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- All Subjects: Molecular Dynamics
- All Subjects: Structural Health Monitoring
- Creators: Dai, Lenore
- Resource Type: Text
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
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
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
Description
Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic for application in metallic and composite material systems. The primary means of interrogating a structure in this work is through the use of Lamb wave propagation which works well for the thin structures used in aerospace applications. Piezoelectric transducers (PZTs) were selected for this application since they can be used as both sensors and actuators of guided waves. Placement of these transducers is an important issue in wave based approaches as Lamb waves are sensitive to changes in material properties, geometry, and boundary conditions which may obscure the presence of damage if they are not taken into account during sensor placement. The placement scheme proposed in this dissertation arranges piezoelectric transducers in a pitch-catch mode so the entire structure can be covered using a minimum number of sensors. The stress distribution of the structure is also considered so PZTs are placed in regions where they do not fail before the host structure. In order to process the data from these transducers, advanced signal processing techniques are employed to detect the presence of damage in complex structures. To provide a better estimate of the damage for accurate life estimation, machine learning techniques are used to classify the type of damage in the structure. A data structure analysis approach is used to reduce the amount of data collected and increase computational efficiency. In the case of low velocity impact damage, fiber Bragg grating (FBG) sensors were used with a nonlinear regression tool to reconstruct the loading at the impact site.
ContributorsCoelho, Clyde (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Wu, Tong (Committee member) / Das, Santanu (Committee member) / Rajadas, John (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Metal-organic frameworks (MOFs) are a new set of porous materials comprised of metals or metal clusters bonded together in a coordination system by organic linkers. They are becoming popular for gas separations due to their abilities to be tailored toward specific applications. Zirconium MOFs in particular are known for their high stability under standard temperature and pressure due to the strength of the Zirconium-Oxygen coordination bond. However, the acid modulator needed to ensure long range order of the product also prevents complete linker deprotonation. This leads to a powder product that cannot easily be incorporated into continuous MOF membranes. This study therefore implemented a new bi-phase synthesis technique with a deprotonating agent to achieve intergrowth in UiO-66 membranes. Crystal intergrowth will allow for effective gas separations and future permeation testing. During experimentation, successful intergrown UiO-66 membranes were synthesized and characterized. The degree of intergrowth and crystal orientations varied with changing deprotonating agent concentration, modulator concentration, and ligand:modulator ratios. Further studies will focus on achieving the same results on porous substrates.
ContributorsClose, Emily Charlotte (Author) / Mu, Bin (Thesis director) / Shan, Bohan (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
A hybrid molecular dynamics (MD) simulation framework is developed to emulate mechanochemical reaction of mechanophores in epoxy-based nanocomposites. Two different force fields, a classical force field and a bond order based force field are hybridized to mimic the experimental processes from specimen preparation to mechanical loading test. Ultra-violet photodimerization for mechanophore synthesis and epoxy curing for thermoset polymer generation are successfully simulated by developing a numerical covalent bond generation method using the classical force field within the framework. Mechanical loading tests to activate mechanophores are also virtually conducted by deforming the volume of a simulation unit cell. The unit cell deformation leads to covalent bond elongation and subsequent bond breakage, which is captured using the bond order based force field. The outcome of the virtual loading test is used for local work analysis, which enables a quantitative study of mechanophore activation. Through the local work analysis, the onset and evolution of mechanophore activation indicating damage initiation and propagation are estimated; ultimately, the mechanophore sensitivity to external stress is evaluated. The virtual loading tests also provide accurate estimations of mechanical properties such as elastic, shear, bulk modulus, yield strain/strength, and Poisson’s ratio of the system. Experimental studies are performed in conjunction with the simulation work to validate the hybrid MD simulation framework. Less than 2% error in estimations of glass transition temperature (Tg) is observed with experimentally measured Tgs by use of differential scanning calorimetry. Virtual loading tests successfully reproduce the stress-strain curve capturing the effect of mechanophore inclusion on mechanical properties of epoxy polymer; comparable changes in Young’s modulus and yield strength are observed in experiments and simulations. Early damage signal detection, which is identified in experiments by observing increased intensity before the yield strain, is captured in simulations by showing that the critical strain representing the onset of the mechanophore activation occurs before the estimated yield strain. It is anticipated that the experimentally validated hybrid MD framework presented in this dissertation will provide a low-cost alternative to additional experiments that are required for optimizing material design parameters to improve damage sensing capability and mechanical properties.
In addition to the study of mechanochemical reaction analysis, an atomistic model of interphase in carbon fiber reinforced composites is developed. Physical entanglement between semi-crystalline carbon fiber surface and polymer matrix is captured by introducing voids in multiple graphene layers, which allow polymer matrix to intertwine with graphene layers. The hybrid MD framework is used with some modifications to estimate interphase properties that include the effect of the physical entanglement. The results are compared with existing carbon fiber surface models that assume that carbon fiber has a crystalline structure and hence are unable to capture the physical entanglement. Results indicate that the current model shows larger stress gradients across the material interphase. These large stress gradients increase the viscoplasticity and damage effects at the interphase. The results are important for improved prediction of the nonlinear response and damage evolution in composite materials.
In addition to the study of mechanochemical reaction analysis, an atomistic model of interphase in carbon fiber reinforced composites is developed. Physical entanglement between semi-crystalline carbon fiber surface and polymer matrix is captured by introducing voids in multiple graphene layers, which allow polymer matrix to intertwine with graphene layers. The hybrid MD framework is used with some modifications to estimate interphase properties that include the effect of the physical entanglement. The results are compared with existing carbon fiber surface models that assume that carbon fiber has a crystalline structure and hence are unable to capture the physical entanglement. Results indicate that the current model shows larger stress gradients across the material interphase. These large stress gradients increase the viscoplasticity and damage effects at the interphase. The results are important for improved prediction of the nonlinear response and damage evolution in composite materials.
ContributorsKoo, Bonsung (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Jiao, Yang (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
Amine-modified solid sorbents and membrane separation are promising technologies for separation and capture of carbon dioxide (CO2) from combustion flue gas. Amine absorption processes are mature, but still have room for improvement. This work focused on the synthesis of amine-modified aerogels and metal-organic framework-5 (MOF-5) membranes for CO2 separation. A series of solid sorbents were synthesized by functionalizing amines on the surface of silica aerogels. This was done by three coating methods: physical adsorption, magnetically assisted impact coating (MAIC) and atomic layer deposition (ALD). CO2 adsorption capacity of the sorbents was measured at room temperature in a Cahn microbalance. The sorbents synthesized by physical adsorption show the largest CO2 adsorption capacity (1.43-1.63 mmol CO2/g). An additional sorbent synthesized by ALD on hydrophilic aerogels at atmospheric pressures shows an adsorption capacity of 1.23 mmol CO2/g. Studies on one amine-modified sorbent show that the powder is of agglomerate bubbling fluidization (ABF) type. The powder is difficult to fluidize and has limited bed expansion. The ultimate goal is to configure the amine-modified sorbents in a micro-jet assisted gas fluidized bed to conduct adsorption studies. MOF-5 membranes were synthesized on α-alumina supports by two methods: in situ synthesis and secondary growth synthesis. Characterization by scanning electron microscope (SEM) imaging and X-ray diffraction (XRD) show that the membranes prepared by both methods have a thickness of 14-16 μm, and a MOF-5 crystal size of 15-25 μm with no apparent orientation. Single gas permeation results indicate that the gas transport through both membranes is determined by a combination of Knudsen diffusion and viscous flow. The contribution of viscous flow indicates that the membranes have defects.
ContributorsRosa, Teresa M (Author) / Lin, Jerry (Thesis advisor) / Pfeffer, Robert (Thesis advisor) / Dai, Lenore (Committee member) / Nielsen, David (Committee member) / Arizona State University (Publisher)
Created2010
Description
In order to better understand the physical properties of polyethylene, an extremely common plastic used mostly in packaging, many scientists and engineers use olecular dynamics. To reduce the computational expense associated with traditional atomistic molecular dynamics, coarse-grained molecular dynamics is often used. Coarse-grained molecular dynamics groups multiple atoms into single beads, reducing the number of degrees of freedom in a system and eliminating smaller atoms with faster kinematics. However, even coarse-grained methods have their limitations, one of which is timestep duration, which is limited by the maximum vibrational frequency in the coarse-grained system. To study this limitation, a coarse-grained model of polyethylene was created such that every C 2 H 4 unit was replaced with a bead. Coarse-grained potentials for bond-stretching, bond-bending, and non-bonded interaction were generated using the iterative Boltzmann inversion method, which matches coarse-grained distribution functions to atomistic distribution functions. After the creation of the model, the coarse-grained potentials were rescaled by a constant so that they were less stiff, decreasing the maximum vibrational frequency of the system. It is found that by diminishing the bond-stretching potential to 6.25% of its original value, the maximum stable timestep can be increased 85% over that of the unmodified potential functions. The results of this work suggest that it may be possible to simulate lengthy processes, such as the crystallization of polyethylene, in less time with adjusted coarse-grained potentials. Additionally, the large discrepancies in the speed of bond-stretching, bond-bending, and non- bonded interaction dynamics suggest that a multi-timestep method may be worth investigating in future work.
ContributorsWiles, Christian Scott (Author) / Oswald, Jay (Thesis director) / Dai, Lenore (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.
ContributorsSchichtel, Jacob (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Ghoshal, Anindya (Committee member) / Huang, Huei-Ping (Committee member) / Jiao, Yang (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2022