An experimental investigation of the interaction of water waves with flexible structures acting as breakwaters was carried out. Wave profiles, mapped out by water level measuring transducers, were studied to provide information on the performance of different breakwater models. A new signal analysis procedure for determining reflection coefficients based on wavelet theory was developed and compared to a conventional method. The reliability of using wavelet analysis to separate a partial standing wave into incident and reflected wave components was verified with a numerical example. It was also verified by the small variance in the estimates of the incident wave height from independent experimental measurements. Different geometries of rigid and flexible structures were constructed and examined. Reflection, transmission and energy loss coefficients were obtained over them. The influence of various properties of the models, such as the width and the internal pressure, on the effectiveness in reflecting or absorbing the incident wave energy was determined. Various factors which affect the performance of the breakwater, including the water depth, the wave length and the wave amplitude, were measured and documented. Suspended and bottom-mounted models were considered. The flow field over and near a hemi-cylindrical breakwater model was also examined using a flow visualization technique. An overall comparison among the models has also been provided. The results showed that the rectangular models, rigid and flexible, are the most effective structures to dissipate wave energy. The flow visualization technique indicated that the flow conforms with the circular geometry of a hemi-cylindrical breakwater model, yielding no flow separation. / Ph. D.

Time-Frequency decomposition is a signal processing method for analyzing and extracting information from aperiodic signals. Analysis of these signals are ineffective when done using the Fourier transform, instead these signals must be analyzed in the time and frequency domain simultaneously. The current tools for Time-Frequency analysis are either proprietary or computationally expensive making it prohibitive for researchers to use. This thesis investigates the computational aspects of signal processing with a focus on Time-Frequency analysis using wavelets. We develop algorithms that compute and plot the Time-Frequency decomposition automatically, and implement them in C++ as a framework. As a result our framework is significantly faster than MATLAB, and can be easily incorporated into applications that require Time-Frequency analysis. The framework is applied to identify the Event Related Spectral Perturbation of EEG signals; and to vibrational analysis by identifying the mechanical modal parameters of oscillating machines. / Thesis / Master of Applied Science (MASc)