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- All Subjects: Mechanical Engineering
- All Subjects: Machine Learning
- Member of: Theses and Dissertations
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
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Description
ABSTRACT Electronics especially mobile electronics such as smart phones, tablet PCs, notebooks and digital cameras are undergoing rapid development nowadays and have thoroughly changed our lives. With the requirement of more transistors, higher power, smaller size, lighter weight and even bendability, thermal management of these devices became one of the key challenges. Compared to active heat management system, heat pipe, which is a passive fluidic system, is considered promising to solve this problem. However, traditional heat pipes have size, weight and capillary limitation. Thus new type of heat pipe with smaller size, lighter weight and higher capillary pressure is needed. Nanofiber has been proved with superior properties and has been applied in multiple areas. This study discussed the possibility of applying nanofiber in heat pipe as new wick structure. In this study, a needleless electrospinning device with high productivity rate was built onsite to systematically investigate the effect of processing parameters on fiber properties as well as to generate nanofiber mat to evaluate its capability in electronics cooling. Polyethylene oxide (PEO) and Polyvinyl Alcohol (PVA) nanofibers were generated. Tensiometer was used for wettability measurement. The results show that independent parameters including spinneret type, working distance, solution concentration and polymer type are strongly correlated with fiber morphology compared to other parameters. The results also show that the fabricated nanofiber mat has high capillary pressure.
ContributorsSun, Tianwei (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongyu (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2014
Description
Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.
ContributorsHuff, Daniel W (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Chakrabarti, Chaitali (Committee member) / Chattopadhyay, Aditi (Committee member) / Arizona State University (Publisher)
Created2013
Description
As robots are increasingly migrating out of factories and research laboratories and into our everyday lives, they should move and act in environments designed for humans. For this reason, the need of anthropomorphic movements is of utmost importance. The objective of this thesis is to solve the inverse kinematics problem of redundant robot arms that results to anthropomorphic configurations. The swivel angle of the elbow was used as a human arm motion parameter for the robot arm to mimic. The swivel angle is defined as the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Using kinematic data recorded from human subjects during every-day life tasks, the linear sensorimotor transformation model was validated and used to estimate the swivel angle, given the desired end-effector position. Defining the desired swivel angle simplifies the kinematic redundancy of the robot arm. The proposed method was tested with an anthropomorphic redundant robot arm and the computed motion profiles were compared to the ones of the human subjects. This thesis shows that the method computes anthropomorphic configurations for the robot arm, even if the robot arm has different link lengths than the human arm and starts its motion at random configurations.
ContributorsWang, Yuting (Author) / Artemiadis, Panagiotis (Thesis advisor) / Mignolet, Marc (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
The objective of this research is to develop methods for generating the Tolerance-Map for a line-profile that is specified by a designer to control the geometric profile shape of a surface. After development, the aim is to find one that can be easily implemented in computer software using existing libraries. Two methods were explored: the parametric modeling method and the decomposed modeling method. The Tolerance-Map (T-Map) is a hypothetical point-space, each point of which represents one geometric variation of a feature in its tolerance-zone. T-Maps have been produced for most of the tolerance classes that are used by designers, but, prior to the work of this project, the method of construction required considerable intuitive input, rather than being based primarily on automated computer tools. Tolerances on line-profiles are used to control cross-sectional shapes of parts, such as every cross-section of a mildly twisted compressor blade. Such tolerances constrain geometric manufacturing variations within a specified two-dimensional tolerance-zone. A single profile tolerance may be used to control position, orientation, and form of the cross-section. Four independent variables capture all of the profile deviations: two independent translations in the plane of the profile, one rotation in that plane, and the size-increment necessary to identify one of the allowable parallel profiles. For the selected method of generation, the line profile is decomposed into three types of segments, a primitive T-Map is produced for each segment, and finally the T-Maps from all the segments are combined to obtain the T-Map for the given profile. The types of segments are the (straight) line-segment, circular arc-segment, and the freeform-curve segment. The primitive T-Maps are generated analytically, and, for freeform-curves, they are built approximately with the aid of the computer. A deformation matrix is used to transform the primitive T-Maps to a single coordinate system for the whole profile. The T-Map for the whole line profile is generated by the Boolean intersection of the primitive T-Maps for the individual profile segments. This computer-implemented method can generate T-Maps for open profiles, closed ones, and those containing concave shapes.
ContributorsHe, Yifei (Author) / Davidson, Joseph (Thesis advisor) / Shah, Jami (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
Description
The presented work in this report is about Real time Estimation of wind and analyzing current wind correction algorithm in commercial off the shelf Autopilot board. The open source ArduPilot Mega 2.5 (APM 2.5) board manufactured by 3D Robotics is used. Currently there is lot of development being done in the field of Unmanned Aerial Systems (UAVs), various aerial platforms and corresponding; autonomous systems for them. This technology has advanced to such a stage that UAVs can be used for specific designed missions and deployed with reliability. But in some areas like missions requiring high maneuverability with greater efficiency is still under research area. This would help in increasing reliability and augmenting range of UAVs significantly. One of the problems addressed through this thesis work is, current autopilot systems have algorithm that handles wind by attitude correction with appropriate Crab angle. But the real time wind vector (direction) and its calculated velocity is based on geometrical and algebraic transformation between ground speed and air speed vectors. This method of wind estimation and prediction, many a times leads to inaccuracy in attitude correction. The same has been proved in the following report with simulation and actual field testing. In later part, new ways to tackle while flying windy conditions have been proposed.
ContributorsBiradar, Anandrao Shesherao (Author) / Saripalli, Srikanth (Thesis advisor) / Berman, Spring (Thesis advisor) / Thanga, Jekan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multi-pulse particle tracking velocimetry (multi-pulse PTV) is a recently proposed flow measurement technique aiming to improve the performance of conventional PTV/ PIV. In this work, multi-pulse PTV is assessed based on PTV simulations in terms of spatial resolution, velocity measurement accuracy and the capability of acceleration measurement. The errors of locating particles, velocity measurement and acceleration measurement are analytically calculated and compared among quadruple-pulse, triple-pulse and dual-pulse PTV. The optimizations of triple-pulse and quadruple-pulse PTV are discussed, and criteria are developed to minimize the combined error in position, velocity and acceleration. Experimentally, the velocity and acceleration fields of a round impinging air jet are measured to test the triple-pulse technique. A high speed beam-splitting camera and a custom 8-pulsed laser system are utilized to achieve good timing flexibility and temporal resolution. A new method to correct the registration error between CCDs is also presented. Consequently, the velocity field shows good consistency between triple-pulse and dual-pulse measurements. The mean acceleration profile along the centerline of the jet is used as the ground truth for the verification of the triple-pulse PIV measurements of the acceleration fields. The instantaneous acceleration field of the jet is directly measured by triple-pulse PIV and presented. Accelerations up to 1,000 g's are measured in these experiments.
ContributorsDing, Liuyang (Author) / Adrian, Ronald J. (Thesis advisor) / Herrmann, Marcus (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2014
Description
There has been considerable advancement in the algae research field to move algae production for biofuels and bio-products forward to become commercially viable. However, there is one key element that humans cannot control, the natural externalities that impact production. An algae cultivation system is similar to agricultural crop farming practices. Algae are grown on an area of land for a certain time period with the aim of harvesting the biomass produced. One of the advantages of using algae biomass is that it can be used as a source of energy in the form of biofuels. Major advances in algae research and development practices have led to new knowledge about the remarkable potential of algae to serve as a sustainable source of biofuel. The challenge is to make the price of biofuels from algae cost-competitive with the price of petroleum-based fuels. The scope of this research was to design a concept for an automated system to control specific externalities and determine if integrating the system in an algae cultivation system could improve the algae biomass production process. This research required the installation and evaluation of an algae cultivation process, components selection and computer software programming for an automated system. The results from the automated system based on continuous real time monitored variables validated that the developed system contributes insights otherwise not detected from a manual measurement approach. The implications of this research may lead to technology that can be used as a base model to further improve algae cultivation systems.
ContributorsPuruhito, Emil (Author) / Sommerfeld, Milton (Thesis advisor) / Gintz, Jerry (Thesis advisor) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
ContributorsViswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Thesis advisor) / T.H CHI, Michelene (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Small metallic parts of size less than 1mm, with features measured in tens of microns, with tolerances as small as 0.1 micron are in demand for the research in many fields such as electronics, optics, and biomedical engineering. Because of various drawbacks with non-mechanical micromanufacturing processes, micromilling has shown itself to be an attractive alternative manufacturing method. Micromilling is a microscale manufacturing process that can be used to produce a wide range of small parts, including those that have complex 3-dimensional contours. Although the micromilling process is superficially similar to conventional-scale milling, the physical processes of micromilling are unique due to the scale effects. These scale effects occur due to unequal scaling of the parameters from the macroscale to the microscale milling. One key example of scale effects in micromilling process is a geometrical source of error known as chord error. The chord error limits the feedrate to a reduced value to produce the features within machining tolerances. In this research, it is hypothesized that the increase of chord error in micromilling can be alleviated by intelligent modification of the kinematic arrangement of the micromilling machine. Currently, all 3-axis micromilling machines are constructed with a Cartesian kinematic arrangement with three perpendicular linear axes. In this research, the cylindrical kinematic arrangement is introduced, and an analytical expression for the chord error for this arrangement is derived. The numerical simulations are performed to evaluate the chord errors for the cylindrical kinematic arrangement. It is found that cylindrical kinematic arrangement gives reduced chord error for some types of the desired toolpaths. Then, the kinematic redundancy is introduced to design a novel kinematic arrangement. Several desired toolpaths have been numerically simulated to evaluate the chord error for kinematically redundant arrangement. It is concluded that this arrangement gives up to 5 times reduced error for all the desired toolpaths considered, and allows significant gains in allowable feedrates.
ContributorsChukewad, Yogesh Madhavrao (Author) / SODEMANN, ANGELA A (Thesis advisor) / Davidson, Joseph K. (Thesis advisor) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2014