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Description
V(D)J recombination is responsible for generating an enormous repertoire of immunoglobulins and T cell receptors, therefore it is a centerpiece to the formation of the adaptive immune system. The V(D)J recombination process proceeds through two steps, site-specific cleavage at RSS (Recombination Signal Sequence) site mediated by the RAG recombinase (RAG1/2) and the subsequent imprecise resolution of the DNA ends, which is carried out by the ubiquitous non-homologous end joining pathway (NHEJ). The V(D)J recombination reaction is obliged to be tightly controlled under all circumstances, as it involves generations of DNA double strand breaks, which are considered the most dangerous lesion to a cell. Multifaceted regulatory mechanisms have been evolved to create great diversity of the antigen receptor repertoire while ensuring genome stability. The RAG-mediated cleavage reaction is stringently regulated at both the pre-cleavage stage and the post-cleavage stage. Specifically, RAG1/2 first forms a pre-cleavage complex assembled at the boarder of RSS and coding flank, which ensures the appropriate DNA targeting. Subsequently, this complex initiates site-specific cleavage, generating two types of double stranded DNA breaks, hairpin-ended coding ends (HP-CEs) and blunt signal ends (SEs). After the cleavage, RAG1/2 proteins bind and retain the recombination ends to form post-cleavage complexes (PCC), which collaborates with the NHEJ machinery for appropriate transfer of recombination ends to NHEJ for proper end resolution. However, little is known about the molecular basis of this collaboration, partly attributed to the lack of sensitive assays to reveal the interaction of PCC with HP-CEs. Here, for the first time, by using two complementary fluorescence-based techniques, fluorescence anisotropy and fluorescence resonance energy transfer (FRET), I managed to monitor the RAG1/2-catalyzed cleavage reaction in real time, from the pre-cleavage to the post-cleavage stages. By examining the dynamic fluorescence changes during the RAG-mediated cleavage reactions, and by manipulating the reaction conditions, I was able to characterize some fundamental properties of RAG-DNA interactions before and after cleavage. Firstly, Mg2+, known as a physiological cofactor at the excision step, also promotes the HP-CEs retention in the RAG complex after cleavage. Secondly, the structure of pre-cleavage complex may affect the subsequent collaborations with NHEJ for end resolution. Thirdly, the non-core region of RAG2 may have differential influences on the PCC retention of HP-CEs and SEs. Furthermore, I also provide the first evidence of RAG1-mediated regulation of RAG2. Our study provides important insights into the multilayered regulatory mechanisms, in modulating recombination events in developing lymphocytes and paves the way for possible development of detection and diagnotic markers for defective recombination events that are often associated immunodeficiency and/or lymphoid malignancy.
ContributorsWang, Guannan (Author) / Chang, Yung (Thesis advisor) / Levitus, Marcia (Committee member) / Misra, Rajeev (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Intrinsic antibiotic resistance is of growing concern in modern medical treatment. The primary action of multidrug resistant strains is through over-expression of active transporters which recognize a broad range of antibiotics. In Escherichia coli, the TolC-AcrAB complex has become a model system to understand antibiotic efflux. While the structures of these three proteins (and many of their homologs) are known, the exact mechanisms of interaction are still poorly understood. By mutational analysis of the TolC turn 1 residues, a drug hypersensitive mutant has been identified which is defective in functional interactions with AcrA and AcrB. Antibiotic resistant revertants carry alterations in both TolC and AcrA act by stabilizing functional complex assembly and opening of the TolC aperture, as monitored by stability of a labile TolC mutant and sensitivity to vancomycin, respectively. Alterations in the AcrB periplasmic hairpin loops lead to a similar antibiotic hypersensitivity phenotype and destabilized complex assembly. Likewise, alterations in TolC which constitutively open the aperture suppress this antibiotic sensitivity. Suppressor alterations in AcrA and AcrB partially restore antibiotic resistance by mediating stability of the complex. The AcrA suppressor alterations isolated in these studies map to the three crystallized domains and it is concluded they alter the AcrA conformation such that it is permanently fixed in an active state, which wild type only transiently goes through when activated by AcrB. Through this genetic evidence, a direct interaction between TolC and AcrB which is stabilized by AcrA has been proposed. In addition to stabilizing the interactions between TolC and AcrB, AcrA is also responsible for triggering opening of the TolC aperture by mediating energy flow from AcrB to TolC. By permanently altering the conformation of AcrA, suppressor mutants allow defective TolC or AcrB mutants to regain functional interactions lost by the initial mutations. The data provide the genetic proof for direct interaction between AcrB and that AcrA mediated opening of TolC requires AcrB as a scaffold.
ContributorsWeeks, Jon William (Author) / Misra, Rajeev (Thesis advisor) / Stout, Valerie (Committee member) / Shi, Yixin (Committee member) / Clark-Curtiss, Josephine (Committee member) / Arizona State University (Publisher)
Created2012
Description
The discovery and development of novel antibacterial agents is essential to address the rising health concern over antibiotic resistant bacteria. This research investigated the antibacterial activity of a natural clay deposit near Crater Lake, Oregon, that is effective at killing antibiotic resistant human pathogens. The primary rock types in the deposit are andesitic pyroclastic materials, which have been hydrothermally altered into argillic clay zones. High-sulfidation (acidic) alteration produced clay zones with elevated pyrite (18%), illite-smectite (I-S) (70% illite), elemental sulfur, kaolinite and carbonates. Low-sulfidation alteration at neutral pH generated clay zones with lower pyrite concentrations pyrite (4-6%), the mixed-layered I-S clay rectorite (R1, I-S) and quartz.
Antibacterial susceptibility testing reveals that hydrated clays containing pyrite and I-S are effective at killing (100%) of the model pathogens tested (E. coli and S. epidermidis) when pH (< 4.2) and Eh (> 450 mV) promote pyrite oxidation and mineral dissolution, releasing > 1 mM concentrations of Fe2+, Fe3+ and Al3+. However, certain oxidized clay zones containing no pyrite still inhibited bacterial growth. These clays buffered solutions to low pH (< 4.7) and oxidizing Eh (> 400 mV) conditions, releasing lower amounts (< 1 mM) of Fe and Al. The presence of carbonate in the clays eliminated antibacterial activity due to increases in pH, which lower pyrite oxidation and mineral dissolution rates.
The antibacterial mechanism of these natural clays was explored using metal toxicity and genetic assays, along with advanced bioimaging techniques. Antibacterial clays provide a continuous reservoir of Fe2+, Fe3+ and Al3+ that synergistically attack pathogens while generating hydrogen peroxide (H2O¬2). Results show that dissolved Fe2+ and Al3+ are adsorbed to bacterial envelopes, causing protein misfolding and oxidation in the outer membrane. Only Fe2+ is taken up by the cells, generating oxidative stress that damages DNA and proteins. Excess Fe2+ oxidizes inside the cell and precipitates Fe3+-oxides, marking the sites of hydroxyl radical (•OH) generation. Recognition of this novel geochemical antibacterial process should inform designs of new mineral based antibacterial agents and could provide a new economic industry for such clays.
Antibacterial susceptibility testing reveals that hydrated clays containing pyrite and I-S are effective at killing (100%) of the model pathogens tested (E. coli and S. epidermidis) when pH (< 4.2) and Eh (> 450 mV) promote pyrite oxidation and mineral dissolution, releasing > 1 mM concentrations of Fe2+, Fe3+ and Al3+. However, certain oxidized clay zones containing no pyrite still inhibited bacterial growth. These clays buffered solutions to low pH (< 4.7) and oxidizing Eh (> 400 mV) conditions, releasing lower amounts (< 1 mM) of Fe and Al. The presence of carbonate in the clays eliminated antibacterial activity due to increases in pH, which lower pyrite oxidation and mineral dissolution rates.
The antibacterial mechanism of these natural clays was explored using metal toxicity and genetic assays, along with advanced bioimaging techniques. Antibacterial clays provide a continuous reservoir of Fe2+, Fe3+ and Al3+ that synergistically attack pathogens while generating hydrogen peroxide (H2O¬2). Results show that dissolved Fe2+ and Al3+ are adsorbed to bacterial envelopes, causing protein misfolding and oxidation in the outer membrane. Only Fe2+ is taken up by the cells, generating oxidative stress that damages DNA and proteins. Excess Fe2+ oxidizes inside the cell and precipitates Fe3+-oxides, marking the sites of hydroxyl radical (•OH) generation. Recognition of this novel geochemical antibacterial process should inform designs of new mineral based antibacterial agents and could provide a new economic industry for such clays.
ContributorsMorrison, Keith D (Author) / Williams, Lynda B (Thesis advisor) / Williams, Stanley N (Thesis advisor) / Misra, Rajeev (Committee member) / Shock, Everett (Committee member) / Anbar, Ariel (Committee member) / Arizona State University (Publisher)
Created2015
Description
Design problem formulation is believed to influence creativity, yet it has received only modest attention in the research community. Past studies of problem formulation are scarce and often have small sample sizes. The main objective of this research is to understand how problem formulation affects creative outcome. Three research areas are investigated: development of a model which facilitates capturing the differences among designers' problem formulation; representation and implication of those differences; the relation between problem formulation and creativity.
This dissertation proposes the Problem Map (P-maps) ontological framework. P-maps represent designers' problem formulation in terms of six groups of entities (requirement, use scenario, function, artifact, behavior, and issue). Entities have hierarchies within each group and links among groups. Variables extracted from P-maps characterize problem formulation.
Three experiments were conducted. The first experiment was to study the similarities and differences between novice and expert designers. Results show that experts use more abstraction than novices do and novices are more likely to add entities in a specific order. Experts also discover more issues.
The second experiment was to see how problem formulation relates to creativity. Ideation metrics were used to characterize creative outcome. Results include but are not limited to a positive correlation between adding more issues in an unorganized way with quantity and variety, more use scenarios and functions with novelty, more behaviors and conflicts identified with quality, and depth-first exploration with all ideation metrics. Fewer hierarchies in use scenarios lower novelty and fewer links to requirements and issues lower quality of ideas.
The third experiment was to see if problem formulation can predict creative outcome. Models based on one problem were used to predict the creativity of another. Predicted scores were compared to assessments of independent judges. Quality and novelty are predicted more accurately than variety, and quantity. Backward elimination improves model fit, though reduces prediction accuracy.
P-maps provide a theoretical framework for formalizing, tracing, and quantifying conceptual design strategies. Other potential applications are developing a test of problem formulation skill, tracking students' learning of formulation skills in a course, and reproducing other researchers’ observations about designer thinking.
This dissertation proposes the Problem Map (P-maps) ontological framework. P-maps represent designers' problem formulation in terms of six groups of entities (requirement, use scenario, function, artifact, behavior, and issue). Entities have hierarchies within each group and links among groups. Variables extracted from P-maps characterize problem formulation.
Three experiments were conducted. The first experiment was to study the similarities and differences between novice and expert designers. Results show that experts use more abstraction than novices do and novices are more likely to add entities in a specific order. Experts also discover more issues.
The second experiment was to see how problem formulation relates to creativity. Ideation metrics were used to characterize creative outcome. Results include but are not limited to a positive correlation between adding more issues in an unorganized way with quantity and variety, more use scenarios and functions with novelty, more behaviors and conflicts identified with quality, and depth-first exploration with all ideation metrics. Fewer hierarchies in use scenarios lower novelty and fewer links to requirements and issues lower quality of ideas.
The third experiment was to see if problem formulation can predict creative outcome. Models based on one problem were used to predict the creativity of another. Predicted scores were compared to assessments of independent judges. Quality and novelty are predicted more accurately than variety, and quantity. Backward elimination improves model fit, though reduces prediction accuracy.
P-maps provide a theoretical framework for formalizing, tracing, and quantifying conceptual design strategies. Other potential applications are developing a test of problem formulation skill, tracking students' learning of formulation skills in a course, and reproducing other researchers’ observations about designer thinking.
ContributorsDinar, Mahmoud (Author) / Shah, Jami J. (Thesis advisor) / Langley, Pat (Committee member) / Davidson, Joseph K. (Committee member) / Lande, Micah (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a brief review is made about these three material systems. In Chapter 2, detailed discussion of the statistical morphological descriptors and a stochastic optimization approach for microstructure reconstruction is presented. In Chapter 3, the lattice particle method for micromechanical analysis of complex heterogeneous materials is introduced. In Chapter 4, a new class of hyperuniform heterogeneous material with superior mechanical properties is investigated. In Chapter 5, a bio-material system, i.e., cellularized collagen gel is modeled using correlation functions and stochastic reconstruction to study the collective dynamic behavior of the embed tumor cells. In chapter 6, LMPA soft robotic system is generated by generalizing the correlation functions and the rigidity tunability of this smart composite is discussed. In Chapter 7, a future work plan is presented.
ContributorsXu, Yaopengxiao (Author) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Wang, Qing Hua (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Lignocellulosic biomass represents a renewable domestic feedstock that can support large-scale biochemical production processes for fuels and specialty chemicals. However, cost-effective conversion of lignocellulosic sugars into valuable chemicals by microorganisms still remains a challenge. Biomass recalcitrance to saccharification, microbial substrate utilization, bioproduct titer toxicity, and toxic chemicals associated with chemical pretreatments are at the center of the bottlenecks limiting further commercialization of lignocellulose conversion. Genetic and metabolic engineering has allowed researchers to manipulate microorganisms to overcome some of these challenges, but new innovative approaches are needed to make the process more commercially viable. Transport proteins represent an underexplored target in genetic engineering that can potentially help to control the input of lignocellulosic substrate and output of products/toxins in microbial biocatalysts. In this work, I characterize and explore the use of transport systems to increase substrate utilization, conserve energy, increase tolerance, and enhance biocatalyst performance.
ContributorsKurgan, Gavin (Author) / Wang, Xuan (Thesis advisor) / Nielsen, David (Committee member) / Misra, Rajeev (Committee member) / Nannenga, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for novel methods to better characterize the pipe material properties. The focus of this study is the design of a Bayesian Network information fusion model for the prediction of accurate probabilistic pipe strength and consequently the maximum allowable operating pressure. A multimodal diagnosis is performed by assessing the mechanical property variation within the pipe in terms of material property measurements, such as microstructure, composition, hardness and other mechanical properties through experimental analysis, which are then integrated with the Bayesian network model that uses a Markov chain Monte Carlo (MCMC) algorithm. Prototype testing is carried out for model verification, validation and demonstration and data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. With a view of providing a holistic measure of material performance in service, the fatigue properties of the pipe steel are investigated. The variation in the fatigue crack growth rate (da/dN) along the direction of the pipe wall thickness is studied in relation to the microstructure and the material constants for the crack growth have been reported. A combination of imaging and composition analysis is incorporated to study the fracture surface of the fatigue specimen. Finally, some well-known statistical inference models are employed for prediction of manufacturing process parameters for steel pipelines. The adaptability of the small datasets for the accuracy of the prediction outcomes is discussed and the models are compared for their performance.
ContributorsDahire, Sonam (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Coordination and control of Intelligent Agents as a team is considered in this thesis.
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
ContributorsKAMBAM, KARTHIK (Author) / Zhang, Wenlong (Thesis advisor) / Nedich, Angelia (Thesis advisor) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Aging-related damage and failure in structures, such as fatigue cracking, corrosion, and delamination, are critical for structural integrity. Most engineering structures have embedded defects such as voids, cracks, inclusions from manufacturing. The properties and locations of embedded defects are generally unknown and hard to detect in complex engineering structures. Therefore, early detection of damage is beneficial for prognosis and risk management of aging infrastructure system.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
ContributorsChang, Qinan (Author) / Liu, Yongming (Thesis advisor) / Mignolet, Marc (Committee member) / Chattopadhyay, Aditi (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2019