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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
Plasmon resonance in nanoscale metallic structures has shown its ability to concentrate electromagnetic energy into sub-wavelength volumes. Metal nanostructures exhibit a high extinction coefficient in the visible and near infrared spectrum due to their large absorption and scattering cross sections corresponding to their surface plasmon resonance. Hence, they can serve as an attractive candidate for solar energy conversion. Recent papers have showed that dielectric core/metallic shell nanoparticles yielded a plasmon resonance wavelength tunable from visible to infrared by changing the ratio of core radius to the total radius. Therefore it is interesting to develop a dispersion of core-shell multifunctional nanoparticles capable of dynamically changing their volume ratio and thus their spectral radiative properties. Nanoparticle suspensions (nanofluids) are known to offer a variety of benefits for thermal transport and energy conversion. Nanofluids have been proven to increase the efficiency of the photo-thermal energy conversion process in direct solar absorption collectors (DAC). Combining these two cutting-edge technologies enables the use of core-shell nanoparticles to control the spectral and radiative properties of plasmonic nanofluids in order to efficiently harvest and convert solar energy. Plasmonic nanofluids that have strong energy concentrating capacity and spectral selectivity can be used in many high-temperature energy systems where radiative heat transport is essential. In this thesis，the surface plasmon resonance effect and the wavelength tuning ranges for different metallic shell nanoparticles are investigated, the solar-weighted efficiencies of corresponding core-shell nanoparticle suspensions are explored, and a quantitative study of core-shell nanoparticle suspensions in a DAC system is provided. Using core-shell nanoparticle dispersions, it is possible to create efficient spectral solar absorption fluids and design materials for applications which require variable spectral absorption or scattering.
ContributorsLv, Wei (Author) / Phelan, Patrick E (Thesis advisor) / Dai, Lenore (Committee member) / Prasher, Ravi (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the maturity of advanced composites as feasible structural materials for various applications there is a critical need to solve the challenge of designing these material systems for optimal performance. However, determining superior design methods requires a deep understanding of the material-structure properties at various length scales. Due to the length-scale dependent behavior of advanced composites, multiscale modeling techniques may be used to describe the dominant mechanisms of damage and failure in these material systems. With polymer matrix fiber composites and nanocomposites it becomes essential to include even the atomic length scale, where the resin-hardener-nanofiller molecules interact, in the multiscale modeling framework. Additionally, sources of variability are also critical to be included in these models due to the important role of uncertainty in advance composite behavior. Such a methodology should be able to describe length scale dependent mechanisms in a computationally efficient manner for the analysis of practical composite structures.
In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.
In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.
ContributorsRai, Ashwin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Rajadas, John (Committee member) / Fard, Masoud Yekani (Committee member) / Arizona State University (Publisher)
Created2018
Description
Hydrogel polymers have been the subject of many studies, due to their fascinating ability to alternate between being hydrophilic and hydrophobic, upon the application of appropriate stimuli. In particular, thermo-responsive hydrogels such as N-Isopropylacrylamide (NIPAM), which possess a unique lower critical solution temperature (LCST) of 32°C, have been leveraged for membrane-based processes such as using NIPAM as a draw agent for forward osmosis (FO) desalination. The low LCST temperature of NIPAM ensures that fresh water can be recovered, at a modest energy cost as compared to other thermally based desalination processes which require water recovery at higher temperatures. This work studies by experimentation, key process parameters involved in desalination by FO using NIPAM and a copolymer of NIPAM and Sodium Acrylate (NIPAM-SA). It encompasses synthesis of the hydrogels, development of experiments to effectively characterize synthesized products, and the measuring of FO performance for the individual hydrogels. FO performance was measured using single layers of NIPAM and NIPAM-SA respectively. The values of permeation flux obtained were compared to relevant published literature and it was found to be within reasonable range. Furthermore, a conceptual design for future large-scale implementation of this technology is proposed. It is proposed that perhaps more effort should focus on physical processes that have the ability to increase the low permeation flux of hydrogel driven FO desalination systems, rather than development of novel classes of hydrogels
ContributorsAbdullahi, Adnan None (Author) / Phelan, Patrick (Thesis advisor) / Wang, Robert (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2019
Description
Among the alternative processes for the traditional distillation, adsorption and membrane separations are the two most promising candidates and metal-organic frameworks (MOFs) are the new material candidate as adsorbent or membrane due to their high surface area, various pore sizes, and highly tunable framework functionality. This dissertation presents an investigation of the formation process of MOF membrane, framework defects, and two-dimensional (2D) MOFs, aiming to explore the answers for three critical questions: (1) how to obtain a continuous MOF membrane, (2) how defects form in MOF framework, and (3) how to obtain isolated 2D MOFs. To solve the first problem, the accumulated protons in the MOF synthesis solution is proposed to be the key factor preventing the continuous growth among Universitetet I Oslo-(UiO)-66 crystals. The hypothesis is verified by the growth reactivation under the addition of deprotonating agent. As long as the protons were sufficiently coordinated by the deprotonating agent, the continuous growth of UiO-66 is guaranteed. Moreover, the modulation effect can impact the coordination equilibrium so that an oriented growth of UiO-66 film was achieved in membrane structures. To find the answer for the second problem, the defect formation mechanism in UiO-66 was investigated and the formation of missing-cluster (MC) defects is attributed to the partially-deprotonated ligands. Experimental results show the number of MC defects is sensitive to the addition of deprotonating agent, synthesis temperature, and reactant concentration. Pore size distribution allows an accurate and convenient characterization of the defects. Results show that these defects can cause significant deviations of its pore size distribution from the perfect crystal. The study of the third questions is based on the established bi-phase synthesis method, a facile synthesis method is adopted for the production of high quality 2D MOFs in large scale. Here, pyridine is used as capping reagent to prevent the interplanar hydrogen bond formation. Meanwhile, formic acid and triethylamine as modulator and deprotonating agent to balance the anisotropic growth, crystallinity, and yield in the 2D MOF synthesis. As a result, high quality 2D zinc-terephthalic acid (ZnBDC) and copper-terephthalic acid (CuBDC) with extraordinary aspect ratio samples were successfully synthesized.
ContributorsShan, Bohan (Author) / Mu, Bin (Thesis advisor) / Forzani, Erica (Committee member) / Dai, Lenore (Committee member) / Lin, Jerry (Committee member) / Liu, Jingyue (Committee member) / Arizona State University (Publisher)
Created2019
Description
A new type of electronics was envisioned, namely edible electronics. Edible electronics are made by Food and Drug Administration (FDA) certified edible materials which can be eaten and digested by human body. Different from implantable electronics, test or treatment using edible electronics doesn’t require operations and perioperative complications.
This dissertation bridges the food industry, material sciences, device fabrication, and biomedical engineering by demonstrating edible supercapacitors and electronic components and devices such as pH sensor.
Edible supercapacitors were fabricated using food materials from grocery store. 5 of them were connected in series to power a snake camera. Tests result showed that the current generated by supercapacitor have the ability to kill bacteria. Next more food, processed food and non-toxic level electronic materials were investigated. A “preferred food kit” was created for component fabrication based on the investigation. Some edible electronic components, such as wires, resistor, inductor, etc., were developed and characterized utilizing the preferred food kit. These components make it possible to fabricate edible electronic/device in the future work. Some edible electronic components were integrated into an edible electronic system/device. Then edible pH sensor was introduced and fabricated. This edible pH sensor can be swallowed and test pH of gastric fluid. PH can be read in a phone within seconds after the pH sensor was swallowed. As a side project, an edible double network gel electrolyte was synthesized for the edible supercapacitor.
This dissertation bridges the food industry, material sciences, device fabrication, and biomedical engineering by demonstrating edible supercapacitors and electronic components and devices such as pH sensor.
Edible supercapacitors were fabricated using food materials from grocery store. 5 of them were connected in series to power a snake camera. Tests result showed that the current generated by supercapacitor have the ability to kill bacteria. Next more food, processed food and non-toxic level electronic materials were investigated. A “preferred food kit” was created for component fabrication based on the investigation. Some edible electronic components, such as wires, resistor, inductor, etc., were developed and characterized utilizing the preferred food kit. These components make it possible to fabricate edible electronic/device in the future work. Some edible electronic components were integrated into an edible electronic system/device. Then edible pH sensor was introduced and fabricated. This edible pH sensor can be swallowed and test pH of gastric fluid. PH can be read in a phone within seconds after the pH sensor was swallowed. As a side project, an edible double network gel electrolyte was synthesized for the edible supercapacitor.
ContributorsXu, Wenwen (Author) / Jiang, Hanqing (Thesis advisor) / Dai, Lenore (Committee member) / Green, Matthew (Committee member) / Mu, Bin (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Solid oxide fuel cells have become a promising candidate in the development of high-density clean energy sources for the rapidly increasing demands in energy and global sustainability. In order to understand more about solid oxide fuel cells, the important step is to understand how to model heterogeneous materials. Heterogeneous materials are abundant in nature and also created in various processes. The diverse properties exhibited by these materials result from their complex microstructures, which also make it hard to model the material. Microstructure modeling and reconstruction on a meso-scale level is needed in order to produce heterogeneous models without having to shave and image every slice of the physical material, which is a destructive and irreversible process. Yeong and Torquato [1] introduced a stochastic optimization technique that enables the generation of a model of the material with the use of correlation functions. Spatial correlation functions of each of the various phases within the heterogeneous structure are collected from a two-dimensional micrograph representing a slice of a solid oxide fuel cell through computational means. The assumption is that two-dimensional images contain key structural information representative of the associated full three-dimensional microstructure. The collected spatial correlation functions, a combination of one-point and two-point correlation functions are then outputted and are representative of the material. In the reconstruction process, the characteristic two-point correlation functions is then inputted through a series of computational modeling codes and software to generate a three-dimensional visual model that is statistically similar to that of the original two-dimensional micrograph. Furthermore, parameters of temperature cooling stages and number of pixel exchanges per temperature stage are utilized and altered accordingly to observe which parameters has a higher impact on the reconstruction results. Stochastic optimization techniques to produce three-dimensional visual models from two-dimensional micrographs are therefore a statistically reliable method to understanding heterogeneous materials.
ContributorsPhan, Richard Dylan (Author) / Jiao, Yang (Thesis director) / Ren, Yi (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The recovery of biofuels permits renewable alternatives to present day fossil fuels that cause devastating effects on the planet. Pervaporation is a separation process that shows promise for the separation of ethanol from biologically fermentation broths. The performance of thin film composite membranes of polydimethylsiloxane (PDMS) and zeolite imidazolate frameworks (ZIF-71) dip coated onto a porous substrate are analyzed. Pervaporation performance factors of flux, separation factor and selectivity are measured for varying ZIF-71 loadings of pure PDMS, 5 wt%, 12.5 wt% and 25 wt% at 60 oC with a 2 wt% ethanol/water feed. The increase in ZIF-71 loadings increased the performance of PDMS to produce higher flux, higher separation factor and high selectivity than pure polymeric films.
ContributorsLau, Ching Yan (Author) / Lind, Mary Laura (Thesis director) / Durgun, Pinar Cay (Committee member) / Lively, Ryan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Chemical Engineering Program (Contributor)
Created2014-05
Description
Currently, approximately 40% of the world’s electricity is generated from coal and coal power plants are one of the major sources of greenhouse gases accounting for a third of all CO2 emissions. The Integrated Gasification Combined Cycle (IGCC) has been shown to provide an increase in plant efficiency compared to traditional coal-based power generation processes resulting in a reduction of greenhouse gas emissions. The goal of this project was to analyze the performance of a new SNDC ceramic-carbonate dual-phase membrane for CO2 separation. The chemical formula for the SNDC-carbonate membrane was Sm0.075Nd0.075Ce0.85O1.925. This project also focused on the use of this membrane for pre-combustion CO2 capture coupled with a water gas shift (WGS) reaction for a 1000 MW power plant. The addition of this membrane to the traditional IGCC process provides a purer H2 stream for combustion in the gas turbine and results in lower operating costs and increased efficiencies for the plant. At 900 °C the CO2 flux and permeance of the SNDC-carbonate membrane were 0.65 mL/cm2•min and 1.0×10-7 mol/m2•s•Pa, respectively. Detailed in this report are the following: background regarding CO2 separation membranes and IGCC power plants, SNDC tubular membrane preparation and characterization, IGCC with membrane reactor plant design, process heat and mass balance, and plant cost estimations.
ContributorsDunteman, Nicholas Powell (Author) / Lin, Jerry (Thesis director) / Dong, Xueliang (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor) / School of Sustainability (Contributor)
Created2014-05
Description
The two central goals of this project were 1) to develop a testing method utilizing coatings on ultra-thin stainless steel to measure the thermal conductivity (k) of battery electrode materials and composites, and 2) to measure and compare the thermal conductivities of lithium iron phosphate (LiFePO4, "LFP") in industry-standard graphite/LFP mixtures as well as graphene/LFP mixtures and a synthesized graphene/LFP nanocomposite. Graphene synthesis was attempted before purchasing graphene materials, and further exploration of graphene synthesis is recommended due to limitations in purchased product quality. While it was determined after extensive experimentation that the graphene/LFP nanocomposite could not be successfully synthesized according to current literature information, a mixed composite of graphene/LFP was successfully tested and found to have k = 0.23 W/m*K. This result provides a starting point for further thermal testing method development and k optimization in Li-ion battery electrode nanocomposites.
ContributorsStehlik, Daniel Wesley (Author) / Chan, Candace K. (Thesis director) / Dai, Lenore (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2014-05