The observation of turbulence in the Florida Current is presented with the use of velocity measurements collected with an Acoustic Doppler Current Profiler (ADCP). The research is conducted through application of the theories of Taylor and Kolmogorov and related derivations, and processing tools of MATLAB software to this Eulerian observation of flow [1]. The velocity profile of the Florida Current is deduced in terms of its turbulent character with shear, acceleration, gradient, Reynolds Number, Reynolds Stress, Welch power spectrum density of current velocity, wavenumbers of Taylor’s hypothesis and Kolmogorov, wavenumber spectrum, eddy diameters, diapycnal diffusivity, and the Richardson Number. Processing methods are validated with results of other research conducted in the Florida Current with the use of a Multi-Scale Profiler, and an Advanced Microstructure Profiler for determination of shear, dissipation, diffusivity, and estimates of turbulent eddy diameters based on Taylor’s Hypothesis [1][4]. A spectral analysis is developed and is compared with Kolmogorov’s -5/3-Law. The process and the results of the analysis are described. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.