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Description
Identification of early damage in polymer composite materials is of significant importance so that preventative measures can be taken before the materials reach catastrophic failure. Scientists have been developing damage detection technologies over many years and recently, mechanophore-based polymers, in which mechanical energy is translated to activate a chemical transformation, have received increasing attention. More specifically, the damage can be made detectable by mechanochromic polymers, which provide a visible color change upon the scission of covalent bonds under stress. This dissertation focuses on the study of a novel self-sensing framework for identifying early and in-situ damage by employing unique stress-sensing mechanophores. Two types of mechanophores, cyclobutane and cyclooctane, were utilized, and the former formed from cinnamoyl moeities and the latter formed from anthracene upon photodimerization. The effects on the thermal and mechanical properties with the addition of the cyclobutane-based polymers into epoxy matrices were investigated. The emergence of cracks was detected by fluorescent signals at a strain level right after the yield point of the polymer blends, and the fluorescence intensified with the accumulation of strain. Similar to the mechanism of fluorescence emission from the cleavage of cyclobutane, the cyclooctane moiety generated fluorescent emission with a higher quantum yield upon cleavage. The experimental results also demonstrated the success of employing the cyclooctane type mechanophore as a potential force sensor, as the fluorescence intensification was correlated with the strain increase.
ContributorsZou, Jin (Author) / Dai, Lenore L (Thesis advisor) / Chattopadhyay, Aditi (Thesis advisor) / Lind, Mary L (Committee member) / Mu, Bin (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2014
Description
This investigation focuses on the development of uncertainty modeling methods applicable to both the structural and thermal models of heated structures as part of an effort to enable the design under uncertainty of hypersonic vehicles. The maximum entropy-based nonparametric stochastic modeling approach is used within the context of coupled structural-thermal Reduced Order Models (ROMs). Not only does this strategy allow for a computationally efficient generation of samples of the structural and thermal responses but the maximum entropy approach allows to introduce both aleatoric and some epistemic uncertainty into the system.
While the nonparametric approach has a long history of applications to structural models, the present investigation was the first one to consider it for the heat conduction problem. In this process, it was recognized that the nonparametric approach had to be modified to maintain the localization of the temperature near the heat source, which was successfully achieved.
The introduction of uncertainty in coupled structural-thermal ROMs of heated structures was addressed next. It was first recognized that the structural stiffness coefficients (linear, quadratic, and cubic) and the parameters quantifying the effects of the temperature distribution on the structural response can be regrouped into a matrix that is symmetric and positive definite. The nonparametric approach was then applied to this matrix allowing the assessment of the effects of uncertainty on the resulting temperature distributions and structural response.
The third part of this document focuses on introducing uncertainty using the Maximum Entropy Method at the level of finite element by randomizing elemental matrices, for instance, elemental stiffness, mass and conductance matrices. This approach brings some epistemic uncertainty not present in the parametric approach (e.g., by randomizing the elasticity tensor) while retaining more local character than the operation in ROM level.
The last part of this document focuses on the development of “reduced ROMs” (RROMs) which are reduced order models with small bases constructed in a data-driven process from a “full” ROM with a much larger basis. The development of the RROM methodology is motivated by the desire to optimally reduce the computational cost especially in multi-physics situations where a lack of prior understanding/knowledge of the solution typically leads to the selection of ROM bases that are excessively broad to ensure the necessary accuracy in representing the response. It is additionally emphasized that the ROM reduction process can be carried out adaptively, i.e., differently over different ranges of loading conditions.
While the nonparametric approach has a long history of applications to structural models, the present investigation was the first one to consider it for the heat conduction problem. In this process, it was recognized that the nonparametric approach had to be modified to maintain the localization of the temperature near the heat source, which was successfully achieved.
The introduction of uncertainty in coupled structural-thermal ROMs of heated structures was addressed next. It was first recognized that the structural stiffness coefficients (linear, quadratic, and cubic) and the parameters quantifying the effects of the temperature distribution on the structural response can be regrouped into a matrix that is symmetric and positive definite. The nonparametric approach was then applied to this matrix allowing the assessment of the effects of uncertainty on the resulting temperature distributions and structural response.
The third part of this document focuses on introducing uncertainty using the Maximum Entropy Method at the level of finite element by randomizing elemental matrices, for instance, elemental stiffness, mass and conductance matrices. This approach brings some epistemic uncertainty not present in the parametric approach (e.g., by randomizing the elasticity tensor) while retaining more local character than the operation in ROM level.
The last part of this document focuses on the development of “reduced ROMs” (RROMs) which are reduced order models with small bases constructed in a data-driven process from a “full” ROM with a much larger basis. The development of the RROM methodology is motivated by the desire to optimally reduce the computational cost especially in multi-physics situations where a lack of prior understanding/knowledge of the solution typically leads to the selection of ROM bases that are excessively broad to ensure the necessary accuracy in representing the response. It is additionally emphasized that the ROM reduction process can be carried out adaptively, i.e., differently over different ranges of loading conditions.
ContributorsSong, Pengchao (Author) / Mignolet, Marc P (Thesis advisor) / Smarslok, Benjamin (Committee member) / Chattopadhyay, Aditi (Committee member) / Liu, Yongming (Committee member) / Jiang, Hanqing (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2019
Description
The problem of catastrophic damage purveys in any material application, and minimizing its occurrence is paramount for general health and safety. Thus, novel damage detection schemes are required that can sense the precursors to damage. Mechanochemistry is the area of research that involves the use of mechanical force to induce a chemical change, with recent study focusing on directing the mechanical force to embedded mechanophore units for a targeted chemical response. Mechanophores are molecular units that provide a measureable signal in response to an applied force, often in the form of a visible color change or fluorescent emission, and their application to thermoset network polymers has been limited. Following preliminary work on polymer blends of cyclobutane-based mechanophores and epoxy, dimeric 9-anthracene carboxylic acid (Di-AC)-based mechanophore particles were synthesized and employed to form stress sensitive particle reinforced epoxy matrix composites.
Under an applied stress, the cyclooctane-rings in the Di-AC particles revert back to their fluorescent anthracene form, which linearly enhances the overall fluorescence of the composite in response to the applied strain. The fluorescent signal further allows for stress sensing in the elastic region of the stress-strain curve, which is considered to be a form of damage precursor detection. This behavior was further analyzed at the molecular scale with corresponding molecular dynamics simulations. Following the successful application of Di-AC to an epoxy matrix, the mechanophore particles were incorporated into a polyurethane matrix to show the universal nature of Di-AC as a stress-sensitive particle filler. Interestingly, in polyurethane Di-AC could successfully detect damage with less applied strain compared to the epoxy system.
While mechanophores of varying chemistries have been covalently incorporated into elastomeric and thermoplastic polymer systems, they have not yet been covalently incorporated a thermoset network polymer. Thus, following the study of mechanophore particles as stress-sensitive fillers, two routes of grafting mechanophore units into an epoxy system to form a self-sensing nanocomposite were explored. These involved the mechanophore precursor and mechanophore, cinnamamide and di-cinnamamide, respectively. With both molecules, the free amine groups can directly bond to epoxy resin to covalently incorporate themselves within the thermoset network to form a self-sensing nanocomposite.
Under an applied stress, the cyclooctane-rings in the Di-AC particles revert back to their fluorescent anthracene form, which linearly enhances the overall fluorescence of the composite in response to the applied strain. The fluorescent signal further allows for stress sensing in the elastic region of the stress-strain curve, which is considered to be a form of damage precursor detection. This behavior was further analyzed at the molecular scale with corresponding molecular dynamics simulations. Following the successful application of Di-AC to an epoxy matrix, the mechanophore particles were incorporated into a polyurethane matrix to show the universal nature of Di-AC as a stress-sensitive particle filler. Interestingly, in polyurethane Di-AC could successfully detect damage with less applied strain compared to the epoxy system.
While mechanophores of varying chemistries have been covalently incorporated into elastomeric and thermoplastic polymer systems, they have not yet been covalently incorporated a thermoset network polymer. Thus, following the study of mechanophore particles as stress-sensitive fillers, two routes of grafting mechanophore units into an epoxy system to form a self-sensing nanocomposite were explored. These involved the mechanophore precursor and mechanophore, cinnamamide and di-cinnamamide, respectively. With both molecules, the free amine groups can directly bond to epoxy resin to covalently incorporate themselves within the thermoset network to form a self-sensing nanocomposite.
ContributorsNofen, Elizabeth (Author) / Dai, Lenore L (Thesis advisor) / Chattopadhyay, Aditi (Thesis advisor) / Emady, Heather (Committee member) / Mu, Bin (Committee member) / Nielsen, David (Committee member) / Arizona State University (Publisher)
Created2016
Description
While understanding of failure mechanisms for polymeric composites have improved vastly over recent decades, the ability to successfully monitor early failure and subsequent prevention has come of much interest in recent years. One such method to detect these failures involves the use of mechanochemistry, a field of chemistry in which chemical reactions are initiated by deforming highly-strained bonds present in certain moieties. Mechanochemistry is utilized in polymeric composites as a means of stress-sensing, utilizing weak and force-responsive chemical bonds to activate signals when embedded in a composite material. These signals can then be detected to determine the amount of stress applied to a composite and subsequent potential damage that has occurred due to the stress. Among mechanophores, the cinnamoyl moiety is capable of stress response through fluorescent signal under mechanical load. The cinnamoyl group is fluorescent in its initial state and capable of undergoing photocycloaddition in the presence of ultraviolet (UV) light, followed by subsequent reversion when under mechanical load. Signal generation before the yield point of the material provides a form of damage precursor detection.This dissertation explores the implementation of mechanophores in novel approaches to overcome some of the many challenges within the mechanochemistry field. First, new methods of mechanophore detection were developed through utilization of Fourier transform infrared (FTIR) spectroscopy signals and in-situ stress sensing. Developing an in-situ testing method provided a two-fold advantage of higher resolution and more time efficiency over current methods involving image analysis with a fluorescent microscope. Second, bonding mechanophores covalently into the backbone of an epoxy matrix mitigated property loss due to mechanophore incorporation. This approach was accomplished through functionalizing either the resin or hardener component of the matrix. Finally, surface functionalization of fibers was performed and allowed for unaltered fabrication procedures of composite layups as well as provided increased adhesion at the fiber-matrix interphase. The developed materials could enable a simple, non-invasive, and non-detrimental structural health monitoring approach.
ContributorsGunckel, Ryan Patrick (Author) / Dai, Lenore (Thesis advisor) / Chattopadhyay, Aditi (Thesis advisor) / Lind Thomas, Mary Laura (Committee member) / Liu, Yongming (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2021