Matching Items (40)
Filtering by
- All Subjects: Mechanical Engineering
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
Shock loading is a complex phenomenon that can lead to failure mechanisms such as strain localization, void nucleation and growth, and eventually spall fracture. Studying incipient stages of spall damage is of paramount importance to accurately determine initiation sites in the material microstructure where damage will nucleate and grow and to formulate continuum models that account for the variability of the damage process due to microstructural heterogeneity. The length scale of damage with respect to that of the surrounding microstructure has proven to be a key aspect in determining sites of failure initiation. Correlations have been found between the damage sites and the surrounding microstructure to determine the preferred sites of spall damage, since it tends to localize at and around the regions of intrinsic defects such as grain boundaries and triple points. However, considerable amount of work still has to be done in this regard to determine the physics driving the damage at these intrinsic weak sites in the microstructure. The main focus of this research work is to understand the physical mechanisms behind the damage localization at these preferred sites. A crystal plasticity constitutive model is implemented with different damage criteria to study the effects of stress concentration and strain localization at the grain boundaries. A cohesive zone modeling technique is used to include the intrinsic strength of the grain boundaries in the simulations. The constitutive model is verified using single elements tests, calibrated using single crystal impact experiments and validated using bicrystal and multicrystal impact experiments. The results indicate that strain localization is the predominant driving force for damage initiation and evolution. The microstructural effects on theses damage sites are studied to attribute the extent of damage to microstructural features such as grain orientation, misorientation, Taylor factor and the grain boundary planes. The finite element simulations show good correlation with the experimental results and can be used as the preliminary step in developing accurate probabilistic models for damage nucleation.
ContributorsKrishnan, Kapil (Author) / Peralta, Pedro (Thesis advisor) / Mignolet, Marc (Committee member) / Sieradzki, Karl (Committee member) / Jiang, Hanqing (Committee member) / Oswald, Jay (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
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
This research examines several critical aspects of the so-called "film induced cleavage" model of stress corrosion cracking using silver-gold alloys as the parent-phase material. The model hypothesizes that the corrosion generates a brittle nanoporous film, which subsequently fractures forming a high-speed crack that is injected into the uncorroded parent-phase alloy. This high speed crack owing to its kinetic energy can penetrate beyond the corroded layer into the parent phase and thus effectively reducing strength of the parent phase. Silver-gold alloys provide an ideal system to study this effect, as hydrogen effect can be ruled out on thermodynamic basis. During corrosion of the silver-gold alloy, the less noble metal i.e. silver is removed from the system leaving behind a nanoporous gold (NPG) layer. In the case of polycrystalline material, this corrosion process proceeds deeper along the grain boundary than the matrix grain. All of the cracks with apparent penetration beyond the corroded (dealloyed) layer are intergranular. Our aim was to study the crack penetration depth along the grain boundary to ascertain whether the penetration occurs past the grain-boundary dealloyed depth. EDS and imaging in high-resolution aberration corrected scanning transmission electron microscope (STEM) and atom probe tomography (APT) have been used to evaluate the grain boundary corrosion depth.
The mechanical properties of monolithic NPG are also studied. The motivation behind this is two-fold. The crack injection depth depends on the speed of the crack formed in the nanoporous layer, which in turn depends on the mechanical properties of the NPG. Also NPG has potential applications in actuation, sensing and catalysis. The measured value of the Young's modulus of NPG with 40 nm ligament size and 28% density was ~ 2.5 GPa and the Poisson's ratio was ~ 0.20. The fracture stress was observed to be ~ 11-13 MPa. There was no significant change observed between these mechanical properties on oxidation of NPG at 1.4 V. The fracture toughness value for the NPG was ~ 10 J/m2. Also dynamic fracture tests showed that the NPG is capable of supporting crack velocities ~ 100 - 180 m/s.
The mechanical properties of monolithic NPG are also studied. The motivation behind this is two-fold. The crack injection depth depends on the speed of the crack formed in the nanoporous layer, which in turn depends on the mechanical properties of the NPG. Also NPG has potential applications in actuation, sensing and catalysis. The measured value of the Young's modulus of NPG with 40 nm ligament size and 28% density was ~ 2.5 GPa and the Poisson's ratio was ~ 0.20. The fracture stress was observed to be ~ 11-13 MPa. There was no significant change observed between these mechanical properties on oxidation of NPG at 1.4 V. The fracture toughness value for the NPG was ~ 10 J/m2. Also dynamic fracture tests showed that the NPG is capable of supporting crack velocities ~ 100 - 180 m/s.
ContributorsBadwe, Nilesh (Author) / Sieradzki, Karl (Thesis advisor) / Peralta, Pedro (Committee member) / Oswald, Jay (Committee member) / Mahajan, Ravi (Committee member) / Arizona State University (Publisher)
Created2014
Description
Commercially pure (CP) and extra low interstitial (ELI) grade Ti-alloys present excellent corrosion resistance, lightweight, and formability making them attractive materials for expanded use in transportation and medical applications. However, the strength and toughness of CP titanium are affected by relatively small variations in their impurity/solute content (IC), e.g., O, Al, and V. This increase in strength is due to the fact that the solute either increases the critical stress required for the prismatic slip systems ({10-10}<1-210>) or activates another slip system ((0001)<11-20>, {10-11}<11-20>). In particular, solute additions such as O can effectively strengthen the alloy but with an attendant loss in ductility by changing the behavior from wavy (cross slip) to planar nature. In order to understand the underlying behavior of strengthening by solutes, it is important to understand the atomic scale mechanism. This dissertation aims to address this knowledge gap through a synergistic combination of density functional theory (DFT) and molecular dynamics. Further, due to the long-range strain fields of the dislocations and the periodicity of the DFT simulation cells, it is difficult to apply ab initio simulations to study the dislocation core structure. To alleviate this issue we developed a multiscale quantum mechanics/molecular mechanics approach (QM/MM) to study the dislocation core. We use the developed QM/MM method to study the pipe diffusion along a prismatic edge dislocation core. Complementary to the atomistic simulations, the Semi-discrete Variational Peierls-Nabarro model (SVPN) was also used to analyze the dislocation core structure and mobility. The chemical interaction between the solute/impurity and the dislocation core is captured by the so-called generalized stacking fault energy (GSFE) surface which was determined from DFT-VASP calculations. By taking the chemical interaction into consideration the SVPN model can predict the dislocation core structure and mobility in the presence and absence of the solute/impurity and thus reveal the effect of impurity/solute on the softening/hardening behavior in alpha-Ti. Finally, to study the interaction of the dislocation core with other planar defects such as grain boundaries (GB), we develop an automated method to theoretically generate GBs in HCP type materials.
ContributorsBhatia, Mehul Anoopkumar (Author) / Solanki, Kiran N (Thesis advisor) / Peralta, Pedro (Committee member) / Jiang, Hanqing (Committee member) / Neithalath, Narayanan (Committee member) / Rajagopalan, Jagannathan (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
Description
A method of determining nanoparticle temperature through fluorescence intensity levels is described. Intracellular processes are often tracked through the use of fluorescence tagging, and ideal temperatures for many of these processes are unknown. Through the use of fluorescence-based thermometry, cellular processes such as intracellular enzyme movement can be studied and their respective temperatures established simultaneously. Polystyrene and silica nanoparticles are synthesized with a variety of temperature-sensitive dyes such as BODIPY, rose Bengal, Rhodamine dyes 6G, 700, and 800, and Nile Blue A and Nile Red. Photographs are taken with a QImaging QM1 Questar EXi Retiga camera while particles are heated from 25 to 70 C and excited at 532 nm with a Coherent DPSS-532 laser. Photographs are converted to intensity images in MATLAB and analyzed for fluorescence intensity, and plots are generated in MATLAB to describe each dye's intensity vs temperature. Regression curves are created to describe change in fluorescence intensity over temperature. Dyes are compared as nanoparticle core material is varied. Large particles are also created to match the camera's optical resolution capabilities, and it is established that intensity values increase proportionally with nanoparticle size. Nile Red yielded the closest-fit model, with R2 values greater than 0.99 for a second-order polynomial fit. By contrast, Rhodamine 6G only yielded an R2 value of 0.88 for a third-order polynomial fit, making it the least reliable dye for temperature measurements using the polynomial model. Of particular interest in this work is Nile Blue A, whose fluorescence-temperature curve yielded a much different shape from the other dyes. It is recommended that future work describe a broader range of dyes and nanoparticle sizes, and use multiple excitation wavelengths to better quantify each dye's quantum efficiency. Further research into the effects of nanoparticle size on fluorescence intensity levels should be considered as the particles used here greatly exceed 2 ìm. In addition, Nile Blue A should be further investigated as to why its fluorescence-temperature curve did not take on a characteristic shape for a temperature-sensitive dye in these experiments.
ContributorsTomforde, Christine (Author) / Phelan, Patrick (Thesis advisor) / Dai, Lenore (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
Description
Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal processing, pattern recognition, data mining, high fidelity probabilistic progressive damage models, physics based damage models, and regression analysis. Due to the wide application of carbon fiber reinforced composites and their multiscale failure mechanisms, it is necessary to emphasize the research of SHM on composite structures. This research develops a comprehensive framework for the damage detection, localization, quantification, and prediction of the remaining useful life of complex composite structures. To interrogate a composite structure, guided wave propagation is applied to thin structures such as beams and plates. Piezoelectric transducers are selected because of their versatility, which allows them to be used as sensors and actuators. Feature extraction from guided wave signals is critical to demonstrate the presence of damage and estimate the damage locations. Advanced signal processing techniques are employed to extract robust features and information. To provide a better estimate of the damage for accurate life estimation, probabilistic regression analysis is used to obtain a prediction model for the prognosis of complex structures subject to fatigue loading. Special efforts have been applied to the extension of SHM techniques on aerospace and spacecraft structures, such as UAV composite wings and deployable composite boom structures. Necessary modifications of the developed SHM techniques were conducted to meet the unique requirements of the aerospace structures. The developed SHM algorithms are able to accurately detect and quantify impact damages as well as matrix cracking introduced.
ContributorsLiu, Yingtao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Rajadas, John (Committee member) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012
Description
Damage assessment and residual useful life estimation (RULE) are essential for aerospace, civil and naval structures. Structural Health Monitoring (SHM) attempts to automate the process of damage detection and identification. Multiscale modeling is a key element in SHM. It not only provides important information on the physics of failure, such as damage initiation and growth, the output can be used as "virtual sensing" data for detection and prognosis. The current research is part of an ongoing multidisciplinary effort to develop an integrated SHM framework for metallic aerospace components. In this thesis a multiscale model has been developed by bridging the relevant length scales, micro, meso and macro (or structural scale). Micro structural representations obtained from material characterization studies are used to define the length scales and to capture the size and orientation of the grains at the micro level. Parametric studies are conducted to estimate material parameters used in this constitutive model. Numerical and experimental simulations are performed to investigate the effects of Representative Volume Element (RVE) size, defect area fraction and distribution. A multiscale damage criterion accounting for crystal orientation effect is developed. This criterion is applied for fatigue crack initial stage prediction. A damage evolution rule based on strain energy density is modified to incorporate crystal plasticity at the microscale (local). Optimization approaches are used to calculate global damage index which is used for the RVE failure prediciton. Potential cracking directions are provided from the damage criterion simultaneously. A wave propagation model is incorporated with the damage model to detect changes in sensing signals due to plastic deformation and damage growth.
ContributorsLuo, Chuntao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Dai, Lenore (Committee member) / Li, Jian (Committee member) / Arizona State University (Publisher)
Created2011