Department of Ocean and Mechanical Engineering

This thesis reports the development of a novel drug delivery system consisting of hollow nanoparticles, formed from manganese dioxide (δ-MnO2) sheets, that are coated with polydopamine and folic acid to selectively target cancer cells. The biodegradability and colloidal stability of the uncoated hollow nanoparticles were investigated in comparison to solid MnO2 nanoparticles and graphene oxide sheets. The MnO2 hollow nanoparticles degraded at a faster rate and seem to have a higher surface area and better colloidal dispersion than solid MnO2 nanoparticles. Xanthan gum was proven to improve colloidal dispersion of these hollow nanoparticles and were used for further cell studies. In this study, cancer and healthy cells were treated with coated hollow nanoparticles, and results indicate that this novel hollow nanoparticle may preferentially target and kill cancer cells. Particle aggregation has shown to be toxic to cells. Further studies with this novel drug delivery system may lead to a groundbreaking solution to targeted cancer therapy.

Shear sheltering is defined as the effect of the mean flow velocity profile in a boundary layer on the turbulence caused by an imposed gust. In aeroacoustic applications turbulent boundary layers interacting with blade trailing edges or roughness elements are an important source of sound, and the effect of shear sheltering on these noise sources has not been studied in detail. Since the surface pressure spectrum below the boundary layer is the primary driver of trailing edge and roughness noise, this thesis considers the effect that shear sheltering has on the surface pressure spectrum below a boundary layer. This study presents a model of the incoming turbulence as a vortex sheet at a specified height above the surface and shows, using canonical boundary layers and approximations to numerical results, how the mean flow velocity profile can be manipulated to alter the surface pressure spectrum and hence the associated trailing edge noise. The results from this model demonstrate that different mean velocity profiles drive significant changes in the unsteady characteristics of the flow. The surface pressure fluctuations results also suggest that boundary layers where the shear in the mean velocity profile is significant can be beneficial for the reduction of trailing edge noise at particular frequencies.

When a liquid drains through a hole in a container, a vortex may form between the surface and the drainage hole. An interesting phenomenon occurs in the presence of two drainage holes. Only one vortex forms, while the other hole will mostly drain as sink flow. In addition, the vortex can switch between one hole and the other with regular periodicity. The primary goal of this study is to measure this periodicity under varying conditions (height of water in the container, diameter of the drainage holes, and distance between drainage holes). Additionally, a study concerning the volume flow rates of vortical vs. sink flow out of the drainage holes was conducted. In the case of two drainage holes, when the height of the water was decreased in the container, the diameter of drainage holes decreased, or the distance between drainage holes was increased, the switching period was shown to decrease.

In order to study the mechanical performance of dry-cast synthetic fiber reinforced concrete (SynFRC), samples of varying geometry, fiber content, and environmental exposure were developed and tested using the modified indirect tensile test. The samples created consisted of three different thicknesses (with two different geometries), and six different fiber contents that differed in either type, or quantity, of fibers. Throughout the duration of this research, procedures for inflicting detrimental materials into the concrete samples were employed at a number of different environments by implementing accelerated rates of deterioration using geometric adjustments, increased temperature exposure, wetting/drying cycles, and preparation techniques. The SynFRC samples studied were immersed in a wide range of environments including: the exposure of samples to high humidity and calcium hydroxide environments, which served at the control group, while the sea water, low pH, and barge conditioning environments were used to depict the real world environments similar to what would be experienced in the
Florida ecosystem. As a result of this conditioning regime, the concrete was able to imitate the real-world effects that the environments would have inflicted if exposed for long durations after an exposure period of only 20-24 months. Having adequately conditioned the samples in their respective environments, they were then tested (and forensically investigated) using the modified indirect tensile testing method to gather data regarding each sample’s toughness and load handling capability. By analyzing the results from each sample, the toughness was calculated by taking the area under the force displacement curve. From these toughness readings it was found that possible degradation occurred between the fiber-matrix interface of some of the concrete samples conditioned in the Barge environment. From these specimens that were immersed in the barge environment, a handful of them exhibited multiple episodes of strain softening characteristics within their force displacement curves. In regard to the fibers used within the samples, the PVA fibers tended to pull off more while the Tuff Strand SF fibers had the highest tendency to break (despite some of the fibers showing similar pull off and breaking failure characteristics). When it comes to the overall thickness of the sample, there was clear correlation between the increase in size and the increase in sample toughness, however the degree to which it correlates varies from sample to sample.

In this research an attempt is made at explaining the physical processes behind energy dissipation during wave breaking, through spectral analysis of the resulting sound. The size of an air bubble can be directly linked to the frequency of the sound that is heard using the simple harmonic solution to the Rayleigh–Plesset equation. It indicates the inverse relationship between frequency and bubble size. And this relationship has been used to identify wave breaking in general [MANASSEH 2006]. Now this research goes a step farther and looks at how the frequency spectrum of the sound changes with time, in an effort to understand the general pattern and from that to deduce an empirical equation that describes the breaking down of turbulence during a wave breaking event.
Two main processes have been identified, with the second process having three main indicators that are necessary to evidence wave breaking. The first process is a near instantaneous shattering of the initial air bubble into much smaller metastable bubbles of a size that appears to be common for all waves independent of wave height. Then in the second process, the bubbles continue to break down following a recognisable pattern.

Noise prediction methods are necessary in aspects of aerodynamic and hydrodynamic engineering. Predictive models of noise from rotating machinery ingesting turbulence is of much interest and relatively recently studied. This thesis presents a numerical method processed in a series of three codes that was written and edited to receive input for geometrical features of rotating machinery, as well as, adjustments to turbulent operating conditions. One objective of this thesis was to create a platform of analysis for any rotor design to obtain five parameters necessary for noise prediction; 1) the hydrodynamic inflow angle to each blade section, 2) chord length as a function of radius, 3) the cylindrical radius of each blade section, 4) & 5) the leading edge as a function of span in both the rotor-plane and as a function of axial distance downstream. Another objective of this thesis was to use computational fluid dynamics (CFD), specifically by using a Reynold’s-Averaged Navier-Stokes (RANS) Shear Stress Transport (SST) 𝑘 − 𝜔 model simulation in ANSYS Fluent, to obtain the turbulent kinetic energy distribution, also necessary in the noise prediction method presented. The purpose of collecting the rotor geometry data and turbulent kinetic energy data was to input the values into the first of the series of codes and run the calculation so that the output spectra could be compared to experimental noise measurements conducted at the Stability Wind Tunnel at Virginia Tech. The comparison shows that the prediction method results in data that can be reliable if careful attention is payed to the input parameters and the length scale used for analysis. The significance of this research is the noise prediction method presented and used simplifies the model of turbulence by using a correlation function that can be determined by a one-dimensional function while also simplifying the iterations completed on rotor blade to calculate the unsteady forces.

Numerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable chaos, without requiring any a-priori knowledge about the underlying dynamics. The autonomous decisions taken by the RL algorithm are analyzed to understand how the system’s dynamics are impacted. Learning from this analysis, a simple control-law capable of restoring chaotic behavior is formulated. The reverse-engineering approach adopted in this work underlines the immense potential of the techniques used here to discover effective control strategies in complex dynamical systems. The autonomous nature of the learning algorithm makes it applicable to a diverse variety of non-linear systems, and highlights the potential of RLenabled control for regulating other transient-chaos like catastrophic events.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.