AMC-SS: A Multi-Element Generalized Polynomial Chaos Method for Modeling Uncertainty in Flow Simulations

AMC-SS：一种用于流体仿真中不确定性建模的多元素广义多项式混沌方法

• 批准号: 0510799
• 负责人: George Karniadakis
• 金额: $ 31.18万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0510799&HistoricalAwards=false
• 关键词: AMC; SS; Multi; Element; Generalized

In numerical simulations of fluid flows, just as in experiments, we often question the accuracy of the results, and we construct error bars that reflect the numerical accuracy of the solution. In many cases, however, there exists a much larger error associated with the fact that the physical parameters, the geometry, and the operating conditions of the simulated flow are not precisely known. With the computational fluid dynamics field reaching now some degree of maturity, we naturally pose the more general question of how to model uncertainty and stochastic input mathematically, and how to develop new algorithms that will yield simulation results that reflect accurately the propagation of uncertainty. To this end, the Monte Carlo approach can be employed but it is computationally expensive and it is only used as the last resort.In this grant we develop a new approach similar to high-order finite element methods but instead decomposing the random domain. Specifically, we extend the pioneering ideas of Norbert Wiener in generalized Fourier series -- the so-called polynomial chaos expansion -- and apply it locally to each random element. The resulting system of governing equations forms a set of coupled modified flow equations, which are deterministic and thus can be solved with standard numerical methods. Comparisons with the Monte Carlo method show that the new method is faster by a factor of 100 to 1000 on the average. We propose to document systematically this method and use it to study in detail important problems in high-speed flows and in modeling blood flow in the human arterial tree. The proposed approach will affect fundamentally the way we design new experiments and the type of questions that we can address, while the interaction between simulation and experiment will become more meaningful and more dynamic. This, in turn, will find its way into the design of flow systems equipment and will provide a rigorous reliability framework.We plan to involve graduate and undergraduate students in the current research and we will develop a specific initiative to attract pre-college female students to mathematics and computational science.

在流体流动的数值模拟中，就像在实验中一样，我们经常质疑结果的准确性，并构建反映解决方案数值准确性的误差条。 然而，在许多情况下，由于模拟流的物理参数、几何形状和操作条件并不精确已知，因此存在更大的误差。 随着计算流体动力学领域现在达到一定程度的成熟，我们自然会提出更普遍的问题，即如何对不确定性和随机输入进行数学建模，以及如何开发新算法来产生准确反映不确定性传播的模拟结果。 为此，可以采用蒙特卡罗方法，但它的计算成本很高，并且仅用作最后的手段。在这项资助中，我们开发了一种类似于高阶有限元方法的新方法，但分解了随机域。 具体来说，我们扩展了诺伯特·维纳在广义傅里叶级数中的开创性思想——所谓的多项式混沌展开——并将其局部应用于每个随机元素。 由此产生的控制方程组形成一组耦合修正的流动方程，这些方程是确定性的，因此可以用标准数值方法求解。 与蒙特卡罗方法的比较表明，新方法平均速度快了 100 到 1000 倍。 我们建议系统地记录该方法，并用它来详细研究高速流动和人体动脉树血流建模中的重要问题。所提出的方法将从根本上影响我们设计新实验的方式以及我们可以解决的问题类型，而模拟和实验之间的交互将变得更有意义、更动态。 反过来，这将进入流动系统设备的设计，并提供严格的可靠性框架。我们计划让研究生和本科生参与当前的研究，我们将制定一项具体举措，吸引大学预科女学生学习数学和计算科学。