Matching Items (60)
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- All Subjects: Mechanical Engineering
- All Subjects: soft robotics
- Creators: Chattopadhyay, Aditi
- Member of: Theses and Dissertations
- Resource Type: Text
Description
Damage and failure of advanced composite materials and structures are often manifestations of nonlinear deformation that involve multiple mechanisms and their interactions at the constituent length scale. The presence and interactions of inelastic microscale constituents strongly influence the macroscopic damage anisotropy and useful residual life. The mechano-chemical interactions between constituents at the atomistic length scale play a more critical role with nanoengineered composites. Therefore, it is desirable to link composite behavior to specific microscopic constituent properties explicitly and lower length scale features using high-fidelity multiscale modeling techniques.In the research presented in this dissertation, an atomistically-informed multiscale modeling framework is developed to investigate damage evolution and failure in composites with radially-grown carbon nanotube (CNT) architecture. A continuum damage mechanics (CDM) model for the radially-grown CNT interphase region is developed with evolution equations derived using atomistic simulations. The developed model is integrated within a high-fidelity generalized method of cells (HFGMC) micromechanics theory and is used to parametrically investigate the influence of various input micro and nanoscale parameters on the mechanical properties, such as elastic stiffness, strength, and toughness. In addition, the inter-fiber stresses and the onset of damage in the presence of the interphase region are investigated to better understand the energy dissipation mechanisms that attribute to the enhancement in the macroscopic out-of-plane strength and toughness. Note that the HFGMC theory relies heavily on the description of microscale features and requires many internal variables, leading to high computational costs. Therefore, a novel reduced-order model (ROM) is also developed to surrogate full-field nonlinear HFGMC simulations and decrease the computational time and memory requirements of concurrent multiscale simulations significantly.
The accurate prediction of composite sandwich materials' thermal stability and durability remains a challenge due to the variability of thermal-related material coefficients at different temperatures and the extensive use of bonded fittings. Consequently, the dissertation also investigates the thermomechanical performance of a complex composite sandwich space structure subject to thermal cycling. Computational finite element (FE) simulations are used to investigate the intrinsic failure mechanisms and damage precursors in honeycomb core composite sandwich structures with adhesively bonded fittings.
ContributorsVenkatesan, Karthik Rajan (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Yekani Fard, Masoud (Committee member) / Stoumbos, Tom (Committee member) / Arizona State University (Publisher)
Created2021
Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.
Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Carbon nanotubes (CNTs) have emerged as compelling materials for enhancing both electrical and mechanical properties of aerospace structures. Buckypaper (BP), a porous
membrane consisting of a highly cross-linked network of CNTs, can be effectively
integrated with carbon fiber reinforced polymer (CFRP) composites to simultaneously
enhance their electromagnetic interference (EMI) shielding effectiveness (SE) and
mechanical properties. In existing literature, CNT based nanocomposites are shown to
improve the flexural strength and stiffness of CFRP laminates. However, a limited amount
of research has been reported in predicting the EMI SE of hybrid BP embedded CFRP
composites.
To characterize the EMI shielding response of hybrid BP/CFRP laminates, a novel
modeling approach based on equivalent electrical circuits is employed to estimate the
electrical conductivity of unidirectional CFRP plies. This approach uses Monte Carlo
simulations and accounts for the effects of quantum tunneling at the fiber-fiber contact
region. This study specifically examines a signal frequency range of 50 MHz to 12 GHz,
corresponding to the very high to X band spectrum. The results indicate that at a frequency
of 12 GHz, the longitudinal conductivity decreases to around ~3,300 S/m from an initial
DC value of 40,000 S/m, while the transverse conductivity concurrently increases from
negligible to approximately ~12.67 S/m. These results are then integrated into Ansys High
Frequency Structure Simulator (HFSS) to predict EMI SE by simulating the propagation
of electromagnetic waves through a semi-infinite composite shield representative volume
element. The numerical simulations illustrate that incorporating BP allows for significant
ii
improvements in SE of hybrid BP/CFRP composites. At 12 GHz signal frequency, for
example, the incorporation of a single BP interleave enhances the SE of a [90,0] laminate
by up to ~64%, while the incorporation of two BP interleaves in a [90,0,+45,-45,0,90]s
balanced symmetric laminate enhances its SE by ~20% . This enhancement is due to the
high conductivity of BP at high frequencies.
Additionally, to evaluate the flexural property enhancements due to BP, experimental
three-point bend tests were conducted on different configurations of hybrid BP/CFRP
laminates, and their strength and stiffness were compared with the non-BP samples.
Micrographs of failed samples are acquired using an optical microscope, which provides
insights into their underlying damage mechanisms. Fractography analysis confirms the role
of BP in preventing through-thickness crack propagation, attributed to the excellent crack
retardation properties of CNTs.
ContributorsTripathi, Kartik (Author) / Chattopadhyay, Aditi (Thesis advisor) / Henry, Todd C. (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2024
Description
According to Our World in Data, the industry sector contributes approximately 5.2 percent of the world's greenhouse gas emissions in 2016 [1]. Of that percentage, the cement industry contributes approximately 3 percent, thus accounting for more than 57 percent of all greenhouse gas emissions within the industry sector. Industrial-scale heating that is powered by renewable energy sources has the potential to combat this issue. This paper aims to analyze and model the Reverse Brayton Cycle to be used as a heat pump in a novel cement production system. The Simple Reverse Brayton Cycle and its potential concerning performance indicators such as coefficient of performance and scalability are determined. A Regenerative Brayton cycle is modeled in MATLAB® programming in order to be optimized and compared to conventional processes that require higher temperatures. Traditional manufacturing methods are discussed. Furthermore, possible methods of improvement are explored to view its effect on performance and temperatures between stages within the cycle.
ContributorsRivera, Daniel E (Author) / Phelan, Patrick (Thesis advisor) / Milcarek, Ryan (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2024
Description
Solid Oxide Fuel Cells (SOFCs) generate electricity using only hydrogen and oxygen and they form H2O as the only byproduct, giving them the potential to significantly reduce carbon emissions and the impacts of global warming. In order to meet the global power demands today, SOFCs need to significantly increase their power density and improve robustness in startup and cycling operations. This study explores the impact of decreasing the anode thickness to improve the mass transport of the fuel through the anode of a micro-tubular (mT) SOFC because few studies have reported the correlation between the two. Decreasing the thickness decreases the chance for concentration overpotential which is caused by not enough of the reactants being able to reach the reaction site while products are not able to be removed quickly enough. Experiments were performed in a split tube furnace heated to 750°C with nickel-yttria stabilized zirconia (Ni-YSZ) supported cells. Pure hydrogen was supplied to the cell at rates of 10, 20, 30, and 40 mL/min while the cathode was supplied air from the environment. The cell's performance was studied using the current-voltage method to generate polarization curves and electrochemical impedance spectroscopy to create Bode and Nyquist plots. The results from the electrochemical impedance spectroscopy show a lower impedance for the frequencies pertaining to the gas diffusion in the anode for the thinner cells. This suggests that decreasing the anode thickness increases the mass transport of the gas. Additionally, through a distribution of relaxation times (DRT) analysis, the peaks vary between the two cell thicknesses at the frequencies pertaining to gas diffusion in anode-supported cells, implicating the decreased resistance created by thinning the anode layer.
ContributorsPhillips, Kristina (Author) / Milcarek, Ryan (Thesis advisor) / Wang, Robert (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2023
Description
Uncertainty quantification is critical for engineering design and analysis. Determining appropriate ways of dealing with uncertainties has been a constant challenge in engineering. Statistical methods provide a powerful aid to describe and understand uncertainties. This work focuses on applying Bayesian methods and machine learning in uncertainty quantification and prognostics among all the statistical methods. This study focuses on the mechanical properties of materials, both static and fatigue, the main engineering field on which this study focuses. This work can be summarized in the following items: First, maintaining the safety of vintage pipelines requires accurately estimating the strength. The objective is to predict the reliability-based strength using nondestructive multimodality surface information. Bayesian model averaging (BMA) is implemented for fusing multimodality non-destructive testing results for gas pipeline strength estimation. Several incremental improvements are proposed in the algorithm implementation. Second, the objective is to develop a statistical uncertainty quantification method for fatigue stress-life (S-N) curves with sparse data.Hierarchical Bayesian data augmentation (HBDA) is proposed to integrate hierarchical Bayesian modeling (HBM) and Bayesian data augmentation (BDA) to deal with sparse data problems for fatigue S-N curves. The third objective is to develop a physics-guided machine learning model to overcome limitations in parametric regression models and classical machine learning models for fatigue data analysis. A Probabilistic Physics-guided Neural Network (PPgNN) is proposed for probabilistic fatigue S-N curve estimation. This model is further developed for missing data and arbitrary output distribution problems. Fourth, multi-fidelity modeling combines the advantages of low- and high-fidelity models to achieve a required accuracy at a reasonable computation cost. The fourth objective is to develop a neural network approach for multi-fidelity modeling by learning the correlation between low- and high-fidelity models. Finally, conclusions are drawn, and future work is outlined based on the current study.
ContributorsChen, Jie (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Ren, Yi (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study investigates the energy saving potential of high albedo roof coatings which are designed to reflect a large proportion of solar radiation compared to traditional roofing materials. Using EnergyPlus simulations, the efficacy of silicone, acrylic, and aluminum roof coatings is assessed across two prototype commercial buildings—a standalone retail (2,294 m2 or 24,692 ft2) and a strip-mall (2,090 m2 or 22,500 ft2)—located in four cities: Phoenix, Houston, Los Angeles, and Miami. The performance of reflective coatings was compared with respect to a black roof having a solar reflectance of 5% and a thermal emittance of 90%. A sensitivity analysis was done to assess the impact of solar reflectance and thermal emittance on the ability of roof coatings to reduce surface temperatures, a key factor behind energy savings. This factor plays a crucial role in all three heat transfer mechanisms: conduction, convection, and radiation. The rooftop surface temperature exhibits considerable variation depending on the solar reflectance and thermal emittance attributes of the roof. A contour plot between these properties reveals that high values of both result in reduced cooling needs and a heating penalty which is insignificant when compared with cooling savings for cooling-dominant climates like Phoenix where the cooling demand significantly outweighs the heating demand, yielding significant energy savings. Furthermore, the study also investigates the effects of reflective coatings on buildings that have photovoltaic solar panels installed on them. This includes exploring their impact on building HVAC loads, as well as the performance improvement due to the reduced temperatures beneath them.
ContributorsSharma, Ajay Kumar (Author) / Phelan, Patrick (Thesis advisor) / Neithalath, Narayanan (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2024
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
Description
This thesis presents the design and testing of a soft robotic device for water utility pipeline inspection. The preliminary findings of this new approach to conventional methods of pipe inspection demonstrate that a soft inflatable robot can successfully traverse the interior space of a range of diameter pipes using pneumatic and without the need to adjust rigid, mechanical components. The robot utilizes inflatable soft actuators with an adjustable radius which, when pressurized, can provide a radial force, effectively anchoring the device in place. Additional soft inflatable actuators translate forces along the center axis of the device which creates forward locomotion when used in conjunction with the radial actuation. Furthermore, a bio-inspired control algorithm for locomotion allows the robot to maneuver through a pipe by mimicking the peristaltic gait of an inchworm. This thesis provides an examination and evaluation of the structure and behavior of the inflatable actuators through computational modeling of the material and design, as well as the experimental data of the forces and displacements generated by the actuators. The theoretical results are contrasted with/against experimental data utilizing a physical prototype of the soft robot. The design is anticipated to enable compliant robots to conform to the space offered to them and overcome occlusions from accumulated solids found in pipes. The intent of the device is to be used for inspecting existing pipelines owned and operated by Salt River Project, a Phoenix-area water and electricity utility provider.
ContributorsAdams, Wade Silas (Author) / Aukes, Daniel (Thesis advisor) / Sugar, Thomas (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
In these times of increasing industrialization, there arises a need for effective and energy efficient heat transfer/heat exchange devices. The focus nowadays is on identifying various methods and techniques which can aid the process of developing energy efficient devices. One of the most common heat transfer devices is a heat exchanger. Heat exchangers are an essential commodity to any industry and their efficiency can play an important role in making industries energy efficient and reduce the energy losses in the devices, in turn decreasing energy inputs to run the industry.
One of the ways in which we can improve the efficiency of heat exchangers is by applying ultrasonic energy to a heat exchanger. This research explores the possibility of introducing the external input of ultrasonic energy to increase the efficiency of the heat exchanger. This increase in efficiency can be estimated by calculating the parameters important for the characterization of a heat exchanger, which are effectiveness (ε) and overall heat transfer coefficient (U). These parameters are calculated for both the non-ultrasound and ultrasound conditions in the heat exchanger.
This a preliminary study of ultrasound and its effect on a conventional shell-and-coil heat exchanger. From the data obtained it can be inferred that the increase in effectiveness and overall heat transfer coefficient upon the application of ultrasound is 1% and 6.22% respectively.
One of the ways in which we can improve the efficiency of heat exchangers is by applying ultrasonic energy to a heat exchanger. This research explores the possibility of introducing the external input of ultrasonic energy to increase the efficiency of the heat exchanger. This increase in efficiency can be estimated by calculating the parameters important for the characterization of a heat exchanger, which are effectiveness (ε) and overall heat transfer coefficient (U). These parameters are calculated for both the non-ultrasound and ultrasound conditions in the heat exchanger.
This a preliminary study of ultrasound and its effect on a conventional shell-and-coil heat exchanger. From the data obtained it can be inferred that the increase in effectiveness and overall heat transfer coefficient upon the application of ultrasound is 1% and 6.22% respectively.
ContributorsAnnam, Roshan Sameer (Author) / Phelan, Patrick (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2019