Petascale computations for complex turbulent flows at high Reynolds number

该奖项促进了使用IBM开发的名为Blue沃茨的大型新计算资源的科学研究，并计划部署在伊利诺伊大学。它提供差旅费，以支持格鲁吉亚理工学院、华盛顿大学、圣地亚哥超级计算机中心、得克萨斯大学奥斯汀分校、蓝色沃茨项目小组和供应商技术小组的主要调查人员之间的技术协调。 拟议的研究采用直接数值模拟（DNS），以解决与三个高雷诺数湍流相关的研究问题：湍流非预混甲烷火焰;标量混合和颗粒分散在各向同性湍流;和湍流壁有界的流动在一个通道。 在每一种情况下，中心重点是达到足够高的雷诺数，以探索由此产生的复杂的物理。 由于计算时间的要求，这些模拟，这是网格高达8192立方分辨率或等效的，是不可行的现有设施，并超过了什么是实验可能的。研究人员开发的先进算法将使模拟能够解决能源生产（湍流燃烧）和消费（运输）技术核心的基础研究问题。湍流燃烧是美国能源生产的一大部分的中心，而壁边界湍流是运输中固有的能量损失的核心。了解这些应用中湍流的详细动力学，将可能提高湍流影响的预测能力，从而通过改进设计提高性能。因此，拟议的DNS及其后续研究的主要影响将是燃烧和运输系统的工程。这反过来又会影响能源消耗和污染。

This award facilitates scientific research using the large new computational resource named Blue Waters being developed by IBM and scheduled to be deployed at the University of Illinois. It provides travel funds to support technical coordination between the principal investigators at Georgia Institute of Technology, University of Washington, San Diego Supercomputer Center, University of Texas at Austin, the Blue Waters project team and vendor technical team. The proposed research employs direct numerical simulations (DNS) to address research questions associated with three high Reynolds number turbulent flows: a turbulent non-premixed methane flame; scalar mixing and particle dispersion in isotropic turbulence; and turbulent wall-bounded flow in a channel. In each case, the central emphasis is on reaching sufficiently high Reynolds number to explore the resulting complex physics. Due to the compute time requirements, these simulations, which are on grids up to 8192-cubed resolution or equivalent, are not feasible on existing facilities and exceed what is experimentally possible. The advanced algorithms developed by the researchers will allow the simulations to address fundamental research issues at the heart of energy production (turbulent combustion) and consumption (transportation) technologies.Turbulent combustion is at the center of a large fraction of US energy production, and wall-bounded turbulence is central to the energy losses inherent in transportation. An understanding of the detailed dynamics of the turbulence in these applications will likely increase predictive capabilities of the effects of turbulence leading to enhanced performance through improved design. Thus, the primary impact of the proposed DNS and the subsequent research that it will enable will be on the engineering of combustion and transportation systems. This in turn affects energy consumption and pollution.