Integral Equation Methods for Variable Coefficient Elliptic Problems and Applications

变系数椭圆问题的积分方程方法及其应用

• 批准号: 0411920
• 负责人: Jingfang Huang
• 金额: $ 17.2万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-15 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0411920&HistoricalAwards=false
• 关键词: Integral; Equation; Methods; Variable; Coefficient

The focus of this proposal is on the mathematical analysis and efficient implementation of integral equation based methods (IEM) for the solution of physically important, spatially inhomogeneous or "variable coefficient" elliptic equations. These arise in applications which require the modeling of complex fluid flows, such as combustion or ground water pollution. They also arise in the design of novel fluidic Micro-Electro-Mechanical Systems (MEMS), as well as solid-state semiconductor devices. Over the last decade, IEMs have proven to be very successful and are now widely used in electromagnetic, elastic, and fluid dynamic modeling when the governing equation is of "constant coefficient" type. In order to address more general, variable coefficient problems, we represent the solution as a volume integral using the Green's function for a simple, nearby problem convolved with an unknown density. Inserting this representation into the original elliptic equation leads to a linear integral equation. A principal advantage of this approach is that it avoids solving the sparse but poorly conditioned linear systems that result from direct discretization of the differential equation. Discretization of the linear integral equation leads, instead, to a well conditioned dense linear system, for which Krylov subspace methods converge quickly. Historically, the disadvantage of using iterative methods with integral equations was the cost of computing the dense matrix-vector multiplications which are needed at every step. With the advent of the fast multipole method (FMM), however, these can be evaluated in optimal time. Nevertheless, in order for the method to be robust and practical, several issues remain. In particular, effective preconditioning strategies for problems with strong gradients in the material properties are not well developed. Preliminary analysis of some multilevel approaches for model problems are promising.Our planned research program combines classical mathematics (PDEs, integral equations, potential theory) with scientific computing research (fast summation, iterative solvers, adaptive mesh refinement, domain decomposition) and important applications (porous media and variable density flows). The interdisciplinary nature of the work provides fertile ground for training graduate students as computational scientists. Experience gained in the application areas mentioned will have broad impact, since the techniques created can be transferred to many other application areas in the environmental, geophysical, biological, and engineering sciences.

该提案的重点是数学分析和有效实施的积分方程为基础的方法（IEM）的物理上重要的，空间上不均匀的或“变系数”椭圆方程的解决方案。 这些问题出现在需要对复杂流体流动进行建模的应用中，例如燃烧或地下水污染。 它们也出现在新型流体微机电系统（MEMS）以及固态半导体器件的设计中。 在过去的十年中，IEM已经被证明是非常成功的，现在被广泛用于电磁，弹性和流体动力学建模时的控制方程是“常系数”类型。 为了解决更一般的，变系数的问题，我们表示的解决方案作为一个体积积分使用绿色的功能为一个简单的，附近的问题与未知的密度卷积。 将此表示插入到原始椭圆方程中会导致线性积分方程。 这种方法的一个主要优点是，它避免了解决稀疏的，但条件差的线性系统，直接离散的微分方程。 线性积分方程的离散化导致，而不是一个良好的条件下，密集的线性系统，Krylov子空间方法收敛迅速。 从历史上看，使用积分方程迭代方法的缺点是计算每一步都需要的密集矩阵向量乘法的成本。 然而，随着快速多极子方法（FMM）的出现，这些可以在最佳时间进行评估。 然而，为了使该方法稳健和实用，仍然存在一些问题。 特别是，有效的预处理策略的问题，在材料性能的强梯度还没有得到很好的发展。 我们计划的研究计划将经典数学（偏微分方程、积分方程、位势理论）与科学计算研究（快速求和、迭代求解、自适应网格细化、区域分解）和重要应用（多孔介质和变密度流）相结合。 这项工作的跨学科性质为培养研究生成为计算科学家提供了肥沃的土壤。在上述应用领域获得的经验将产生广泛的影响，因为所创造的技术可以转移到环境，地球物理，生物和工程科学的许多其他应用领域。