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- All Subjects: Mechanical Engineering
- All Subjects: Materials Science
- Creators: Solanki, Kiran
Description
Recently, the use of zinc oxide (ZnO) nanowires as an interphase in composite materials has been demonstrated to increase the interfacial shear strength between carbon fiber and an epoxy matrix. In this research work, the strong adhesion between ZnO and carbon fiber is investigated to elucidate the interactions at the interface that result in high interfacial strength. First, molecular dynamics (MD) simulations are performed to calculate the adhesive energy between bare carbon and ZnO. Since the carbon fiber surface has oxygen functional groups, these were modeled and MD simulations showed the preference of ketones to strongly interact with ZnO, however, this was not observed in the case of hydroxyls and carboxylic acid. It was also found that the ketone molecules ability to change orientation facilitated the interactions with the ZnO surface. Experimentally, the atomic force microscope (AFM) was used to measure the adhesive energy between ZnO and carbon through a liftoff test by employing highly oriented pyrolytic graphite (HOPG) substrate and a ZnO covered AFM tip. Oxygen functionalization of the HOPG surface shows the increase of adhesive energy. Additionally, the surface of ZnO was modified to hold a negative charge, which demonstrated an increase in the adhesive energy. This increase in adhesion resulted from increased induction forces given the relatively high polarizability of HOPG and the preservation of the charge on ZnO surface. It was found that the additional negative charge can be preserved on the ZnO surface because there is an energy barrier since carbon and ZnO form a Schottky contact. Other materials with the same ionic properties of ZnO but with higher polarizability also demonstrated good adhesion to carbon. This result substantiates that their induced interaction can be facilitated not only by the polarizability of carbon but by any of the materials at the interface. The versatility to modify the magnitude of the induced interaction between carbon and an ionic material provides a new route to create interfaces with controlled interfacial strength.
ContributorsGalan Vera, Magdian Ulises (Author) / Sodano, Henry A (Thesis advisor) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Oswald, Jay (Committee member) / Speyer, Gil (Committee member) / Arizona State University (Publisher)
Created2013
Description
Smoke entering a flight deck cabin has been an issue for commercial aircraft for many years. The issue for a flight crew is how to mitigate the smoke so that they can safely fly the aircraft. For this thesis, the feasibility of having a Negative Pressure System that utilizes the cabin altitude pressure and outside altitude pressure to remove smoke from a flight deck was studied. Existing procedures for flight crews call for a descent down to a safe level for depressurizing the aircraft before taking further action. This process takes crucial time that is critical to the flight crew's ability to keep aware of the situation. This process involves a flight crews coordination and fast thinking to manually take control of the aircraft; which has become increasing more difficult due to the advancements in aircraft automation. Unfortunately this is the only accepted procedure that is used by a flight crew. Other products merely displace the smoke. This displacement is after the time it takes for the flight crew to set up the smoke displacement unit with no guarantee that a flight crew will be able to see or use all of the aircraft's controls. The Negative Pressure System will work automatically and not only use similar components already found on the aircraft, but work in conjunction with the smoke detection system and pressurization system so smoke removal can begin without having to descend down to a lower altitude. In order for this system to work correctly many factors must be taken into consideration. The size of a flight deck varies from aircraft to aircraft, therefore the ability for the system to efficiently remove smoke from an aircraft is taken into consideration. For the system to be feasible on an aircraft the cost and weight must be taken into consideration as the added fuel consumption due to weight of the system may be the limiting factor for installing such a system on commercial aircraft.
ContributorsDavies, Russell (Author) / Rogers, Bradley (Thesis advisor) / Palmgren, Dale (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving reliable composite systems is a strong capability of assessing and inspecting physical damage of critical structural components. Installation of a robust Structural Health Monitoring (SHM) system would be very valuable in detecting the onset of composite failure. A number of major issues still require serious attention in connection with the research and development aspects of sensor-integrated reliable SHM systems for composite structures. In particular, the sensitivity of currently available sensor systems does not allow detection of micro level damage; this limits the capability of data driven SHM systems. As a fundamental layer in SHM, modeling can provide in-depth information on material and structural behavior for sensing and detection, as well as data for learning algorithms. This dissertation focusses on the development of a multiscale analysis framework, which is used to detect various forms of damage in complex composite structures. A generalized method of cells based micromechanics analysis, as implemented in NASA's MAC/GMC code, is used for the micro-level analysis. First, a baseline study of MAC/GMC is performed to determine the governing failure theories that best capture the damage progression. The deficiencies associated with various layups and loading conditions are addressed. In most micromechanics analysis, a representative unit cell (RUC) with a common fiber packing arrangement is used. The effect of variation in this arrangement within the RUC has been studied and results indicate this variation influences the macro-scale effective material properties and failure stresses. The developed model has been used to simulate impact damage in a composite beam and an airfoil structure. The model data was verified through active interrogation using piezoelectric sensors. The multiscale model was further extended to develop a coupled damage and wave attenuation model, which was used to study different damage states such as fiber-matrix debonding in composite structures with surface bonded piezoelectric sensors.
ContributorsMoncada, Albert (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2012
Description
Aluminum alloys and their composites are attractive materials for applications requiring high strength-to-weight ratios and reasonable cost. Many of these applications, such as those in the aerospace industry, undergo fatigue loading. An understanding of the microstructural damage that occurs in these materials is critical in assessing their fatigue resistance. Two distinct experimental studies were performed to further the understanding of fatigue damage mechanisms in aluminum alloys and their composites, specifically fracture and plasticity. Fatigue resistance of metal matrix composites (MMCs) depends on many aspects of composite microstructure. Fatigue crack growth behavior is particularly dependent on the reinforcement characteristics and matrix microstructure. The goal of this work was to obtain a fundamental understanding of fatigue crack growth behavior in SiC particle-reinforced 2080 Al alloy composites. In situ X-ray synchrotron tomography was performed on two samples at low (R=0.1) and at high (R=0.6) R-ratios. The resulting reconstructed images were used to obtain three-dimensional (3D) rendering of the particles and fatigue crack. Behaviors of the particles and crack, as well as their interaction, were analyzed and quantified. Four-dimensional (4D) visual representations were constructed to aid in the overall understanding of damage evolution. During fatigue crack growth in ductile materials, a plastic zone is created in the region surrounding the crack tip. Knowledge of the plastic zone is important for the understanding of fatigue crack formation as well as subsequent growth behavior. The goal of this work was to quantify the 3D size and shape of the plastic zone in 7075 Al alloys. X-ray synchrotron tomography and Laue microdiffraction were used to non-destructively characterize the volume surrounding a fatigue crack tip. The precise 3D crack profile was segmented from the reconstructed tomography data. Depth-resolved Laue patterns were obtained using differential-aperture X-ray structural microscopy (DAXM), from which peak-broadening characteristics were quantified. Plasticity, as determined by the broadening of diffracted peaks, was mapped in 3D. Two-dimensional (2D) maps of plasticity were directly compared to the corresponding tomography slices. A 3D representation of the plastic zone surrounding the fatigue crack was generated by superimposing the mapped plasticity on the 3D crack profile.
ContributorsHruby, Peter (Author) / Chawla, Nikhilesh (Thesis advisor) / Solanki, Kiran (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2014
Description
Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.
ContributorsHuff, Daniel W (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Chakrabarti, Chaitali (Committee member) / Chattopadhyay, Aditi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Damage detection in heterogeneous material systems is a complex problem and requires an in-depth understanding of the material characteristics and response under varying load and environmental conditions. A significant amount of research has been conducted in this field to enhance the fidelity of damage assessment methodologies, using a wide range of sensors and detection techniques, for both metallic materials and composites. However, detecting damage at the microscale is not possible with commercially available sensors. A probable way to approach this problem is through accurate and efficient multiscale modeling techniques, which are capable of tracking damage initiation at the microscale and propagation across the length scales. The output from these models will provide an improved understanding of damage initiation; the knowledge can be used in conjunction with information from physical sensors to improve the size of detectable damage. In this research, effort has been dedicated to develop multiscale modeling approaches and associated damage criteria for the estimation of damage evolution across the relevant length scales. Important issues such as length and time scales, anisotropy and variability in material properties at the microscale, and response under mechanical and thermal loading are addressed. Two different material systems have been studied: metallic material and a novel stress-sensitive epoxy polymer.
For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.
Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.
Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
ContributorsZhang, Jinjun (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this work, the hydrodynamics of Suction Stabilization is studied. Suction stabilization was found to stabilize floating platforms/floats in a much better way as compared to the conventional methods. This was achieved by an effective increment in the metacentric height due to the Inverse Slack Tank (IST) effect. The study involves the analysis of the existing designs and optimizing its performance. This research investigates the stability of such floats and the hydrodynamic forces acting on the same for offshore applications, such as wind turbines. A simple mathematical model for the condition of parametric resonance is developed and the results are verified, both analytically and experimentally.
ContributorsCherangara Subramanian, Susheelkumar (Author) / Redkar, Sangram (Thesis advisor) / Rajadas, John (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2014
Description
Fission products in nuclear fuel pellets can affect fuel performance as they change the fuel chemistry and structure. The behavior of the fission products and their release mechanisms are important to the operation of a power reactor. Research has shown that fission product release can occur through grain boundary (GB) at low burnups. Early fission gas release models, which assumed spherical grains with no effect of GB diffusion, did not capture the early stage of the release behavior well. In order to understand the phenomenon at low burnup and how it leads to the later release mechanism, a microstructurally explicit model is needed. This dissertation conducted finite element simulations of the transport behavior using 3-D microstructurally explicit models. It looks into the effects of GB character, with emphases on conditions that can lead to enhanced effective diffusion. Moreover, the relationship between temperature and fission product transport is coupled to reflect the high temperature environment.
The modeling work began with 3-D microstructure reconstruction for three uranium oxide samples with different oxygen stoichiometry: UO2.00 UO2.06 and UO2.14. The 3-D models were created based on the real microstructure of depleted UO2 samples characterized by Electron Backscattering Diffraction (EBSD) combined with serial sectioning. Mathematical equations on fission gas diffusion and heat conduction were studied and derived to simulate the fission gas transport under GB effect. Verification models showed that 2-D elements can be used to model GBs to reduce the number of elements. The effect of each variable, including fuel stoichiometry, temperature, GB diffusion, triple junction diffusion and GB thermal resistance, is verified, and they are coupled in multi-physics simulations to study the transport of fission gas at different radial location of a fuel pellet. It was demonstrated that the microstructural model can be used to incorporate the effect of different physics to study fission gas transport. The results suggested that the GB effect is the most significant at the edge of fuel pellet where the temperature is the lowest. In the high temperature region, the increase in bulk diffusivity due to excess oxygen diminished the effect of GB diffusion.
The modeling work began with 3-D microstructure reconstruction for three uranium oxide samples with different oxygen stoichiometry: UO2.00 UO2.06 and UO2.14. The 3-D models were created based on the real microstructure of depleted UO2 samples characterized by Electron Backscattering Diffraction (EBSD) combined with serial sectioning. Mathematical equations on fission gas diffusion and heat conduction were studied and derived to simulate the fission gas transport under GB effect. Verification models showed that 2-D elements can be used to model GBs to reduce the number of elements. The effect of each variable, including fuel stoichiometry, temperature, GB diffusion, triple junction diffusion and GB thermal resistance, is verified, and they are coupled in multi-physics simulations to study the transport of fission gas at different radial location of a fuel pellet. It was demonstrated that the microstructural model can be used to incorporate the effect of different physics to study fission gas transport. The results suggested that the GB effect is the most significant at the edge of fuel pellet where the temperature is the lowest. In the high temperature region, the increase in bulk diffusivity due to excess oxygen diminished the effect of GB diffusion.
ContributorsLim, Harn Chyi (Author) / Peralta, Pedro (Thesis advisor) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2014
Description
The United States Department of Energy (DOE) has always held the safety and reliability of the nation's nuclear reactor fleet as a top priority. Continual improvements and advancements in nuclear fuels have been instrumental in maximizing energy generation from nuclear power plants and minimizing waste. One aspect of the DOE Fuel Cycle Research and Development Advanced Fuels Campaign is to improve the mechanical properties of uranium dioxide (UO2) for nuclear fuel applications.
In an effort to improve the performance of UO2, by increasing the fracture toughness and ductility, small quantities of oxide materials have been added to samples to act as dopants. The different dopants used in this study are: titanium dioxide, yttrium oxide, aluminum oxide, silicon dioxide, and chromium oxide. The effects of the individual dopants and some dopant combinations on the microstructure and mechanical properties are determined using indentation fracture experiments in tandem with scanning electron microscopy. Indentation fracture experiments are carried out at room temperature and at temperatures between 450 °C and 1160 °C.
The results of this work find that doping with aluminosilicate produces the largest favorable change in the mechanical properties of UO2. This sample exhibits an increase in fracture toughness at room temperature without showing a change in yield strength at elevated temperatures. The results also show that doping with Al2O3 and TiO2 produce stronger samples and it is hypothesized that this is a result of the sample containing dopant-rich secondary phase particles.
In an effort to improve the performance of UO2, by increasing the fracture toughness and ductility, small quantities of oxide materials have been added to samples to act as dopants. The different dopants used in this study are: titanium dioxide, yttrium oxide, aluminum oxide, silicon dioxide, and chromium oxide. The effects of the individual dopants and some dopant combinations on the microstructure and mechanical properties are determined using indentation fracture experiments in tandem with scanning electron microscopy. Indentation fracture experiments are carried out at room temperature and at temperatures between 450 °C and 1160 °C.
The results of this work find that doping with aluminosilicate produces the largest favorable change in the mechanical properties of UO2. This sample exhibits an increase in fracture toughness at room temperature without showing a change in yield strength at elevated temperatures. The results also show that doping with Al2O3 and TiO2 produce stronger samples and it is hypothesized that this is a result of the sample containing dopant-rich secondary phase particles.
ContributorsMcDonald, Robert (Author) / Peralta, Pedro (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2014