Matching Items (2)
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
This is a two-part thesis.
Part 1 of this thesis investigates the influence of spatial temperature distribution on the accuracy of performance data of photovoltaic (PV) modules in outdoor conditions and provides physical approaches to improve the spatial temperature distribution of the test modules so an accurate performance data can be obtained in the field. Conventionally, during outdoor performance testing, a single thermocouple location is used on the backsheet or back glass of a test module. This study clearly indicates that there is a large spatial temperature difference between various thermocouple locations within a module. Two physical approaches or configurations were experimented to improve the spatial temperature uniformity: thermally insulating the inner and outer surface of the frame; backsheet and inner surface of the frame. All the data were compared with un-insulated conventional configuration. This study was performed in an array setup of six modules under two different preconditioning electrical configurations, Voc and MPPT over several clear sunny days. This investigation concludes that the best temperature uniformity and the most accurate I-V data can be obtained only by thermally insulating the inner and outer frame surfaces or by using the average of four thermocouple temperatures, as specified in IEC 61853-2, without any thermal insulation.
Part 2 of this thesis analyzes the field data obtained from old PV power plants using various statistical techniques to identify the most influential degradation modes on fielded PV modules in two different climates: hot-dry (Arizona); cold-dry (New York). Performance data and visual inspection data of 647 modules fielded in five different power plants were analyzed. Statistical tests including hypothesis testing were carried out to identify the I-V parameter(s) that are affected the most. The affected performance parameters (Isc, Voc, FF and Pmax) were then correlated with the defects to determine the most dominant defect affecting power degradation. Analysis indicates that the cell interconnect discoloration (or solder bond deterioration) is the dominant defect in hot-dry climate leading to series resistance increase and power loss, while encapsulant delamination is being the most dominant defect in cold-dry climate leading to cell mismatch and power loss.
Part 1 of this thesis investigates the influence of spatial temperature distribution on the accuracy of performance data of photovoltaic (PV) modules in outdoor conditions and provides physical approaches to improve the spatial temperature distribution of the test modules so an accurate performance data can be obtained in the field. Conventionally, during outdoor performance testing, a single thermocouple location is used on the backsheet or back glass of a test module. This study clearly indicates that there is a large spatial temperature difference between various thermocouple locations within a module. Two physical approaches or configurations were experimented to improve the spatial temperature uniformity: thermally insulating the inner and outer surface of the frame; backsheet and inner surface of the frame. All the data were compared with un-insulated conventional configuration. This study was performed in an array setup of six modules under two different preconditioning electrical configurations, Voc and MPPT over several clear sunny days. This investigation concludes that the best temperature uniformity and the most accurate I-V data can be obtained only by thermally insulating the inner and outer frame surfaces or by using the average of four thermocouple temperatures, as specified in IEC 61853-2, without any thermal insulation.
Part 2 of this thesis analyzes the field data obtained from old PV power plants using various statistical techniques to identify the most influential degradation modes on fielded PV modules in two different climates: hot-dry (Arizona); cold-dry (New York). Performance data and visual inspection data of 647 modules fielded in five different power plants were analyzed. Statistical tests including hypothesis testing were carried out to identify the I-V parameter(s) that are affected the most. The affected performance parameters (Isc, Voc, FF and Pmax) were then correlated with the defects to determine the most dominant defect affecting power degradation. Analysis indicates that the cell interconnect discoloration (or solder bond deterioration) is the dominant defect in hot-dry climate leading to series resistance increase and power loss, while encapsulant delamination is being the most dominant defect in cold-dry climate leading to cell mismatch and power loss.
ContributorsUmachandran, Neelesh (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Wang, Liping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2015