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- Genre: Masters Thesis
- Creators: Yang, Yezhou
- Member of: Theses and Dissertations
Description
Gallium-based liquid metals are of interest for a variety of applications including flexible electronics, soft robotics, and biomedical devices. Still, nano- to microscale device fabrication with these materials is challenging because of their strong adhesion to a majority of substrates. This unusual high adhesion is attributed to the formation of a thin oxide shell; however, its role in the adhesion process has not yet been established. In the first part of the thesis, we described a multiscale study aiming at understanding the fundamental mechanisms governing wetting and adhesion of gallium-based liquid metals. In particular, macroscale dynamic contact angle measurements were coupled with Scanning Electron Microscope (SEM) imaging to relate macroscopic drop adhesion to morphology of the liquid metal-surface interface. In addition, room temperature liquid-metal microfluidic devices are also attractive systems for hyperelastic strain sensing. Currently two types of liquid metal-based strain sensors exist for inplane measurements: single-microchannel resistive and two-microchannel capacitive devices. However, with a winding serpentine channel geometry, these sensors typically have a footprint of about a square centimeter, limiting the number of sensors that can be embedded into. In the second part of the thesis, firstly, simulations and an experimental setup consisting of two GaInSn filled tubes submerged within a dielectric liquid bath are used to quantify the effects of the cylindrical electrode geometry including diameter, spacing, and meniscus shape as well as dielectric constant of the insulating liquid and the presence of tubing on the overall system's capacitance. Furthermore, a procedure for fabricating the two-liquid capacitor within a single straight polydiemethylsiloxane channel is developed. Lastly, capacitance and response of this compact device to strain and operational issues arising from complex hydrodynamics near liquid-liquid and liquid-elastomer interfaces are described.
ContributorsLiu, Shanliangzi (Author) / Rykaczewski, Konrad (Thesis advisor) / Alford, Terry (Committee member) / Herrmann, Marcus (Committee member) / Hildreth, Owen (Committee member) / Arizona State University (Publisher)
Created2015
Description
Nanostructured materials show signicant enhancement in the thermoelectric g-
ure of merit (zT) due to quantum connement eects. Improving the eciency of
thermoelectric devices allows for the development of better, more economical waste
heat recovery systems. Such systems may be used as bottoming or co-generation
cycles in conjunction with conventional power cycles to recover some of the wasted
heat. Thermal conductivity measurement systems are an important part of the char-
acterization processes of thermoelectric materials. These systems must possess the
capability of accurately measuring the thermal conductivity of both bulk and thin-lm
samples at dierent ambient temperatures.
This paper discusses the construction, validation, and improvement of a thermal
conductivity measurement platform based on the 3-Omega technique. Room temperature
measurements of thermal conductivity done on control samples with known properties
such as undoped bulk silicon (Si), bulk gallium arsenide (GaAs), and silicon dioxide
(SiO2) thin lms yielded 150 W=m􀀀K, 50 W=m􀀀K, and 1:46 W=m􀀀K respectively.
These quantities were all within 8% of literature values. In addition, the thermal
conductivity of bulk SiO2 was measured as a function of temperature in a Helium-
4 cryostat from 75K to 250K. The results showed good agreement with literature
values that all fell within the error range of each measurement. The uncertainty in
the measurements ranged from 19% at 75K to 30% at 250K. Finally, the system
was used to measure the room temperature thermal conductivity of a nanocomposite
composed of cadmium selenide, CdSe, nanocrystals in an indium selenide, In2Se3,
matrix as a function of the concentration of In2Se3. The observed trend was in
qualitative agreement with the expected behavior.
i
ure of merit (zT) due to quantum connement eects. Improving the eciency of
thermoelectric devices allows for the development of better, more economical waste
heat recovery systems. Such systems may be used as bottoming or co-generation
cycles in conjunction with conventional power cycles to recover some of the wasted
heat. Thermal conductivity measurement systems are an important part of the char-
acterization processes of thermoelectric materials. These systems must possess the
capability of accurately measuring the thermal conductivity of both bulk and thin-lm
samples at dierent ambient temperatures.
This paper discusses the construction, validation, and improvement of a thermal
conductivity measurement platform based on the 3-Omega technique. Room temperature
measurements of thermal conductivity done on control samples with known properties
such as undoped bulk silicon (Si), bulk gallium arsenide (GaAs), and silicon dioxide
(SiO2) thin lms yielded 150 W=m􀀀K, 50 W=m􀀀K, and 1:46 W=m􀀀K respectively.
These quantities were all within 8% of literature values. In addition, the thermal
conductivity of bulk SiO2 was measured as a function of temperature in a Helium-
4 cryostat from 75K to 250K. The results showed good agreement with literature
values that all fell within the error range of each measurement. The uncertainty in
the measurements ranged from 19% at 75K to 30% at 250K. Finally, the system
was used to measure the room temperature thermal conductivity of a nanocomposite
composed of cadmium selenide, CdSe, nanocrystals in an indium selenide, In2Se3,
matrix as a function of the concentration of In2Se3. The observed trend was in
qualitative agreement with the expected behavior.
i
ContributorsJaber, Abbas (Author) / Wang, Robert (Thesis advisor) / Wang, Liping (Committee member) / Rykaczewski, Konrad (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many physical phenomena and industrial applications involve multiphase fluid flows and hence it is of high importance to be able to simulate various aspects of these flows accurately. The Dynamic Contact Angles (DCA) and the contact lines at the wall boundaries are a couple of such important aspects. In the past few decades, many mathematical models were developed for predicting the contact angles of the inter-face with the wall boundary under various flow conditions. These models are used to incorporate the physics of DCA and contact line motion in numerical simulations using various interface capturing/tracking techniques. In the current thesis, a simple approach to incorporate the static and dynamic contact angle boundary conditions using the level set method is developed and implemented in multiphase CFD codes, LIT (Level set Interface Tracking) (Herrmann (2008)) and NGA (flow solver) (Desjardins et al (2008)). Various DCA models and associated boundary conditions are reviewed. In addition, numerical aspects such as the occurrence of a stress singularity at the contact lines and grid convergence of macroscopic interface shape are dealt with in the context of the level set approach.
ContributorsPendota, Premchand (Author) / Herrmann, Marcus (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2015
Description
In a collaborative environment where multiple robots and human beings are expected
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
ContributorsKumar, Ashish, M.S (Author) / Amor, Hani Ben (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
As additive manufacturing grows as a cost-effective method of manufacturing, lighter, stronger and more efficient designs emerge. Heat exchangers are one of the most critical thermal devices in the thermal industry. Additive manufacturing brings us a design freedom no other manufacturing technology offers. Advancements in 3D printing lets us reimagine and optimize the performance of the heat exchangers with an incredible design flexibility previously unexplored due to manufacturing constraints.
In this research, the additive manufacturing technology and the heat exchanger design are explored to find a unique solution to improve the efficiency of heat exchangers. This includes creating a Triply Periodic Minimal Surface (TPMS) geometry, Schwarz-D in this case, using Mathematica with a flexibility to control the cell size of the models generated. This model is then encased in a closed cubical surface with manifolds for fluid inlets and outlets before 3D printed using the polymer nylon for thermal evaluation.
In the extent of this study, the heat exchanger developed is experimentally evaluated. The data obtained are used to derive a relationship between the heat transfer effectiveness and the Number of Transfer Units (NTU).The pressure loss across a fluid channel of the Schwarz D geometry is also studied.
The data presented in this study are part of initial experimental evaluation of 3D printed TPMS heat exchangers.Among heat exchangers with similar performance, the Schwarz D geometry is 32% smaller compared to a shell-and-tube heat exchanger.
In this research, the additive manufacturing technology and the heat exchanger design are explored to find a unique solution to improve the efficiency of heat exchangers. This includes creating a Triply Periodic Minimal Surface (TPMS) geometry, Schwarz-D in this case, using Mathematica with a flexibility to control the cell size of the models generated. This model is then encased in a closed cubical surface with manifolds for fluid inlets and outlets before 3D printed using the polymer nylon for thermal evaluation.
In the extent of this study, the heat exchanger developed is experimentally evaluated. The data obtained are used to derive a relationship between the heat transfer effectiveness and the Number of Transfer Units (NTU).The pressure loss across a fluid channel of the Schwarz D geometry is also studied.
The data presented in this study are part of initial experimental evaluation of 3D printed TPMS heat exchangers.Among heat exchangers with similar performance, the Schwarz D geometry is 32% smaller compared to a shell-and-tube heat exchanger.
ContributorsChandrasekaran, Gokul (Author) / Phelan, Patrick E (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Schultz, Karl U (Committee member) / Arizona State University (Publisher)
Created2018
Description
Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision: including view-point in activity analysis, articulation in shape analysis, and measurement invariance in non-linear dynamical modeling. The increasing success of these methods is attributed to the complementary information that topology provides, as well as availability of tools for computing topological summaries such as persistence diagrams. However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets. In this paper theoretically well-grounded approaches to develop novel perturbation robust topological representations are presented, with the long-term view of making them amenable to fusion with contemporary learning architectures. The proposed representation lives on a Grassmann manifold and hence can be efficiently used in machine learning pipelines.
The proposed representation.The efficacy of the proposed descriptor was explored on three applications: view-invariant activity analysis, 3D shape analysis, and non-linear dynamical modeling. Favorable results in both high-level recognition performance and improved performance in reduction of time-complexity when compared to other baseline methods are obtained.
The proposed representation.The efficacy of the proposed descriptor was explored on three applications: view-invariant activity analysis, 3D shape analysis, and non-linear dynamical modeling. Favorable results in both high-level recognition performance and improved performance in reduction of time-complexity when compared to other baseline methods are obtained.
ContributorsThopalli, Kowshik (Author) / Turaga, Pavan Kumar (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
The advancements in additive manufacturing have made it possible to bring life to designs
that would otherwise exist only on paper. An excellent example of such designs
are the Triply Periodic Minimal Surface (TPMS) structures like Schwarz D, Schwarz
P, Gyroid, etc. These structures are self-sustaining, i.e. they require minimal supports
or no supports at all when 3D printed. These structures exist in stable form in
nature, like butterfly wings are made of Gyroids. Automotive and aerospace industry
have a growing demand for strong and light structures, which can be solved using
TPMS models. In this research we will try and understand some of the properties of
these Triply Periodic Minimal Surface (TPMS) structures and see how they perform
in comparison to the conventional models. The research was concentrated on the
mechanical, thermal and fluid flow properties of the Schwarz D, Gyroid and Spherical
Gyroid Triply Periodic Minimal Surface (TPMS) models in particular, other Triply
Periodic Minimal Surface (TPMS) models were not considered. A detailed finite
element analysis was performed on the mechanical and thermal properties using ANSYS
19.2 and the flow properties were analyzed using ANSYS Fluent under different
conditions.
that would otherwise exist only on paper. An excellent example of such designs
are the Triply Periodic Minimal Surface (TPMS) structures like Schwarz D, Schwarz
P, Gyroid, etc. These structures are self-sustaining, i.e. they require minimal supports
or no supports at all when 3D printed. These structures exist in stable form in
nature, like butterfly wings are made of Gyroids. Automotive and aerospace industry
have a growing demand for strong and light structures, which can be solved using
TPMS models. In this research we will try and understand some of the properties of
these Triply Periodic Minimal Surface (TPMS) structures and see how they perform
in comparison to the conventional models. The research was concentrated on the
mechanical, thermal and fluid flow properties of the Schwarz D, Gyroid and Spherical
Gyroid Triply Periodic Minimal Surface (TPMS) models in particular, other Triply
Periodic Minimal Surface (TPMS) models were not considered. A detailed finite
element analysis was performed on the mechanical and thermal properties using ANSYS
19.2 and the flow properties were analyzed using ANSYS Fluent under different
conditions.
ContributorsRaja, Faisal (Author) / Phelan, Patrick (Thesis advisor) / Bhate, Dhruv (Committee member) / Rykaczewski, Konrad (Committee member) / Arizona State University (Publisher)
Created2019
Description
Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.
ContributorsPonneganti, Ramu (Author) / Yau, Stephen (Thesis advisor) / Richa, Andrea (Committee member) / Yang, Yezhou (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