Matching Items (3)
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
The objective of this research is to develop robust, accurate, and adaptive algorithms in the framework of the extended finite element method (XFEM) for fracture analysis of highly heterogeneous materials with complex internal geometries. A key contribution of this work is the creation of novel methods designed to automate the incorporation of high-resolution data, e.g. from X-ray tomography, that can be used to better interpret the enormous volume of data generated in modern in-situ experimental testing. Thus new algorithms were developed for automating analysis of complex microstructures characterized by segmented tomographic images.
A centrality-based geometry segmentation algorithm was developed to accurately identify discrete inclusions and particles in composite materials where limitations in imaging resolution leads to spurious connections between particles in close contact.To allow for this algorithm to successfully segment geometry independently of particle size and shape, a relative centrality metric was defined to allow for a threshold centrality criterion for removal of voxels that spuriously connect distinct geometries.
To automate incorporation of microstructural information from high-resolution images, two methods were developed that initialize signed distance fields on adaptively-refined finite element meshes. The first method utilizes a level set evolution equation that is directly solved on the finite element mesh through Galerkins method. The evolution equation is formulated to produce a signed distance field that matches geometry defined by a set of voxels segmented from tomographic images. The method achieves optimal convergence for the order of elements used. In a second approach, the fast marching method is employed to initialize a distance field on a uniform grid which is then projected by least squares onto a finite element mesh. This latter approach is shown to be superior in speed and accuracy.
Lastly, extended finite element method simulations are performed for the analysis of particle fracture in metal matrix composites with realistic particle geometries initialized from X-ray tomographic data. In the simulations, particles fracture probabilistically through a Weibull strength distribution. The model is verified through comparisons with the experimentally-measured stress-strain response of the material as well as analysis of the fracture. Further, simulations are then performed to analyze the effect of mesh sensitivity, the effect of fracture of particles on their neighbors, and the role of a particles shape on its fracture probability.
A centrality-based geometry segmentation algorithm was developed to accurately identify discrete inclusions and particles in composite materials where limitations in imaging resolution leads to spurious connections between particles in close contact.To allow for this algorithm to successfully segment geometry independently of particle size and shape, a relative centrality metric was defined to allow for a threshold centrality criterion for removal of voxels that spuriously connect distinct geometries.
To automate incorporation of microstructural information from high-resolution images, two methods were developed that initialize signed distance fields on adaptively-refined finite element meshes. The first method utilizes a level set evolution equation that is directly solved on the finite element mesh through Galerkins method. The evolution equation is formulated to produce a signed distance field that matches geometry defined by a set of voxels segmented from tomographic images. The method achieves optimal convergence for the order of elements used. In a second approach, the fast marching method is employed to initialize a distance field on a uniform grid which is then projected by least squares onto a finite element mesh. This latter approach is shown to be superior in speed and accuracy.
Lastly, extended finite element method simulations are performed for the analysis of particle fracture in metal matrix composites with realistic particle geometries initialized from X-ray tomographic data. In the simulations, particles fracture probabilistically through a Weibull strength distribution. The model is verified through comparisons with the experimentally-measured stress-strain response of the material as well as analysis of the fracture. Further, simulations are then performed to analyze the effect of mesh sensitivity, the effect of fracture of particles on their neighbors, and the role of a particles shape on its fracture probability.
ContributorsYuan, Rui (Author) / Oswald, Jay (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Liu, Yongming (Committee member) / Solanki, Kiran (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2015
Description
An evolving understanding of elastomeric polymer nanocomposites continues to expand commercial, defense, and industrial products and applications. This work explores the thermomechanical properties of elastomeric nanocomposites prepared from bisphenol A diglycidyl ether (BADGE) and three amine-terminated poly(propylene oxides) (Jeffamines). The Jeffamines investigated include difunctional crosslinkers with molecular weights of 2,000 and 4,000 g/mol and a trifunctional crosslinker with a molecular weight of 3,000 g/mol. Additionally, carbon nanotubes (CNTs) were added, up to 1.25 wt%, to each thermoset. The findings indicate that the Tg and storage modulus of the polymer nanocomposites can be controlled independently within narrow concentration windows, and that effects observed following CNT incorporation are dependent on the crosslinker molecular weight.
Polymer matrix composites (PMCs) offer design solutions to produce smart sensing, conductive, or high performance composites for a number of critical applications. Nanoparticle additives, in particular, carbon nanotubes and metallic quantum dots, have been investigated for their ability to improve the conductivity, thermal stability, and mechanical strength of traditional composites. Herein we report the use of quantum dots (QDs) and fluorescently labeled carbon nanotubes (CNTs) to modify the thermomechanical properties of PMCs. Additionally, we find that pronounced changes in fluorescence emerge following plastic deformation, indicating that in these polymeric materials the transduction of mechanical force into the fluorescence occurs in response to mechanical activation.
Segmented ionenes are a class of thermoplastic elastomers that contain a permanent charged group within the polymer backbone and a spacer segment with a low glass transition temperature (Tg) to provide flexibility. Ionenes are of interest because of their synthetic versatility, unique morphologies, and ionic nature. Using phase changing ionene-based nanocomposites could be extended to create reversible mechanically, electrically, optically, and/or thermally responsive materials depending on constituent nanoparticles and polymers. This talk will discuss recent efforts to utilize the synthetic versatility of ionenes (e.g., spacer composition of PTMO or PEG) to prepare percolated ionic domains in microphase separated polymers that display a range of thermomechanical properties. Furthermore, by synthesizing two series of ionene copolymers with either PEG or PTMO spacers at various ratios with 1,12-dibromododecane will yield a range of ion contents (hard contents) and will impact nanoparticle dispersion.
Polymer matrix composites (PMCs) offer design solutions to produce smart sensing, conductive, or high performance composites for a number of critical applications. Nanoparticle additives, in particular, carbon nanotubes and metallic quantum dots, have been investigated for their ability to improve the conductivity, thermal stability, and mechanical strength of traditional composites. Herein we report the use of quantum dots (QDs) and fluorescently labeled carbon nanotubes (CNTs) to modify the thermomechanical properties of PMCs. Additionally, we find that pronounced changes in fluorescence emerge following plastic deformation, indicating that in these polymeric materials the transduction of mechanical force into the fluorescence occurs in response to mechanical activation.
Segmented ionenes are a class of thermoplastic elastomers that contain a permanent charged group within the polymer backbone and a spacer segment with a low glass transition temperature (Tg) to provide flexibility. Ionenes are of interest because of their synthetic versatility, unique morphologies, and ionic nature. Using phase changing ionene-based nanocomposites could be extended to create reversible mechanically, electrically, optically, and/or thermally responsive materials depending on constituent nanoparticles and polymers. This talk will discuss recent efforts to utilize the synthetic versatility of ionenes (e.g., spacer composition of PTMO or PEG) to prepare percolated ionic domains in microphase separated polymers that display a range of thermomechanical properties. Furthermore, by synthesizing two series of ionene copolymers with either PEG or PTMO spacers at various ratios with 1,12-dibromododecane will yield a range of ion contents (hard contents) and will impact nanoparticle dispersion.
ContributorsWang, Meng, Ph.D (Author) / Green, Matthew D (Thesis advisor) / Green, Alexander (Committee member) / Yarger, Jeffery (Committee member) / Arizona State University (Publisher)
Created2019