2026-09-20T12:00:48Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/476462026-02-15T21:13:50Zcom_1773_4888col_1773_4940

Williams, Owen J.H. Kommalapati, Sahil 2021-08-26T18:13:23Z 2021-08-26T18:13:23Z 2021-08-26 2021 Kommalapati_washington_0250O_23284.pdf http://hdl.handle.net/1773/47646 Thesis (Master's)--University of Washington, 2021 Particle image velocimetry (PIV) techniques provide high fidelity measurements of mutliscale turbulent fluid motion. The research presented in this thesis broadly explores the utilization of Machine Learning for processing and analyzing non-time resolved PIV measurements of turbulent flows. The primary goal is to utilize bayesian inference to automate turbulent coherent structure identification in boundary layer flows. Automated identification was hitherto hindered by the lack of a priori information about the convecting velocities of the coherent structures. A Rankine vortex model is utilized to implement a Markov chain Monte Carlo-based vector matching to overcome this problem. Ultimately, a framework was developed for robust identification of vortices that is capable of visualizing the cumulative distributions of properties of all vortex structures in the flow to provide a rich description of multiscale turbulent coherent structures. Additionally, Bayesian inference is proven to outperform traditional optimization methods based on various loss metrics. The other major goal of this thesis is to implement super resolution in turbulent separated flows using neural network architectures. Machine learning based super resolution using PIV measurements is relatively unexplored, compared to its counterpart with CFD simulation. This is partly due to the unavailability of training datasets with sufficient magnitude. A large PIV dataset of non-time resolved measurements of turbulent separated flow with 50,000 snapshots was collected to overcome this problem. A neural network architecture was trained on this dataset to successfully outperform traditional interpolation based super resolution techniques. Finally, it has been shown that the current approach also accurately reproduced the properties of turbulent derived quantities, like the stream wise variance of velocity fluctuations, with better accuracy in comparison with interpolation based super resolution methods. application/pdf en_US CC BY bayesian inference data science machine learning particle image velocimetry super resolution turbulence Mechanical engineering Aerospace engineering Computer science Mechanical engineering Machine Learning for Coherent Structure Identification and Super Resolution in Turbulent flows Thesis Open Access open.access