Studying turbulent scale and space interactions using active grid wind tunnel and DNS database experiments

Johns Hopkins University, Baltimore MD

Investigators

Abstract

The proposed work will use novel approaches in both experimental and cyber-based fluid dynamics to address fundamental issues related to space and scale interactions in turbulent flows. The study of such interactions will impact predictive capabilities and help educate a new generation of students with know-how in integrated fluid dynamics experimentation and data-intensive science. To address multi-scale interactions in turbulence, a new type of active grid with fractal winglets will be built that injects kinetic energy over a broad range of scales but without generating a mean shear. To address spatial interactions in turbulence, another new active grid that produces a uniform turbulent kinetic energy gradient without mean velocity shear will be constructed. Data will be acquired in a wind-tunnel using thermal and particle image velocimetry. Results from these two new basic flows will shed light on the cascade, on the role of initial distribution of energy across scales, and on spatial diffusion rates. The project will also study the evolution of smaller-scale sub-portions of turbulent fluid. In order to follow in 3D the time-evolution of such fluid patches, we will use the recently developed Web-Services accessible public turbulence database that contains a 1024^4 space-time history of forced isotropic turbulence. Similarities and differences with the global evolution as known from the literature and the new experimental results will be studied providing the intellectual merit of the project, and also implications on subgrid-scale models for LES will be established. In terms of broader impacts, turbulence research as proposed here is part of a larger push to deal with systems with nonlinear complex behavior and many degrees of freedom. Large-scale numerical simulation of such systems is, today, at center stage of scientific discussions with important societal ramifications (global change, energy, etc.). Better integrating physical understanding, experimentation, and novel web-based analysis of large databases has the potential to generate new knowledge in the context of the emerging new paradigm of data-intensive science. Graduate education and training will stress the interplay between physical experimentation, simulation and the use of new cyber-based research tools.