Scientific Computing Research Environment for the Mathematical Sciences (SCREMS)

摘要：Traian Iliescu提案：0322852需要科学计算设备来快速求解大涡模拟（LES）模型。 特别是，研究人员研究了复杂工程流动的改进边界条件（LES发展的主要障碍之一）。 目前的模型遭受失败，以正确地捕捉子滤波器尺度运动的边界层理论是无效的，如再循环，分离流等的区域，最初的方法是使用近似反卷积方法的子滤波器尺度信息的部分恢复。 此外，研究人员打算研究LES可能发挥的作用，在发展中的降阶模型的状态估计反馈控制。 LES计算大尺度结构的能力，有效地和准确地可能是重要的实时状态估计所需的大多数流量控制applications.The研究人员打算开发一个科学的计算平台，用于解决一些建模问题（如适当的边界条件和卷积核）一般的三维湍流。 数学上更合理的模型将导致更好地理解流动现象，例如据信对全球海洋模型有重大影响的密度流。 更好的模型对于大气模型和其他地球物理流动也很重要。 此外，我们打算使用这个计算平台来解决一些实际的优化和控制问题，如控制执行器和流量传感器的最佳位置，以及快速（即近实时）的流体系统降阶模型的构建。

ABSTRACTPI: Traian IliescuProposal: 0322852Scientific computing equipment is being requested for rapid solution of large eddy simulation (LES) models. In particular, the investigators study improved boundary conditions for complex engineering flows (one of the main hurdles in the development of LES). Current models suffer from failure to correctly capture subfilter-scale motion in the regions where boundary layer theory is not valid such as recirculation, separated flows, etc. The initial approach is to use approximate deconvolution methods for partial recovery of subfilter scale information. Additionally, the investigators intend to study the role LES may play in the development of reduced-order modeling for state estimation in feedback control. The ability for LES to compute large scale structures efficiently and accurately could be important for the real-time state estimation required for most flow control applications.The investigators intend to develop a scientific computing platform for addressing a number of modeling issues (such as appropriate boundary conditions and convolution kernels) for general three-dimensional turbulent flows. More mathematically sound models will lead to better understanding of flow phenomena such as density currents believed to have a significant influence on global ocean models. Better models are also important for atmospheric models and other geophysical flows. Furthermore, we intend to use this computational platform to address a number of practical optimization and control issues such as the optimal placement of control actuators and flow sensors, and the construction of fast (i.e. near real-time) reduced-order models for fluid systems.