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- All Subjects: Mechanical Engineering
- Creators: Jiang, Hanqing
- Member of: Theses and Dissertations
Description
Pb-free solders are used as interconnects in various levels of micro-electronic packaging. Reliability of these interconnects is very critical for the performance of the package. One of the main factors affecting the reliability of solder joints is the presence of porosity which is introduced during processing of the joints. In this thesis, the effect of such porosity on the deformation behavior and eventual failure of the joints is studied using Finite Element (FE) modeling technique. A 3D model obtained by reconstruction of x-ray tomographic image data is used as input for FE analysis to simulate shear deformation and eventual failure of the joint using ductile damage model. The modeling was done in ABAQUS (v 6.10). The FE model predictions are validated with experimental results by comparing the deformation of the pores and the crack path as predicted by the model with the experimentally observed deformation and failure pattern. To understand the influence of size, shape, and distribution of pores on the mechanical behavior of the joint four different solder joints with varying degrees of porosity are modeled using the validated FE model. The validation technique mentioned above enables comparison of the simulated and actual deformation only. A more robust way of validating the FE model would be to compare the strain distribution in the joint as predicted by the model and as observed experimentally. In this study, to enable visualization of the experimental strain for the 3D microstructure obtained from tomography, a three dimensional digital image correlation (3D DIC) code has been implemented in MATLAB (MathWorks Inc). This developed 3D DIC code can be used as another tool to verify the numerical model predictions. The capability of the developed code in measuring local displacement and strain is demonstrated by considering a test case.
ContributorsJakkali, Vaidehi (Author) / Chawla, Nikhilesh K (Thesis advisor) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2011
Description
Gels are three-dimensional polymer networks with entrapped solvent (water etc.). They bear amazing features such as stimuli-responsive (temperature, PH, electric field etc.), high water content and biocompatibility and thus find a lot of applications. To understand the complex physics behind gel's swelling phenomenon, it is important to build up fundamental mechanical model and extend to complicated cases. In this dissertation, a coupled large deformation and diffusion model regarding gel's swelling behavior is presented. In this model, free-energy of the total gel is constituted by polymer stretching energy and polymer-solvent mixing energy. In-house nonlinear finite element code is implemented with fast computational capability. Complex phenomenon such as buckling and healing of cracked gel by swelling are studied. Due to the wide coverage of polymeric materials and solvents, solvent diffusion in gels not only follows Fickian diffusion law where concentration map is continuous but also follows non-Fickian diffusion law where concentration map shows high gradient. Phenomenological model with viscoelastic polymer constitutive and concentration dependent diffusivity is created. The model well captures this special diffusion phenomenon such as sharp diffusion front and distinctive swollen and unswollen region.
ContributorsZhang, Jiaping (Author) / Jiang, Hanqing (Thesis advisor) / Peralta, Pedro (Committee member) / Dai, Lenore (Committee member) / Rajan, Subramaniam D. (Committee member) / Chawla, Nikhilesh (Committee member) / Arizona State University (Publisher)
Created2012
Description
Hydrogen embrittlement (HE) is a phenomenon that affects both the physical and chemical properties of several intrinsically ductile metals. Consequently, understanding the mechanisms behind HE has been of particular interest in both experimental and modeling research. Discrepancies between experimental observations and modeling results have led to various proposals for HE mechanisms. Therefore, to gain insights into HE mechanisms in iron, this dissertation aims to investigate several key issues involving HE such as: a) the incipient crack tip events; b) the cohesive strength of grain boundaries (GBs); c) the dislocation-GB interactions and d) the dislocation mobility.
The crack tip, which presents a preferential trap site for hydrogen segregation, was examined using atomistic methods and the continuum based Rice-Thompson criterion as sufficient concentration of hydrogen can alter the crack tip deformation mechanism. Results suggest that there is a plausible co-existence of the adsorption induced dislocation emission and hydrogen enhanced decohesion mechanisms. In the case of GB-hydrogen interaction, we observed that the segregation of hydrogen along the interface leads to a reduction in cohesive strength resulting in intergranular failure. A methodology was further developed to quantify the role of the GB structure on this behavior.
GBs play a fundamental role in determining the strengthening mechanisms acting as an impediment to the dislocation motion; however, the presence of an unsurmountable barrier for a dislocation can generate slip localization that could further lead to intergranular crack initiation. It was found that the presence of hydrogen increases the strain energy stored within the GB which could lead to a transition in failure mode. Finally, in the case of body centered cubic metals, understanding the complex screw dislocation motion is critical to the development of an accurate continuum description of the plastic behavior. Further, the presence of hydrogen has been shown to drastically alter the plastic deformation, but the precise role of hydrogen is still unclear. Thus, the role of hydrogen on the dislocation mobility was examined using density functional theory and atomistic simulations. Overall, this dissertation provides a novel atomic-scale understanding of the HE mechanism and development of multiscale tools for future endeavors.
The crack tip, which presents a preferential trap site for hydrogen segregation, was examined using atomistic methods and the continuum based Rice-Thompson criterion as sufficient concentration of hydrogen can alter the crack tip deformation mechanism. Results suggest that there is a plausible co-existence of the adsorption induced dislocation emission and hydrogen enhanced decohesion mechanisms. In the case of GB-hydrogen interaction, we observed that the segregation of hydrogen along the interface leads to a reduction in cohesive strength resulting in intergranular failure. A methodology was further developed to quantify the role of the GB structure on this behavior.
GBs play a fundamental role in determining the strengthening mechanisms acting as an impediment to the dislocation motion; however, the presence of an unsurmountable barrier for a dislocation can generate slip localization that could further lead to intergranular crack initiation. It was found that the presence of hydrogen increases the strain energy stored within the GB which could lead to a transition in failure mode. Finally, in the case of body centered cubic metals, understanding the complex screw dislocation motion is critical to the development of an accurate continuum description of the plastic behavior. Further, the presence of hydrogen has been shown to drastically alter the plastic deformation, but the precise role of hydrogen is still unclear. Thus, the role of hydrogen on the dislocation mobility was examined using density functional theory and atomistic simulations. Overall, this dissertation provides a novel atomic-scale understanding of the HE mechanism and development of multiscale tools for future endeavors.
ContributorsAdlakha, Ilaksh (Author) / Solanki, Kiran (Thesis advisor) / Mignolet, Marc (Committee member) / Chawla, Nikhilesh (Committee member) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2015
Description
The interaction between humans and robots has become an important area of research as the diversity of robotic applications has grown. The cooperation of a human and robot to achieve a goal is an important area within the physical human-robot interaction (pHRI) field. The expansion of this field is toward moving robotics into applications in unstructured environments. When humans cooperate with each other, often there are leader and follower roles. These roles may change during the task. This creates a need for the robotic system to be able to exchange roles with the human during a cooperative task. The unstructured nature of the new applications in the field creates a need for robotic systems to be able to interact in six degrees of freedom (DOF). Moreover, in these unstructured environments, the robotic system will have incomplete information. This means that it will sometimes perform an incorrect action and control methods need to be able to correct for this. However, the most compelling applications for robotics are where they have capabilities that the human does not, which also creates the need for robotic systems to be able to correct human action when it detects an error. Activity in the brain precedes human action. Utilizing this activity in the brain can classify the type of interaction desired by the human. For this dissertation, the cooperation between humans and robots is improved in two main areas. First, the ability for electroencephalogram (EEG) to determine the desired cooperation role with a human is demonstrated with a correct classification rate of 65%. Second, a robotic controller is developed to allow the human and robot to cooperate in six DOF with asymmetric role exchange. This system allowed human-robot cooperation to perform a cooperative task at 100% correct rate. High, medium, and low levels of robotic automation are shown to affect performance, with the human making the greatest numbers of errors when the robotic system has a medium level of automation.
ContributorsWhitsell, Bryan Douglas (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Polygerinos, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2017
Description
This dissertation will investigate two of the most promising high-capacity anode
materials for lithium-based batteries: silicon (Si) and metal lithium (Li). It will focus on
studying the mechanical behaviors of the two materials during charge and discharge and
understanding how these mechanical behaviors may affect their electrochemical
performance.
In the first part, amorphous Si anode will be studied. Despite many existing studies
on silicon (Si) anodes for lithium ion batteries (LIBs), many essential questions still exist
on compound formation, composition, and properties. Here it is shown that some
previously accepted findings do not truthfully reflect the actual lithiation mechanisms in
realistic battery configurations. Furthermore the correlation between structure and
mechanical properties in these materials has not been properly established. Here, a rigorous
and thorough study is performed to comprehensively understand the electrochemical
reaction mechanisms of amorphous-Si (a-Si) in a realistic LIB configuration. In-depth
microstructural characterization was performed and correlations were established between
Li-Si composition, volumetric expansion, and modulus/hardness. It is found that the
lithiation process of a-Si in a real battery setup is a single-phase reaction rather than the
accepted two-phase reaction obtained from in-situ TEM experiments. The findings in this
dissertation establish a reference to quantitatively explain many key metrics for lithiated a
Si as anodes in real LIBs, and can be used to rationally design a-Si based high-performance
LIBs guided by high-fidelity modeling and simulations.
In the second part, Li metal anode will be investigated. Problems related to dendrite
growth on lithium metal anodes such as capacity loss and short circuit present major
barriers to the next-generation high-energy-density batteries. The development of
successful mitigation strategies is impeded by the incomplete understanding of the Li
dendrite growth mechanisms. Here the enabling role of plating residual stress in dendrite
initiation through novel experiments of Li electrodeposition on soft substrates is confirmed,
and the observations is explained with a stress-driven dendrite growth model. Dendrite
growth is mitigated on such soft substrates through surface-wrinkling-induced stress
relaxation in deposited Li film. It is demonstrated that this new dendrite mitigation
mechanism can be utilized synergistically with other existing approaches in the form of
three-dimensional (3D) soft scaffolds for Li plating, which achieves superior coulombic
efficiency over conventional hard copper current collectors under large current density.
materials for lithium-based batteries: silicon (Si) and metal lithium (Li). It will focus on
studying the mechanical behaviors of the two materials during charge and discharge and
understanding how these mechanical behaviors may affect their electrochemical
performance.
In the first part, amorphous Si anode will be studied. Despite many existing studies
on silicon (Si) anodes for lithium ion batteries (LIBs), many essential questions still exist
on compound formation, composition, and properties. Here it is shown that some
previously accepted findings do not truthfully reflect the actual lithiation mechanisms in
realistic battery configurations. Furthermore the correlation between structure and
mechanical properties in these materials has not been properly established. Here, a rigorous
and thorough study is performed to comprehensively understand the electrochemical
reaction mechanisms of amorphous-Si (a-Si) in a realistic LIB configuration. In-depth
microstructural characterization was performed and correlations were established between
Li-Si composition, volumetric expansion, and modulus/hardness. It is found that the
lithiation process of a-Si in a real battery setup is a single-phase reaction rather than the
accepted two-phase reaction obtained from in-situ TEM experiments. The findings in this
dissertation establish a reference to quantitatively explain many key metrics for lithiated a
Si as anodes in real LIBs, and can be used to rationally design a-Si based high-performance
LIBs guided by high-fidelity modeling and simulations.
In the second part, Li metal anode will be investigated. Problems related to dendrite
growth on lithium metal anodes such as capacity loss and short circuit present major
barriers to the next-generation high-energy-density batteries. The development of
successful mitigation strategies is impeded by the incomplete understanding of the Li
dendrite growth mechanisms. Here the enabling role of plating residual stress in dendrite
initiation through novel experiments of Li electrodeposition on soft substrates is confirmed,
and the observations is explained with a stress-driven dendrite growth model. Dendrite
growth is mitigated on such soft substrates through surface-wrinkling-induced stress
relaxation in deposited Li film. It is demonstrated that this new dendrite mitigation
mechanism can be utilized synergistically with other existing approaches in the form of
three-dimensional (3D) soft scaffolds for Li plating, which achieves superior coulombic
efficiency over conventional hard copper current collectors under large current density.
ContributorsWang, Xu (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongbin (Thesis advisor) / Chan, Candace (Committee member) / Wang, Liping (Committee member) / Qiong, Nian (Committee member) / Arizona State University (Publisher)
Created2018
Description
Energy harvesting from ambient is important to configuring Wireless Sensor Networks (WSN) for environmental data collecting. In this work, highly flexible thermoelectric generators (TEGs) have been studied and fabricated to supply power to the wireless sensor notes used for data collecting in hot spring environment. The fabricated flexible TEGs can be easily deployed on the uneven surface of heated rocks at the rim of hot springs. By employing the temperature gradient between the hot rock surface and the air, these TEGs can generate power to extend the battery lifetime of the sensor notes and therefore reduce multiple batteries changes where the environment is usually harsh in hot springs. Also, they show great promise for self-powered wireless sensor notes. Traditional thermoelectric material bismuth telluride (Bi2Te3) and advanced MEMS (Microelectromechanical systems) thin film techniques were used for the fabrication. Test results show that when a flexible TEG array with an area of 3.4cm2 was placed on the hot plate surface of 80°C in the air under room temperature, it had an open circuit voltage output of 17.6mV and a short circuit current output of 0.53mA. The generated power was approximately 7mW/m2.
On the other hand, high pressure, temperatures that can reach boiling, and the pH of different hot springs ranging from <2 to >9 make hot spring ecosystem a unique environment that is difficult to study. WSN allows many scientific studies in harsh environments that are not feasible with traditional instrumentation. However, wireless pH sensing for long time in situ data collection is still challenging for two reasons. First, the existing commercial-off-the-shelf pH meters are frequent calibration dependent; second, biofouling causes significant measurement error and drift. In this work, 2-dimentional graphene pH sensors were studied and calibration free graphene pH sensor prototypes were fabricated. Test result shows the resistance of the fabricated device changes linearly with the pH values (in the range of 3-11) in the surrounding liquid environment. Field tests show graphene layer greatly prevented the microbial fouling. Therefore, graphene pH sensors are promising candidates that can be effectively used for wireless pH sensing in exploration of hot spring ecosystems.
On the other hand, high pressure, temperatures that can reach boiling, and the pH of different hot springs ranging from <2 to >9 make hot spring ecosystem a unique environment that is difficult to study. WSN allows many scientific studies in harsh environments that are not feasible with traditional instrumentation. However, wireless pH sensing for long time in situ data collection is still challenging for two reasons. First, the existing commercial-off-the-shelf pH meters are frequent calibration dependent; second, biofouling causes significant measurement error and drift. In this work, 2-dimentional graphene pH sensors were studied and calibration free graphene pH sensor prototypes were fabricated. Test result shows the resistance of the fabricated device changes linearly with the pH values (in the range of 3-11) in the surrounding liquid environment. Field tests show graphene layer greatly prevented the microbial fouling. Therefore, graphene pH sensors are promising candidates that can be effectively used for wireless pH sensing in exploration of hot spring ecosystems.
ContributorsHan, Ruirui (Author) / Yu, Hongyu (Thesis advisor) / Jiang, Hanqing (Committee member) / Yu, Hongbin (Committee member) / Garnero, Edward (Committee member) / Li, Mingming (Committee member) / Arizona State University (Publisher)
Created2018
Description
Multiaxial mechanical fatigue of heterogeneous materials has been a significant cause of concern in the aerospace, civil and automobile industries for decades, limiting the service life of structural components while increasing time and costs associated with inspection and maintenance. Fiber reinforced composites and light-weight aluminum alloys are widely used in aerospace structures that require high specific strength and fatigue resistance. However, studying the fundamental crack growth behavior at the micro- and macroscale as a function of loading history is essential to accurately predict the residual fatigue life of components and achieve damage tolerant designs. The issue of mechanical fatigue can be tackled by developing reliable in-situ damage quantification methodologies and by comprehensively understanding fatigue damage mechanisms under a variety of complex loading conditions. Although a multitude of uniaxial fatigue loading studies have been conducted on light-weight metallic materials and composites, many service failures occur from components being subjected to variable amplitude, mixed-mode multiaxial fatigue loadings. In this research, a systematic approach is undertaken to address the issue of fatigue damage evolution in aerospace materials by:
(i) Comprehensive investigation of micro- and macroscale crack growth behavior in aerospace grade Al 7075 T651 alloy under complex biaxial fatigue loading conditions. The effects of variable amplitude biaxial loading on crack growth characteristics such as crack acceleration and retardation were studied in detail by exclusively analyzing the influence of individual mode-I, mixed-mode and mode-II overload and underload fatigue cycles in an otherwise constant amplitude mode-I baseline load spectrum. The micromechanisms governing crack growth behavior under the complex biaxial loading conditions were identified and correlated with the crack growth behavior and fracture surface morphology through quantitative fractography.
(ii) Development of novel multifunctional nanocomposite materials with improved fatigue resistance and in-situ fatigue damage detection and quantification capabilities. A state-of-the-art processing method was developed for producing sizable carbon nanotube (CNT) membranes for multifunctional composites. The CNT membranes were embedded in glass fiber laminates and in-situ strain sensing and damage quantification was achieved by exploiting the piezoresistive property of the CNT membrane. In addition, improved resistance to fatigue crack growth was observed due to the embedded CNT membrane.
(i) Comprehensive investigation of micro- and macroscale crack growth behavior in aerospace grade Al 7075 T651 alloy under complex biaxial fatigue loading conditions. The effects of variable amplitude biaxial loading on crack growth characteristics such as crack acceleration and retardation were studied in detail by exclusively analyzing the influence of individual mode-I, mixed-mode and mode-II overload and underload fatigue cycles in an otherwise constant amplitude mode-I baseline load spectrum. The micromechanisms governing crack growth behavior under the complex biaxial loading conditions were identified and correlated with the crack growth behavior and fracture surface morphology through quantitative fractography.
(ii) Development of novel multifunctional nanocomposite materials with improved fatigue resistance and in-situ fatigue damage detection and quantification capabilities. A state-of-the-art processing method was developed for producing sizable carbon nanotube (CNT) membranes for multifunctional composites. The CNT membranes were embedded in glass fiber laminates and in-situ strain sensing and damage quantification was achieved by exploiting the piezoresistive property of the CNT membrane. In addition, improved resistance to fatigue crack growth was observed due to the embedded CNT membrane.
ContributorsDatta, Siddhant (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Jiang, Hanqing (Committee member) / Marvi, Hamidreza (Committee member) / Tang, Pingbo (Committee member) / Yekani Fard, Masoud (Committee member) / Iyyer, Nagaraja (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this research, a new cutting edge wear estimator for micro-endmilling is developed and the reliabillity of the estimator is evaluated. The main concept of this estimator is the minimum chip thickness effect. This estimator predicts the cutting edge radius by detecting the drop in the chip production rate as the cutting edge of a micro- endmill slips over the workpiece when the minimum chip thickness becomes larger than the uncut chip thickness, thus transitioning from the shearing to the ploughing dominant regime. The chip production rate is investigated through simulation and experiment. The simulation and the experiment show that the chip production rate decreases when the minimum chip thickness becomes larger than the uncut chip thickness. Also, the reliability of this estimator is evaluated. The probability of correct estimation of the cutting edge radius is more than 80%. This cutting edge wear estimator could be applied to an online tool wear estimation system. Then, a large number of cutting edge wear data could be obtained. From the data, a cutting edge wear model could be developed in terms of the machine control parameters so that the optimum control parameters could be applied to increase the tool life and the machining quality as well by minimizing the cutting edge wear rate.
In addition, in order to find the stable condition of the machining, the stabillity lobe of the system is created by measuring the dynamic parameters. This process is needed prior to the cutting edge wear estimation since the chatter would affect the cutting edge wear and the chip production rate. In this research, a new experimental set-up for measuring the dynamic parameters is developed by using a high speed camera with microscope lens and a loadcell. The loadcell is used to measure the stiffness of the tool-holder assembly of the machine and the high speed camera is used to measure the natural frequency and the damping ratio. From the measured data, a stability lobe is created. Even though this new method needs further research, it could be more cost-effective than the conventional methods in the future.
In addition, in order to find the stable condition of the machining, the stabillity lobe of the system is created by measuring the dynamic parameters. This process is needed prior to the cutting edge wear estimation since the chatter would affect the cutting edge wear and the chip production rate. In this research, a new experimental set-up for measuring the dynamic parameters is developed by using a high speed camera with microscope lens and a loadcell. The loadcell is used to measure the stiffness of the tool-holder assembly of the machine and the high speed camera is used to measure the natural frequency and the damping ratio. From the measured data, a stability lobe is created. Even though this new method needs further research, it could be more cost-effective than the conventional methods in the future.
ContributorsLee, Jue-Hyun (Author) / SODEMANN, ANGELA A (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Hsu, Keng (Committee member) / Artemiadis, Panagiotis (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation presents the development of structural health monitoring and prognostic health management methodologies for complex structures and systems in the field of mechanical engineering. To overcome various challenges historically associated with complex structures and systems such as complicated sensing mechanisms, noisy information, and large-size datasets, a hybrid monitoring framework comprising of solid mechanics concepts and data mining technologies is developed. In such a framework, the solid mechanics simulations provide additional intuitions to data mining techniques reducing the dependence of accuracy on the training set, while the data mining approaches fuse and interpret information from the targeted system enabling the capability for real-time monitoring with efficient computation.
In the case of structural health monitoring, ultrasonic guided waves are utilized for damage identification and localization in complex composite structures. Signal processing and data mining techniques are integrated into the damage localization framework, and the converted wave modes, which are induced by the thickness variation due to the presence of delamination, are used as damage indicators. This framework has been validated through experiments and has shown sufficient accuracy in locating delamination in X-COR sandwich composites without the need of baseline information. Besides the localization of internal damage, the Gaussian process machine learning technique is integrated with finite element method as an online-offline prediction model to predict crack propagation with overloads under biaxial loading conditions; such a probabilistic prognosis model, with limited number of training examples, has shown increased accuracy over state-of-the-art techniques in predicting crack retardation behaviors induced by overloads. In the case of system level management, a monitoring framework built using a multivariate Gaussian model as basis is developed to evaluate the anomalous condition of commercial aircrafts. This method has been validated using commercial airline data and has shown high sensitivity to variations in aircraft dynamics and pilot operations. Moreover, this framework was also tested on simulated aircraft faults and its feasibility for real-time monitoring was demonstrated with sufficient computation efficiency.
This research is expected to serve as a practical addition to the existing literature while possessing the potential to be adopted in realistic engineering applications.
In the case of structural health monitoring, ultrasonic guided waves are utilized for damage identification and localization in complex composite structures. Signal processing and data mining techniques are integrated into the damage localization framework, and the converted wave modes, which are induced by the thickness variation due to the presence of delamination, are used as damage indicators. This framework has been validated through experiments and has shown sufficient accuracy in locating delamination in X-COR sandwich composites without the need of baseline information. Besides the localization of internal damage, the Gaussian process machine learning technique is integrated with finite element method as an online-offline prediction model to predict crack propagation with overloads under biaxial loading conditions; such a probabilistic prognosis model, with limited number of training examples, has shown increased accuracy over state-of-the-art techniques in predicting crack retardation behaviors induced by overloads. In the case of system level management, a monitoring framework built using a multivariate Gaussian model as basis is developed to evaluate the anomalous condition of commercial aircrafts. This method has been validated using commercial airline data and has shown high sensitivity to variations in aircraft dynamics and pilot operations. Moreover, this framework was also tested on simulated aircraft faults and its feasibility for real-time monitoring was demonstrated with sufficient computation efficiency.
This research is expected to serve as a practical addition to the existing literature while possessing the potential to be adopted in realistic engineering applications.
ContributorsLi, Guoyi (Ph.D.) (Author) / Chattopadhyay, Aditi (Thesis advisor) / Mignolet, Marc (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Yekani Fard, Masoud (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2019
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