Structural dynamics.

As humans explore greater depths of Earth’s oceans, there is a growing need for the installation of subsea structures. 71% of the earth’s surface is ocean but there are limitations inherent in current detection instruments for marine applications leading to the need for the development of underwater platforms that allow research of deeper subsea areas. Several underwater platforms including Autonomous Underwater Vehicles (AUVs), Remote Operated Vehicles (ROVs), and wave gliders enable more efficient deployment of marine structures.
Deployable structures are able to be compacted and transported via AUV to their destination then morph into their final form upon arrival. They are a lightweight, compact solution. The wrapped package includes the deployable structure, underwater pump, and other necessary instruments, and the entire package is able to meet the payload capability requirements. Upon inflation, these structures can morph into final shapes that are a hundred times larger than their original volume, which extends the detection range and also provides long-term observation capabilities.
This dissertation reviews underwater platforms, underwater acoustics, imaging sensors, and inflatable structure applications then proposes potential applications for the inflatable structures. Based on the proposed applications, a conceptual design of an underwater tubular structure is developed and initial prototypes are built for the study of the mechanics of inflatable tubes. Numerical approaches for the inflation process and bending loading are developed to predict the inflatable tubular behavior during the structure’s morphing process and under different loading conditions. The material properties are defined based on tensile tests. The numerical results are compared with and verified by experimental data. The methods used in this research provide a solution for underwater inflatable structure design and analysis. Several ocean morphing structures are proposed based on the inflatable tube analysis.

An Ocean Current Turbine (OCT) numerical simulation for creating, testing and
tuning flight and power takeoff controllers, as well as for farm layout optimization is
presented. This simulation utilizes a novel approach for analytically describing oceanic
turbulence. This approach has been integrated into a previously developed turbine
simulation that uses unsteady Blade Element Momentum theory. Using this, the
dynamical response and power production of a single OCT operating in ambient
turbulence is quantified.
An approach for integrating wake effects into this single device numerical
simulation is presented for predicting OCT performance within a farm. To accomplish
this, far wake characteristics behind a turbine are numerically described using analytic
expressions derived from wind turbine wake models. These expressions are tuned to
match OCT wake characteristics calculated from CFD analyses and experimental data. Turbine wake is characterized in terms of increased turbulence intensities and decreased
mean wake velocities. These parameters are calculated based on the performance of the
upstream OCT and integrated into the environmental models used by downstream OCT.
Simulation results are presented that quantify the effects of wakes on downstream turbine
performance over a wide range of relative downstream and cross stream locations for
both moored and bottom mounted turbine systems. This is done to enable the
development and testing of flight and power takeoff controllers designed for maximizing
energy production and reduce turbine loadings.

The flow field behavior of axial flow turbines is of great importance, especially in
modern designs that may operate at a low Reynolds number. At these low Reynolds
numbers, the efficiency loss is significantly augmented compared to higher Reynolds
number flows. A detailed incompressible numerical study of a single stage axial-flow
turbine at a low Reynolds number is investigated with the use of multiple eddy-viscosity
turbulence models. The study includes epistemic uncertainty quantification as a form of
numerical error estimation. The numerical results show good qualitative and quantitative
agreement with experimental data. It was found that the shear stress transport (SST) k - ω turbulence model with rotation/curvature correction and inclusion of transition modeling
is most capable at predicting the mean velocity distribution, which is further enhanced
when the URANS formulation is employed. However, all the cases indicate a large
variation in the prediction of the root-mean-squared of the turbulent velocity fluctuations.