Large scale thermal-solid coupling analysis using inexact balancing domain decomposition

In this research, a system of thermal-solid coupling analysis is developed with the implementation of Inexact Balancing Domain Decomposition with a diagonal scaling (IBDD-DIAG) in both thermal and solid analysis. The IBDD-DIAG is an improved version of Balancing Domain Decomposition (BDD), where an incomplete factorization based parallel direct method is employed to solve a coarse space problem, and the diagonal-scaling is employed to precondition local fine space problems instead of the Neumann-Neumann preconditioner. The developed system performed heat conductive analysis to have temperature distributions in solid models and then performed the structural analysis to see deformation or expansion due to temperature differences. Both of the analyses employed the Hierarchical Domain Decomposition Method (HDDM) with parallel IBDD-DIAG. It is shown that the iterative procedure converges rapidly and the convergence is independent of the number of subdomains, namely, numerical scalability is satisfied. The present system is implemented on massively parallel processors and succeeds in solving a thermal-solid coupling problem of 12 millions of nodes

Treatment of Block-Based Sparse Matrices in Domain Decomposition Method

Abstract— The domain decomposition method involves the finite element solution of problems in the parallel computer. The finite element discretization leads to the solution of large systems of linear equation whose matrix is naturally sparse. The use of proper storing techniques for sparse matrix is fundamental especially when dealing with large scale problems typical of industrial applications. The aim of this research is to review the sparsity pattern of the matrices originating from the discretization of the elasto-plastic and thermal-convection problems. Some practical strategies dealing with sparsity pattern in the finite element code of adventure system are recalled. Several efficient storage schemes to store the matrix originating from elasto-plastic and thermal-convection problems have been proposed. In the proposed technique, inherent block pattern of the matrix is exploited to locate the matrix element. The computation in the high performance computer shows better performance compared to the conventional skyline storage method used by the most of the researchers

Meeting the cultural and service needs of Arabic international students by using QFD

Quality has become an important factor in global competition for many reasons. Intensive global competition and the demand for better quality by customers has led organizations to realize the benefits of providing quality products and services in order to successfully compete and survive. Higher education institutions are one example of these organisations. Higher education institutions work in an intensive competitive environment worldwide driven by increasing demands for learning by local and international students. As a result, the managers of these sectors have realized that improving the quality of services is important for achieving customer satisfaction which can help survival in an internationally competitive market. To do this, it is necessary for organizations to know their customers and identify their requirements. To this end, many higher education institutions have adopted principles of total quality management (TQM) to improve their education quality which leads to better performance through involvement of every department to achieve excellence in business. This chapter considers the importance of measuring quality in order to assist universities to proactively manage the design and improvement of the social and academic experiences of postgraduate international students, and plan management decision-making processes to deliver high-quality services in a globalized business of provision of higher education. Higher education institutions must operate effectively and efficiently and be able to deliver quality programs, by seeking to better understand the needs of their customers to be competitive in this market space

A Scalable Balancing Domain Decomposition Based Preconditioner for Large Scale Heat Transfer Problems

An efficient and scalable Balancing Domain Decomposition (BDD) type preconditioner for large scale linear systems arising from 3-dimensional heat transfer problems is presented. The new method improves parallel scalability of BDD by employing an incomplete balancing technique to approximate a coarse space problem and a diagonal scaling to precondition the local fine space problems instead of the Neumann-Neumann preconditioner. It may increase the number of iterations but reduces the computation costs of the precondition process for each iteration. Consequently, total computation time and required memory are expected to be reduced. The convergence estimates may also be independent of the number of subdomains. We have implemented this algorithm on the parallel processors and have succeeded in solving some illconditioned large scale heat transfer problems

Heat Conductive Analysis with Balancing Domain Decomposition Method

Balancing Domain Decomposition (BDD) proposed by J. Mandel is an effective preconditioning technique for reducing the number of iterations of iterative Domain Decomposition Method(DDM). Until now many researches have been done on the implementation of BDD in different areas including elasticity problems and semiconductor simulation. In this paper, we implement BDD in another area that is 3-dimensional(3-D) heat conductive analysis in solid. We construct the BDD preconditioner in parallel based on Hierarchical Domain Decomposition Method (HDDM). We report the comparative performance of BDD with diagonal scaling and original DDM without preconditioning to analyse heat conductive problems with about 2 million degrees of freedom and over 11 million degrees of freedom. With BDD the convergence rates are reduced effectively and become independent of the number of subdomains

Heat Conductive Analysis with Balancing Domain Decomposition Method

Balancing Domain Decomposition (BDD) proposed by J. Mandel is an effective preconditioning technique for reducing the number of iterations of iterative Domain Decomposition Method(DDM). Until now many researches have been done on the implementation of BDD in different areas including elasticity problems and semiconductor simulation. In this paper, we implement BDD in another area that is 3-dimensional(3-D) heat conductive analysis in solid. We construct the BDD preconditioner in parallel based on Hierarchical Domain Decomposition Method (HDDM). We report the comparative performance of BDD with diagonal scaling and original DDM without preconditioning to analyse heat conductive problems with about 2 million degrees of freedom and over 11 million degrees of freedom. With BDD the convergence rates are reduced effectively and become independent of the number of subdomains