960 resultados para Incomplete Lu Decomposition


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A hybrid finite difference method and vortex method (HDV), which is based on domain decomposition and proposed by the authors (1992), is improved by using a modified incomplete LU decomposition conjugate gradient method (MILU-CG), and a high order implicit difference algorithm. The flow around a rotating circular cylinder at Reynolds number R-e = 1000, 200 and the angular to rectilinear speed ratio alpha is an element of (0.5, 3.25) is studied numerically. The long-time full developed features about the variations of the vortex patterns in the wake, and drag, lift forces on the cylinder are given. The calculated streamline contours agreed well with the experimental visualized flow pictures. The existence of critical states and the vortex patterns at the states are given for the first time. The maximum lift to drag force ratio can be obtained nearby the critical states.

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This paper discusses preconditioned Krylov subspace methods for solving large scale linear systems that originate from oil reservoir numerical simulations. Two types of preconditioners, one being based on an incomplete LU decomposition and the other being based on iterative algorithms, are used together in a combination strategy in order to achieve an adaptive and efficient preconditioner. Numerical tests show that different Krylov subspace methods combining with appropriate preconditioners are able to achieve optimal performance.

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Os efeitos Delaware e Groningen são dois tipos de anomalia que afetam ferramentas de eletrodos para perfilagem de resistividade. Ambos os efeitos ocorrem quando há uma camada muito resistiva, como anidrita ou halita, acima do(s) reservatório(s), produzindo um gradiente de resistividade muito similar ao produzido por um contato óleo-água. Os erros de interpretação produzidos têm ocasionado prejuízos consideráveis à indústria de petróleo. A PETROBRÁS, em particular, tem enfrentado problemas ocasionados pelo efeito Groningen sobre perfis obtidos em bacias paleozóicas da região norte do Brasil. Neste trabalho adaptamos, com avanços, uma metodologia desenvolvida por LOVELL (1990), baseada na equação de Helmholtz para HΦ, para modelagem dos efeitos Delaware e Groningen. Solucionamos esta equação por elementos finitos triangulares e retangulares. O sistema linear gerado pelo método de elementos finitos é resolvido por gradiente bi-conjugado pré-condicionado, sendo este pré-condicionador obtido por decomposição LU (Low Up) da matriz de stiffness. As voltagens são calculadas por um algoritmo, mais preciso, recentemente desenvolvido. Os perfis são gerados por um novo algoritmo envolvendo uma sucessiva troca de resistividade de subdomínios. Este procedimento permite obter cada nova matriz de stiffness a partir da anterior pelo cálculo, muito mais rápido, da variação dessa matriz. Este método permite ainda, acelerar a solução iterativa pelo uso da solução na posição anterior da ferramenta. Finalmente geramos perfis sintéticos afetados por cada um dos efeitos para um modelo da ferramenta Dual Laterolog.

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Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and IV classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a theoretical speedup of four can be achieved. In this paper, we demonstrate the implementation of a parallel LU matrix decomposition algorithm for solving linear systems with SSE and discuss advantages and disadvantages of this approach based on our experimental study.

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This paper discusses the parallel implementation of the solution of a set of linear equations using the Alternative Quadrant Interlocking Factorisation Methods (AQIF), on a star topology. Both the AQIF and LU decomposition methods are mapped onto star topology on an IBM SP2 system, with MPI as the internode communicator. Performance parameters such as speedup, efficiency have been obtained through experimental and theoretical means. The studies demonstrate (i) a mismatch of 15% between the theoretical and experimental results, (ii) scalability of the AQIF algorithm, and (iii) faster executing AQIF algorithm.

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Coarse Grained Reconfigurable Architectures (CGRA) are emerging as embedded application processing units in computing platforms for Exascale computing. Such CGRAs are distributed memory multi- core compute elements on a chip that communicate over a Network-on-chip (NoC). Numerical Linear Algebra (NLA) kernels are key to several high performance computing applications. In this paper we propose a systematic methodology to obtain the specification of Compute Elements (CE) for such CGRAs. We analyze block Matrix Multiplication and block LU Decomposition algorithms in the context of a CGRA, and obtain theoretical bounds on communication requirements, and memory sizes for a CE. Support for high performance custom computations common to NLA kernels are met through custom function units (CFUs) in the CEs. We present results to justify the merits of such CFUs.

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As a fast and effective method for approximate calculation of seismic numerical simulation, ray tracing method, which has important theory and practical application value, in terms of seismic theory and seismic simulation, inversion, migration, imaging, simplified from seismic theory according to geometric seismic, means that the main energy of seismic wave field propagates along ray paths in condition of high-frequency asymptotic approximation. Calculation of ray paths and traveltimes is one of key steps in seismic simulation, inversion, migration, and imaging. Integrated triangular grids layout on wavefront with wavefront reconstruction ray tracing method, the thesis puts forward wavefront reconstruction ray tracing method based on triangular grids layout on wavefront, achieves accurate and fast calculation of ray paths and traveltimes. This method has stable and reasonable ray distribution, and overcomes problems caused by shadows in conventional ray tracing methods. The application of triangular grids layout on wavefront, keeps all the triangular grids stable, and makes the division of grids and interpolation of a new ray convenient. This technology reduces grids and memory, and then improves calculation efficiency. It enhances calculation accuracy by accurate and effective description and division on wavefront. Ray tracing traveltime table, which shares the character of 2-D or 3-D scatter data, has great amount of data points in process of seismic simulation, inversion, migration, and imaging. Therefore the traveltime table file will be frequently read, and the calculation efficiency is very low. Due to these reasons, reasonable traveltime table compression will be very necessary. This thesis proposes surface fitting and scattered data compression with B-spline function method, applies to 2-D and 3-D traveltime table compression. In order to compress 2-D (3-D) traveltime table, first we need construct a smallest rectangular (cuboidal) region with regular grids to cover all the traveltime data points, through the coordinate range of them in 2-D surface (3-D space). Then the value of finite regular grids, which are stored in memory, can be calculated using least square method. The traveltime table can be decompressed when necessary, according to liner interpolation method of 2-D (3-D) B-spline function. In the above calculation, the coefficient matrix is stored using sparse method and the liner system equations are solved using LU decomposition based on the multi-frontal method according to the sparse character of the least square method matrix. This method is practiced successfully in several models, and the cubic B-spline function can be the best basal function for surface fitting. It make the construction surface smooth, has stable and effective compression with high approximate accuracy using regular grids. In this way, through constructing reasonable regular grids to insure the calculation efficiency and accuracy of compression and surface fitting, we achieved the aim of traveltime table compression. This greatly improves calculation efficiency in process of seismic simulation, inversion, migration, and imaging.

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The processes of seismic wave propagation in phase space and one way wave extrapolation in frequency-space domain, if without dissipation, are essentially transformation under the action of one parameter Lie groups. Consequently, the numerical calculation methods of the propagation ought to be Lie group transformation too, which is known as Lie group method. After a fruitful study on the fast methods in matrix inversion, some of the Lie group methods in seismic numerical modeling and depth migration are presented here. Firstly the Lie group description and method of seismic wave propagation in phase space is proposed, which is, in other words, symplectic group description and method for seismic wave propagation, since symplectic group is a Lie subgroup and symplectic method is a special Lie group method. Under the frame of Hamiltonian, the propagation of seismic wave is a symplectic group transformation with one parameter and consequently, the numerical calculation methods of the propagation ought to be symplectic method. After discrete the wave field in time and phase space, many explicit, implicit and leap-frog symplectic schemes are deduced for numerical modeling. Compared to symplectic schemes, Finite difference (FD) method is an approximate of symplectic method. Consequently, explicit, implicit and leap-frog symplectic schemes and FD method are applied in the same conditions to get a wave field in constant velocity model, a synthetic model and Marmousi model. The result illustrates the potential power of the symplectic methods. As an application, symplectic method is employed to give synthetic seismic record of Qinghai foothills model. Another application is the development of Ray+symplectic reverse-time migration method. To make a reasonable balance between the computational efficiency and accuracy, we combine the multi-valued wave field & Green function algorithm with symplectic reverse time migration and thus develop a new ray+wave equation prestack depth migration method. Marmousi model data and Qinghai foothills model data are processed here. The result shows that our method is a better alternative to ray migration for complex structure imaging. Similarly, the extrapolation of one way wave in frequency-space domain is a Lie group transformation with one parameter Z and consequently, the numerical calculation methods of the extrapolation ought to be Lie group methods. After discrete the wave field in depth and space, the Lie group transformation has the form of matrix exponential and each approximation of it gives a Lie group algorithm. Though Pade symmetrical series approximation of matrix exponential gives a extrapolation method which is traditionally regarded as implicit FD migration, it benefits the theoretic and applying study of seismic imaging for it represent the depth extrapolation and migration method in a entirely different way. While, the technique of coordinates of second kind for the approximation of the matrix exponential begins a new way to develop migration operator. The inversion of matrix plays a vital role in the numerical migration method given by Pade symmetrical series approximation. The matrix has a Toepelitz structure with a helical boundary condition and is easy to inverse with LU decomposition. A efficient LU decomposition method is spectral factorization. That is, after the minimum phase correlative function of each array of matrix had be given by a spectral factorization method, all of the functions are arranged in a position according to its former location to get a lower triangular matrix. The major merit of LU decomposition with spectral factorization (SF Decomposition) is its efficiency in dealing with a large number of matrixes. After the setup of a table of the spectral factorization results of each array of matrix, the SF decomposition can give the lower triangular matrix by reading the table. However, the relationship among arrays is ignored in this method, which brings errors in decomposition method. Especially for numerical calculation in complex model, the errors is fatal. Direct elimination method can give the exact LU decomposition But even it is simplified in our case, the large number of decomposition cost unendurable computer time. A hybrid method is proposed here, which combines spectral factorization with direct elimination. Its decomposition errors is 10 times little than that of spectral factorization, and its decomposition speed is quite faster than that of direct elimination, especially in dealing with a large number of matrix. With the hybrid method, the 3D implicit migration can be expected to apply on real seismic data. Finally, the impulse response of 3D implicit migration operator is presented.

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Finding the smallest eigenvalue of a given square matrix A of order n is computationally very intensive problem. The most popular method for this problem is the Inverse Power Method which uses LU-decomposition and forward and backward solving of the factored system at every iteration step. An alternative to this method is the Resolvent Monte Carlo method which uses representation of the resolvent matrix [I -qA](-m) as a series and then performs Monte Carlo iterations (random walks) on the elements of the matrix. This leads to great savings in computations, but the method has many restrictions and a very slow convergence. In this paper we propose a method that includes fast Monte Carlo procedure for finding the inverse matrix, refinement procedure to improve approximation of the inverse if necessary, and Monte Carlo power iterations to compute the smallest eigenvalue. We provide not only theoretical estimations about accuracy and convergence but also results from numerical tests performed on a number of test matrices.

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The thermal behavior of Cu-Al alloys with 17, 19 and 21 at.%Al was examined by differential thermal analysis (DTA), differential scanning calorimetry (DSC), X-ray diffractometry (XRD), optical microscopy (OM) and scanning electron microscopy (SEM). The presence of the gamma phase (Al4Cu9) was clearly detected for the Cu-19 at.%Al alloy and caused the alpha (2) phase disordering process in two stages. The tendency to increase the alpha (2) dissolution precipitates with the increase in the Al content seems to be reverted for compositions at about 21 at.%Al and the heating/cooling ratio seems to influence the thermal response of this process. The presence of the endothermic peak corresponding to the beta (1)--> beta transformation depends on an incomplete beta decomposition reaction. The variation of the heating rate showed that the beta (1)-->(alpha+gamma (1)) decomposition is the dominant reaction for alloys containing 19 and 21 at.%Al.

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Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.

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The thermal decomposition of sodium azide has been studied in the temperature range 240–360°C in vacuum and under pressure of an inert gas, argon. The results show that the decomposition is partial 360°C. From the observations made in the present work, namely: (i) the decomposition is incomplete both under vacuum and inert gas; (ii) mass spectrometric studies do not reveal any decrease in the intensity of the background species, CO+2, CO+, H2O+, and (iii) sodium metal remains in the ‘free state’ as seen by the formation of a metallic mirror at temperatures above 300°C, it has been argued that the partial nature of decompostion is due to the confinement of the decomposition to intermosaic regions within the lattice.

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Results are presented to show that as the thermal decomposition of orthorhombic ammonium perchlorate proceeds there is an accumulation, in the solid, of hydrochloric and nitric acids, the concentrations of which increase up to 15% decomposition after which they decrease until they reach the original values.

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The equilibrium decomposition temperatures of Cu2Ln2O5 (Ln = Tb, Dy, Ho, Er, Tm, Yb, and Lu) compounds have been measured using a combined DTA-TGA apparatus under a flowing Ar + O2 gas mixture, in which the partial pressure of oxygen was controlled at 5.0 × 103 Pa. The Cu2Ln2O5 compounds yield Ln2O3 and Cu2O on decomposition. The decomposition temperature increases monotonically with the atomic number of the lanthanide element. This suggests that the stability of the Cu2Ln2O5 compounds with respect to the component binary oxides increases with decreasing radius of the Ln3+ ion.

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Vita.