Turbulence simulations: multiscale modeling and data-intensive computing methodologies
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Date
2014-02-05
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Johns Hopkins University
Abstract
In this two part work, methodologies for the multiscale modeling of complex turbulent flows and data-intensive computing strategies for large-scale turbulent simulations are developed and presented. The first part of this thesis is devoted to the simulation of turbulent flows over objects characterized by hierarchies of length-scale. Flows of this type present special challenges associated with the cost of resolving small-scale geometric elements. During large eddy simulation (LES), their effects on the resolved scales must be captured realistically through subgrid-scale models. Prior work performed by Chester et al., J. Comput. Phys. 2007 proposed a technique called renormalized numerical simulation (RNS), which is applicable to objects that display scale-invariant geometric (fractal) properties. The idea of RNS is similar to that of the dynamic model used in LES to determine model parameters for the subgrid-stress tensor model in the bulk of the flow. In RNS, drag forces from the resolved elements that are obtained during the simulation are re-scaled appropriately by determining drag coefficients that are then applied to specify the drag forces associated with the subgrid-scale elements. In the current work we introduce a generalized framework for describing and implementing the RNS methodology thereby extending the methodology first presented by Chester et al., 2007. Furthermore, we present various other possible practical implementations of RNS that differ on important, technical aspects related to 1) time averaging, 2) spatial localization, and 3) numerical representation of the drag forces. The new RNS framework is then applied to fractal tree canopies consisting of fractal-like trees with both planar cross-section and three dimensional orientations. The results indicate that the propsed time averaged, local, and explicit formulation of RNS is superior to the predecessor formulation as it enables the modeling of spatially non-homogenous geometries without using a low-level branch based description and preserves the assumed dynamic similary through temporal filtering. In addition, the overall predicted drag force of the non-planar fractal trees is shown to agree well with experimental data. In addition to RNS, a methodology for generating accurate inflow conditions in multiscale turbulence simulations is present. This technique called concurrent precursor simulation (CPS) allows the synchronous generation of inflow data from an upstream precursor simulation. This approach conceptually is the same as the standard precursor simulations (Lund et al., J. Comput. Phys. 1998 and Ferrante et al., J. Comput. Phys. 2004) used in the past, however, it eliminates the I/O bottleneck of disk reads and writes by transferring sampled data directly between domains using MPI. Furthermore, issues with recycling time scales of the sample inflow library are removed since the upstream, precursor simulation is performed concurrently with the target simulation. This methodology is applied to a single fractal tree (modeled using RNS) in turbulent duct flow and to a finite length, developing wind farm. In the second part of this work, data-intensive computing strategies addressing the large-scale data problem in direct numerical simulation (DNS) of turbulent flows are presented. DNS provides the highest fidelity of predicited turbulence data. As a result, these data have served a vital in role in turbulence research and access to such data is key to continued development of the field. Classical approaches to the management and dissemination of these large-scale datasets, however, has proven to be cumbersome and prohibitively expensive in some instances thus minimizing the usefulness of these data to a broad community. Therefore, the Johns Hopkins Turbulent Databases (JHTDB) (Perlman et al., Supercomput. SC ’07 and Li et al., J. Turbul. 2008) have been created which expose large-scale turbulence datasets to the reasearch community worldwide using Web services. The JHTDB project provides Web service libraries for C, Fortran, and Matlab which allow interaction with the DNS data. The design and implementation of the Matlab interface along with several examples are presented. Also, the first Web service based, publicly available channel flow DNS database is produced in this work. The implementation of the channel flow DNS and construction of the subsequent database are presented. These data are then used to study the structure and organization of channel flow turbulence. In this study, the Q criterion (Hunt et al., Stud. Turbul. Using Numer. Simul. Databases 1998) is employed to measure the vortex sizes and organization. Results appear to indicate good, qualitative agreement with theoretical predictions with respect to the presence of large-scale near wall structures and the preponderance of buffer layer vortices.
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Keywords
turbulence, numerical simulation, large eddy simulation, direct numerical simulation, subgrid-scale modeling, data-intensive computing