrix computations in many areas of application. Recently, projection methods based on the extended Krylov subspace using powers of A and A−1 have attracted attention, particularly for functions of a matrix times a vector and matrix equations. In this article, we propose an efficient algorithm for constructing an orthonormal basis for the extended Krylov subspace. Numerical experiments indicate that this algorithm has less computational cost and approximately the same accuracy as the traditional algorithm.

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