Turbulent flow

Wall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a fundamental viewpoint. One unexplained phenomenon is the formation and impact of coherent structures like the ejections of slow near-wall fluid into faster moving ow which have been shown to correlate with increases in friction drag. This thesis focuses on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the nonlinear nature of this phenomenon.
Deep Learning has provided new avenues of analyzing large amounts of data by applying techniques modeled after biological neurons. These techniques allow for the discovery of nonlinear relationships in massive, complex systems like the data found frequently in fluid dynamics simulation. Using a neural network architecture called Convolutional Neural Networks that specializes in uncovering spatial relationships, a network was trained to estimate the relative intensity of ejection structures within turbulent flow simulation without any a priori knowledge of the underlying flow dynamics. To explore the underlying physics that the trained network might reveal, an interpretation technique called Gradient-based Class Activation Mapping was modified to identify salient regions in the flow field which most influenced the trained network to make an accurate estimation of these organized structures. Using various statistical techniques, these salient regions were found to have a high correlation to ejection structures, and to high positive kinetic energy production, low negative production, and low energy dissipation regions within the flow. Additionally, these techniques present a general framework for identifying nonlinear causal structures in general three-dimensional data in any scientific domain where the underlying physics may be unknown.

Surface pressure fluctuations developed by turbulent flow within a boundary layer is a major cause of flow noise from a body and an issue which reveals itself over a wide range of engineering applications. Modified boundary layers (MBLs) inspired by the down coat of an owl’s wing has shown to reduce the acoustic effects caused by flow noise. This thesis investigates the mechanisms that modified boundary layers can provide for reducing the surface pressure fluctuations in a boundary layer. This study analyzes various types of MBLs in a wall jet wind tunnel through computational fluid dynamics and numerical surface pressure spectrum predictions. A novel surface pressure fluctuation spectrum model is developed for use in a wall jet boundary layer and demonstrates high accuracy over a range of Reynolds numbers. Non-dimensional parameters which define the MBL’s geometry and flow environment were found to have a key role in optimizing the acoustic performance.