Mathematical Sciences Computing Research Environments

9508328拉塞尔位于丹佛的科罗拉多大学计算数学中心将购买一台8处理器的太阳微系统SPARC服务器1000E，该服务器将致力于支持数学科学的研究。该设备将用于几个研究项目，特别是：用于飞行器周围随时间变化的可压缩湍流的并行隐式多重网格直接数值模拟。在亚音速和高超声速下的过渡和湍流流动将被直接近似，而不需要湍流模型。地下流动的欧拉-拉格朗日方法和混合方法。将使用新的数值方法模拟多孔介质中的流动，重点是在石油开采和地下水污染物运输方面的应用，新的数值方法旨在准确地模拟通过不规则地质构造的以对流为主的流动。稳定化有限元方法及其大规模并行实现。将开发和分析适用于广泛应用的具有很强精度和稳健性的离散化，并将设计和实现区域分解方程求解器。并行迭代法。对于在高度扭曲的有限元网格上离散固体力学模型所产生的方程，将开发健壮、快速的方程解算器。大型对称特征问题的预条件并行方法。对于对称广义特征问题的几个极值特征对的计算，将发展出快速、准确、稳健的方法。这些项目跨越了科学计算的主要领域：模型开发和应用、离散化方法、线性方程和特征值问题。所有这些都将大规模并行计算作为一个中心主题。

9508328 Russell The Center for Computational Mathematics at the University of Colorado at Denver will purchase an eight-processor Sun Microsystems SPARC server 1000E, which will be dedicated to the support of research in the mathematical sciences. The equipment will be used for several research projects, including in particular: Parallel Implicit Multigrid Methods for Direct Numerical Simulation of Time Dependent Compressible Turbulent Flow around Flight Vehicles. Transitional and turbulent flow at subsonic and hypersonic speeds will be approximated directly, without turbulence models. Eulerian-Lagrangian and Mixed Methods for Subsurface Flows. Flows in porous media, with emphasis on applications to oil recovery and groundwater contaminant transport, will be simulated with new numerical methods designed to be accurate for convection dominated flows through irregular geological formations. Stabilized Finite Element Methods and their Massively Parallel Implementation. Discretizations with strong accuracy and robustness for a wide range of applications will be developed and analyzed, and domain-decomposition equation solvers will be designed and implemented. Parallel Iterative Methods. Robust, fast equation solvers will be developed for equations arising from discretizations of models of solid mechanics on highly distorted finite element meshes. Preconditioned Parallel Methods for Large Symmetric Eigenproblems. Fast, accurate, robust methods will be developed for computation of several extreme eigenpairs of symmetric generalized eigenproblems. The projects span major areas of scientific computation: model development and application, discretization methods, linear equations, and eigenvalue problems. All share large-scale parallel computation as a central theme.