Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 75-77).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1981. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 243-246).

Thesis--Wisconsin. / Vita. Includes bibliographical references (leaves 189-191).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1973. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin--Madison, 1963. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 70-71).

Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2006. / Paul Mayne, Committee Member ; James Tsai, Committee Member ; Glenn Rix, Committee Member ; David Frost, Committee Member ; Carlos Santamarina, Committee Chair.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3884. Advisers: Ronald J. Adrian; S. Balachandar. Includes bibliographical references (leaves 120-123) Available on microfilm from Pro Quest Information and Learning.

Thesis (M. Eng.)(Mechanical)--University of Pretoria, 2005. / Summaries in English and Afrikaans. Includes bibliographical references. Available on the Internet via the World Wide Web.

abstract: Over the past three decades, particle image velocimetry (PIV) has been continuously growing to become an informative and robust experimental tool for fluid mechanics research. Compared to the early stage of PIV development, the dynamic range of PIV has been improved by about an order of magnitude (Adrian, 2005; Westerweel et al., 2013). Further improvement requires a breakthrough innovation, which constitutes the main motivation of this dissertation. N-pulse particle image velocimetry-accelerometry (N-pulse PIVA, where N>=3) is a promising technique to this regard. It employs bursts of N pulses to gain advantages in both spatial and temporal resolution. The performance improvement by N-pulse PIVA is studied using particle tracking (i.e. N-pulse PTVA), and it is shown that an enhancement of at least another order of magnitude is achievable. Furthermore, the capability of N-pulse PIVA to measure unsteady acceleration and force is demonstrated in the context of an oscillating cylinder interacting with surrounding fluid. The cylinder motion, the fluid velocity and acceleration, and the fluid force exerted on the cylinder are successfully measured. On the other hand, a key issue of multi-camera registration for the implementation of N-pulse PIVA is addressed with an accuracy of 0.001 pixel. Subsequently, two applications of N-pulse PTVA to complex flows and turbulence are presented. A novel 8-pulse PTVA analysis was developed and validated to accurately resolve particle unsteady drag in post-shock flows. It is found that the particle drag is substantially elevated from the standard drag due to flow unsteadiness, and a new drag correlation incorporating particle Reynolds number and unsteadiness is desired upon removal of the uncertainty arising from non-uniform particle size. Next, the estimation of turbulence statistics utilizes the ensemble average of 4-pulse PTV data within a small domain of an optimally determined size. The estimation of mean velocity, mean velocity gradient and isotropic dissipation rate are presented and discussed by means of synthetic turbulence, as well as a tomographic measurement of turbulent boundary layer. The results indicate the superior capability of the N-pulse PTV based method to extract high-spatial-resolution high-accuracy turbulence statistics. / Dissertation/Thesis / Animation of N-pulse PIVA measurement of flow-structure interaction / Doctoral Dissertation Mechanical Engineering 2018

Organizing data into groups using unsupervised learning algorithms such as k-means clustering and GMM are some of the most widely used techniques in data exploration and data mining. As these clustering algorithms are iterative by nature, for big datasets it is increasingly challenging to find clusters quickly. The iterative nature of k-means makes it inherently difficult to optimize such algorithms for modern hardware, especially as pushing data through the memory hierarchy is the main bottleneck in modern systems. Therefore, performing on-the-fly unsupervised learning is particularly challenging. In this thesis, we address this challenge by presenting an ensemble of algorithms to provide hardware-aware clustering along with a road-map for hardware-aware machine learning algorithms. We move beyond simple yet aggressive parallelization useful only for the embarrassingly parallel parts of the algorithms by employing data reduction, re-factoring of the algorithm, as well as, parallelization through SIMD commands of a general purpose processor. We find that careful engineering employing the SIMD instructions available by the processor and hand-tuning reduces response time by about 4 times. Further, by reducing both data dimensionality and data-points by PCA and then coreset-based sampling we get a very good representative sample of the dataset. Running clustering on the reduced dataset, we achieve a significant speedup. This data reduction technique reduces data dimensionality and data-points, effectively reducing the cost of the k-means algorithm by reducing the number of iteration and the total amount of computations. Last but not least, using we can save pre-computed data to compute cluster variations on the fly. Compared to the state of the art using k-means++, our approach offers comparable accuracy while running about 14 times faster, by moving less data fewer times through the memory hierarchy.