In this study, a new coupled surface shape design (SSD) methodology named direct design method is presented for the solution of problems containing different types of convection heat transfer in which a specific distr...
详细信息
In this study, a new coupled surface shape design (SSD) methodology named direct design method is presented for the solution of problems containing different types of convection heat transfer in which a specific distribution of either heat flux or temperature is given instead of the shape of a boundary. In the proposed method, the governing equation, without using any mathematical transformation for the physical domains, is manipulated so that the grid generation, solving fluid flow, and heat transfer as well as shape updating can all be carried out simultaneously. Five different inverse shape design problems containing different types of convection heat transfer are solved by the proposed method. All the problems are also solved using the ball-spine algorithm (BSA), which is a recently developed de-coupled algorithm, for the sake of comparison. In all problems, the effects of using different under-relaxation parameters are investigated and the capability of both approaches is compared with each other. The results show that the proposed coupled method can solve the problems better than the BSA in the sense that the direct design method converges sooner than the BSA when the same under-relaxation parameter is used for both methods. Also, it is shown that the computational cost of solving a SSD problem using the direct design method is slightly greater than solving an analysis problem.
In this paper we propose a method to couple two or more explicit numerical schemes approximating the same time-dependent PDE, aiming at creating a new scheme which inherits advantages of the original ones. We consider...
详细信息
In this paper we propose a method to couple two or more explicit numerical schemes approximating the same time-dependent PDE, aiming at creating a new scheme which inherits advantages of the original ones. We consider both advection equations and nonlinear conservation laws. By coupling a macroscopic (Eulerian) scheme with a microscopic (Lagrangian) scheme, we get a new kind of multiscale numerical method.
coupled learning algorithm, in which the eigenvector and eigenvalue of a covariance matrix are estimated in coupled equations simultaneously, is a solution to the speed stability problem that plagues most noncoupled l...
详细信息
ISBN:
(纸本)9781479919802
coupled learning algorithm, in which the eigenvector and eigenvalue of a covariance matrix are estimated in coupled equations simultaneously, is a solution to the speed stability problem that plagues most noncoupled learning rules. Moller has proposed a class of well-performed CPCA (coupled principal component analysis) algorithms, but it is a pity that only few of CMCA (coupled minor component analysis) algorithm was proposed until now. In this paper, to expand the CMCA field, we propose some stable CMCA algorithms based on Miiller's CPCA and CMCA algorithms. The proposed algorithms provide efficient methods to extract the minor eigenvector and eigenvalue of a covariance matrix. Simulation experiments confirm the effectiveness of the proposed algorithms.
coupled learning algorithm,in which the eigenvector and eigenvalue of a covariance matrix are estimated in coupled equations simultaneously,is a solution to the speedstability problem that plagues most noncoupled lear...
详细信息
coupled learning algorithm,in which the eigenvector and eigenvalue of a covariance matrix are estimated in coupled equations simultaneously,is a solution to the speedstability problem that plagues most noncoupled learning *** has proposed a class of well-performed CPCA(coupled principal component analysis) algorithms,but it is a pity that only few of CMCA(coupled minor component analysis)algorithm was proposed until *** this paper,to expand the CMCA field,we propose some stable CMCA algorithms based on Moller's CPCA and CMCA *** proposed algorithms provide efficient methods to extract the minor eigenvector and eigenvalue of a covariance *** experiments confirm the effectiveness of the proposed algorithms.
This paper is concerned with improving the performance of certain Markov chain algorithms for Monte Carlo simulation. We propose a new algorithm for simulating from multivariate Gaussian densities. This algorithm comb...
详细信息
This paper is concerned with improving the performance of certain Markov chain algorithms for Monte Carlo simulation. We propose a new algorithm for simulating from multivariate Gaussian densities. This algorithm combines ideas from coupled Markov chain methods and from an existing algorithm based only on over-relaxation. The rate of convergence of the proposed and existing algorithms can be measured in terms of the square of the spectral radius of certain matrices. We present examples in which the proposed algorithm converges faster than the existing algorithm and the Gibbs sampler. We also derive an expression for the asymptotic variance of any linear combination of the variables simulated by the proposed algorithm. We outline how the proposed algorithm can be extended to non-Gaussian densities.
暂无评论