Mechanical Engineering & Applied Mechanics Doctoral Workhttps://hdl.handle.net/10365/325662024-03-29T06:06:22Z2024-03-29T06:06:22ZInfluences of Seawater Flows on the Degradation of Organic Coatings Applied on Offshore Wind TurbinesVedadi, Aminhttps://hdl.handle.net/10365/334002023-12-20T18:10:02Z2022-01-01T00:00:00ZInfluences of Seawater Flows on the Degradation of Organic Coatings Applied on Offshore Wind Turbines
Vedadi, Amin
The regular protection methods of offshore wind structures consist of the application of two or three layers of epoxy-based coatings with a polyurethane topcoat. The coating systems of offshore wind turbines are mostly tested on-site, where different coated samples are exposed to the sea water at the specific locations planned for the installations of the turbines. Despite several advantages of laboratory testing, the majority of laboratory-based tests have been limited to the exposure of coated or unprotected metals to stationary electrolytic solutions, while the flow-induced corrosion measurements have not received enough attention until recently. The focus of our work is to investigate the influence of applied mechanical stresses due to the water flow on the degradation of organic coatings. In order to resemble the condition of coated monopile structures in shallow water flow, an impingement chamber device and a wave tank were designed and constructed. The Electrochemical Impedance Stereoscopy (EIS) method was utilized for monitoring the electrochemical processes occurred during the degradation of coatings. Computational Fluid Dynamic (CFD) method, as well as Particle Image Velocimetry (PIV) tests were utilized in order to calculate the magnitude of applied stresses on the coating surfaces. Atomic Force Microscopy method (AFM) was employed for characterizations of coating’ surfaces. The theory of thermo-activated processes in combination with the thermoelasticity equations were derived in a way to calculate the influence of applied stresses on different electrochemical parameters of the coatings’ degradation. The afore-mentioned experimental methods and the developed analytical procedure can potentially predict the behavior of organic coatings applied on offshore wind turbines at different exposure zones with respect to the sea water flow.
2022-01-01T00:00:00ZThe Effects of Surface Roughness on the Functionality of Titanium Based Alloy Ti13Zb13Zr Orthopedic ImplantsJahani, Babakhttps://hdl.handle.net/10365/326892022-06-07T14:50:38Z2021-01-01T00:00:00ZThe Effects of Surface Roughness on the Functionality of Titanium Based Alloy Ti13Zb13Zr Orthopedic Implants
Jahani, Babak
In this study, the effects of surface roughness on the wettability, cell attachment, and mechanical properties of titanium-based Ti13Nb13Zr orthopedic implants have been investigated. The aim of this multidisciplinary research was to find an optimum range of surface roughness for Ti13Nb13Zr orthopedic implants that could maximize the attachment and the proliferation of cells and improve the wettability of the surface, without adversely affecting the mechanical strength of the implants. There have been some published research works that support the existence of relations between roughness and the functionality of implants, but still, an optimum roughness that can satisfy all of the orthopedic requirements, either is not fully studied or not published.
It was seen that the performance of orthopedic implants depends on multiple paradoxical parameters. The results of this study on Ti13Nb13Zr show, even though increasing the value of surface roughness can increase the initial phase of cell attachment onto the surface of Ti13Nb13Zr implants, other major functions such as wettability and mechanical properties can be influenced adversely. Through an experimental methodology, this study proposes an optimum range of roughness, which meets all three major functions of cell attachment, mechanical properties, and wettability. In respect to the recent serious health concerns reported over the implants made of Ti6Al4V which is a common material in the implant industry, scientists and researchers are currently working to introduce a better biomaterial. In this study, Ti13Nb13Zr which is a new and advanced titanium-based biomaterial with improved biocompatibility and more desired mechanical properties was selected and studied. The reason for this selection backs to the fact that Ti13Nb13Zr does not release toxic ions (such as Al and V ions) and its mechanical properties are closer to the bone in comparison to many titanium alloys such as Ti6Al4V.
2021-01-01T00:00:00ZSimulation-Based Optimization and Artificial Intelligence Techniques for Macromechanical and Micromechanical Characterization of Soft Biological TissuesRamzanpour, Mohammadrezahttps://hdl.handle.net/10365/326212022-05-31T18:51:18Z2021-01-01T00:00:00ZSimulation-Based Optimization and Artificial Intelligence Techniques for Macromechanical and Micromechanical Characterization of Soft Biological Tissues
Ramzanpour, Mohammadreza
Traumatic brain injury (TBI) is a serious health and socioeconomic issue which affects thousands of lives annually in the United States. Computational simulations play an important role in better understanding of the TBI and on how it happens. Having accurate material properties of the brain tissue and the elements of the brain will help with more accurate computational simulations. Material characterization is therefore the line on which lots of research have been conducted. In recent years, the emerge of data driven approaches has led to better and more accurate soft tissue characterization. In this dissertation, a metaheuristic search optimization method together with simulation-based optimization framework, and artificial intelligence-based approaches have been employed for macromechanical and micromechanical characterization of brain tissue. First, a constrained particle swarm optimization (C-PSO) technique has been established for soft tissue characterization that overcomes the shortcomings of the classical optimization methods. Through the application of the inherent constraints in the hyperelastic and visco-hyperelastic models, it became possible to reduce the time complexity of this optimization algorithm. Subsequently, the developed constrained optimization method was employed to create simulation-based optimization frameworks for characterizing the micro-level constituents of human brain white matter including axons and extracellular matrix using the hyperelastic and visco-hyperelastic constitutive models. This simulation-based optimization framework helps the researchers to go around the complexities involved with the experimental techniques on micro-level characterization of soft tissues. The final part of this dissertation is devoted to the development of the machine learning and deep learning techniques for classifying the tissue stiffness out of the finite element (FE) simulation results. Through the training of a regularized logistic regression and deep learning convolutional neural networks, it became possible to correctly predict more than 91% of the cases of tissues with high stiffness. The tissues with high stiffness are usually indicative of the pathology and hence are important from medical perspective. The outcome of this part of the work could be useful for qualitative description of the soft biological tissue stiffness and pathology diagnosis which can be used as an alternative to the inversion algorithms.
2021-01-01T00:00:00ZDynamic Stall Characteristics of Pitching Finite-Aspect-Ratio WingsUllah, Al Habibhttps://hdl.handle.net/10365/324652022-05-24T14:22:00Z2021-01-01T00:00:00ZDynamic Stall Characteristics of Pitching Finite-Aspect-Ratio Wings
Ullah, Al Habib
In this study, an experimental investigation was performed to characterize the dynamic stall of pitching wings and provide confirmation of the existence of the arch-shaped vortex for moderate sweep wing. Dynamic stall is a complex flow, which happens because of a sudden change of incident angle during the pitching motion. The pitching motion of a wing can cause instability in the shear layer and generate the separation burst at certain angles. For a pitching wing, the dynamic stall vortex is characterized by the formation of an arch-shaped vortex to the evolution of a ring-shaped vortex. The leg of the arch-shaped vortex causes a negative pressure region on the airfoil surface and can, in fact, generate greater lift. However, in certain conditions, the detachment of the arch-shaped vortex from the airfoil surface can cause high pressure and vibration in the structures. The formation of the arch-shaped vortex and its evolution were systematically investigated using cutting-edge flow diagnostic techniques, and the physics of the dynamic stall is explained in addition to providing the confirmation of the theory developed based on Computational Fluid Dynamics.
The study was done using Particle Image Velocimetry (PIV) and Pressure-Sensitive Paint for three sweep angle wings. The wings, with an aspect ratio of AR=4 and a NACA 0012 section assembled with round-tip, are considered for the current experimental study. The sweep angles = 0, 15, and 30 degrees were considered to compare the flow phenomena. The PIV results show the formation of a laminar separation bubble and its evolution to a dynamic stall vortex. The increase of sweep angle causes the formation of such vortices towards the wing tip. In the process of finding the footprint of the vortices and pressure distribution on the surface of the wings, a single-shot lifetime method using fast porous paint was used. The results show the existence of suction pressure and later grows towards the trailing edge of the wing due to the formation of a dynamic stall vortex. In addition, at Re=2x10^5 and reduced frequency k=0.2, a moderate sweep airfoil shows the apparent footprint of the arch-shaped vortex, which confirms the current theory.
2021-01-01T00:00:00Z