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Description
Information exists in various forms and a better utilization of the available information can benefit the system awareness and response predictions. The focus of this dissertation is on the fusion of different types of information using Bayesian-Entropy method. The Maximum Entropy method in information theory introduces a unique way of handling information in the form of constraints. The Bayesian-Entropy (BE) principle is proposed to integrate the Bayes’ theorem and Maximum Entropy method to encode extra information. The posterior distribution in Bayesian-Entropy method has a Bayesian part to handle point observation data, and an Entropy part that encodes constraints, such as statistical moment information, range information and general function between variables. The proposed method is then extended to its network format as Bayesian Entropy Network (BEN), which serves as a generalized information fusion tool for diagnostics, prognostics, and surrogate modeling.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
ContributorsWang, Yuhao (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2020
Description
Almost all mechanical and electro-mechanical products are assemblies of multiple parts, either because of requirements for relative motion, or use of different materials, shape/size differences. Thus, assembly design is the very crux of engineering design. In addition to nominal design of an assembly, there is also tolerance design to determine allowable manufacturing variations to ensure proper functioning and assemblability. Most of the flexible assemblies are made by stamping sheet metal. Sheet metal stamping process involves plastically deforming sheet metals using dies. Sub-assemblies of two or more components are made with either spot-welding or riveting operations. Various sub-assemblies are finally joined, using spot-welds or rivets, to create the desired assembly. When two components are brought together for assembly, they do not align exactly; this causes gaps and irregularities in assemblies. As multiple parts are stacked, errors accumulate further. Stamping leads to variable deformations due to residual stresses and elastic recovery from plastic strain of metals; this is called as the ‘spring-back’ effect. When multiple components are stacked or assembled using spot welds, input parameters variations, such as sheet metal thickness, number and order of spot welds, cause variations in the exact shape of the final assembly in its free state. It is essential to understand the influence of these input parameters on the geometric variations of both the individual components and the assembly created using these components. Design of Experiment is used to generate principal effect study which evaluates the influence of input parameters on output parameters. The scope of this study is to quantify the geometric variations for a flexible assembly and evaluate their dependence on specific input variables. The 3 input variables considered are the thickness of the sheet material, the number of spot welds used and the spot-welding order to create the assembly. To quantify the geometric variations, sprung-back nodal points along lines, circular arcs, a combination of these, and a specific profile are reduced to metrologically simulated features.
ContributorsJoshi, Abhishek (Author) / Ren, Yi (Thesis advisor) / Davidson, Joseph (Committee member) / Shah, Jami (Committee member) / Arizona State University (Publisher)
Created2020
Description
Prior research has provided evidence to suggest that veterans exhibit unique assets that benefit them in engineering education and engineering industry. However, there is little evidence to determine whether their assets are due to military service or other demographic factors such as age, maturity, or gender. The aim of this study is to discover, better understand, and disseminate the unique assets that veterans gained through military service and continue to employ as engineering students or professional engineers. This strength-based thematic analysis investigated the semi-structured narrative interviews of 18 military veterans who are now engineering students or professionals in engineering industry. Using the Funds of Knowledge framework, veterans’ Funds of Knowledge were identified and analyzed for emergent themes. Participants exhibited 10 unique veterans’ Funds of Knowledge. Utilizing analytical memos, repeated reflection, and iterative analysis, two overarching themes emerged, Effective Teaming in Engineering and Adapting to Overcome Challenges. Additionally, a niche concept of Identity Crafting was explored using the unique narratives of two participants. This study provides empirical evidence of military veterans experientially learning valuable assets in engineering from their military service. A better understanding of the veterans’ Funds of Knowledge presented in this study provides valuable opportunities for their utilization in engineering education and engineering industry.
ContributorsSheppard, Michael Scott (Author) / Kellam, Nadia N (Thesis advisor) / Bekki, Jennifer M (Committee member) / Brunhaver, Samantha R (Committee member) / Arizona State University (Publisher)
Created2020
Description
Thermal Energy Storage (TES) is of great significance for many engineering applications as it allows surplus thermal energy to be stored and reused later, bridging the gap between requirement and energy use. Phase change materials (PCMs) are latent heat-based TES which have the ability to store and release heat through phase transition processes over a relatively narrow temperature range. PCMs have a wide range of operating temperatures and therefore can be used in various applications such as stand-alone heat storage in a renewable energy system, thermal storage in buildings, water heating systems, etc. In this dissertation, various PCMs are incorporated and investigated numerically and experimentally with different applications namely a thermochemical metal hydride (MH) storage system and thermal storage in buildings. In the second chapter, a new design consisting of an MH reactor encircled by a cylindrical sandwich bed packed with PCM is proposed. The role of the PCM is to store the heat released by the MH reactor during the hydrogenation process and reuse it later in the subsequent dehydrogenation process. In such a system, the exothermic and endothermic processes of the MH reactor can be utilized effectively by enhancing the thermal exchange between the MH reactor and the PCM bed. Similarly, in the third chapter, a novel design that integrates the MH reactor with cascaded PCM beds is proposed. In this design, two different types of PCMs with different melting temperatures and enthalpies are arranged in series to improve the heat transfer rate and consequently shorten the time duration of the hydrogenation and dehydrogenation processes. The performance of the new designs (in chapters 2 and 3) is investigated numerically and compared with the conventional designs in the literature. The results indicate that the new designs can significantly enhance the time duration of MH reaction (up to 87%). In the fourth chapter, organic coconut oil PCM (co-oil PCM) is explored experimentally and numerically for the first time as a thermal management tool in building applications. The results show that co-oil PCM can be a promising solution to improve the indoor thermal environment in semi-arid regions.
ContributorsAlqahtani, Talal (Author) / Phelan, Patrick E (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Mellouli, Sofiene (Committee member) / Wang, Robert (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2020
Description
A defining feature of many United States (U.S.) doctoral engineering programs is their large proportion of international students. Despite the large student body and the significant impacts that they bring to the U.S. education and economy, a scarcity of research on engineering doctoral students has taken into consideration the existence of international students and the consequential diversity in citizenship among all students. This study was designed to bridge the research gap to improve the understanding of sense of belonging from the perspective of international engineering doctoral students.
A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.
The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.
A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.
The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.
ContributorsLee, Eunsil (Author) / Bekki, Jennifer (Thesis advisor) / Carberry, Adam (Thesis advisor) / Kellam, Nadia (Committee member) / Arizona State University (Publisher)
Created2020
Description
Access to real-time situational information including the relative position and motion of surrounding objects is critical for safe and independent travel. Object or obstacle (OO) detection at a distance is primarily a task of the visual system due to the high resolution information the eyes are able to receive from afar. As a sensory organ in particular, the eyes have an unparalleled ability to adjust to varying degrees of light, color, and distance. Therefore, in the case of a non-visual traveler, someone who is blind or low vision, access to visual information is unattainable if it is positioned beyond the reach of the preferred mobility device or outside the path of travel. Although, the area of assistive technology in terms of electronic travel aids (ETA’s) has received considerable attention over the last two decades; surprisingly, the field has seen little work in the area focused on augmenting rather than replacing current non-visual travel techniques, methods, and tools. Consequently, this work describes the design of an intuitive tactile language and series of wearable tactile interfaces (the Haptic Chair, HaptWrap, and HapBack) to deliver real-time spatiotemporal data. The overall intuitiveness of the haptic mappings conveyed through the tactile interfaces are evaluated using a combination of absolute identification accuracy of a series of patterns and subjective feedback through post-experiment surveys. Two types of spatiotemporal representations are considered: static patterns representing object location at a single time instance, and dynamic patterns, added in the HaptWrap, which represent object movement over a time interval. Results support the viability of multi-dimensional haptics applied to the body to yield an intuitive understanding of dynamic interactions occurring around the navigator during travel. Lastly, it is important to point out that the guiding principle of this work centered on providing the navigator with spatial knowledge otherwise unattainable through current mobility techniques, methods, and tools, thus, providing the \emph{navigator} with the information necessary to make informed navigation decisions independently, at a distance.
ContributorsDuarte, Bryan Joiner (Author) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020
Description
Water desalination has become one of the viable solutions to provide drinking water in regions with limited natural resources. This is particularly true in small communities in arid regions, which suffer from low rainfall, declining surface water and increasing salinity of groundwater. Yet, current desalination methods are difficult to be implemented in these areas due to their centralized large-scale design. In addition, these methods require intensive maintenance, and sometimes do not operate in high salinity feedwater. Membrane distillation (MD) is one technology that can potentially overcome these challenges and has received increasing attention in the last 15 years. The driving force of MD is the difference in vapor pressure across a microporous hydrophobic membrane. Compared to conventional membrane-based technologies, MD can treat high concentration feedwater, does not need intensive pretreatment, and has better fouling resistance. More importantly, MD operates at low feed temperatures and so it can utilize low–grade heat sources such as solar energy for its operation. While the integration of solar energy and MD was conventionally indirect (i.e. by having two separate systems: a solar collector and an MD module), recent efforts were focused on direct integration where the membrane itself is integrated within a solar collector aiming to have a more compact, standalone design suitable for small-scale applications. In this dissertation, a comprehensive review of these efforts is discussed in Chapter 2. Two novel direct solar-powered MD systems were proposed and investigated experimentally: firstly, a direct contact MD (DCMD) system was designed by placing capillary membranes within an evacuated tube solar collector (ETC) (Chapter 3), and secondly, a submerged vacuum MD (S-VMD) system that uses circulation and aeration as agitation techniques was investigated (Chapter 4). A maximum water production per absorbing area of 0.96 kg·m–2·h–1 and a thermal efficiency of 0.51 were achieved. A final study was conducted to investigate the effect of ultrasound in an S-VMD unit (Chapter 5), which significantly enhanced the permeate flux (up to 24%) and reduced the specific energy consumption (up to 14%). The results add substantially to the understanding of integrating ultrasound with different MD processes.
ContributorsBamasag, Ahmad (Author) / Phelan, Patrick E (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Wang, Liping (Committee member) / Bocanegra, Luis (Committee member) / Roedel, Ronald (Committee member) / Arizona State University (Publisher)
Created2020
Description
Over the past decade, machine learning research has made great strides and significant impact in several fields. Its success is greatly attributed to the development of effective machine learning algorithms like deep neural networks (a.k.a. deep learning), availability of large-scale databases and access to specialized hardware like Graphic Processing Units. When designing and training machine learning systems, researchers often assume access to large quantities of data that capture different possible variations. Variations in the data is needed to incorporate desired invariance and robustness properties in the machine learning system, especially in the case of deep learning algorithms. However, it is very difficult to gather such data in a real-world setting. For example, in certain medical/healthcare applications, it is very challenging to have access to data from all possible scenarios or with the necessary amount of variations as required to train the system. Additionally, the over-parameterized and unconstrained nature of deep neural networks can cause them to be poorly trained and in many cases over-confident which, in turn, can hamper their reliability and generalizability. This dissertation is a compendium of my research efforts to address the above challenges. I propose building invariant feature representations by wedding concepts from topological data analysis and Riemannian geometry, that automatically incorporate the desired invariance properties for different computer vision applications. I discuss how deep learning can be used to address some of the common challenges faced when working with topological data analysis methods. I describe alternative learning strategies based on unsupervised learning and transfer learning to address issues like dataset shifts and limited training data. Finally, I discuss my preliminary work on applying simple orthogonal constraints on deep learning feature representations to help develop more reliable and better calibrated models.
ContributorsSom, Anirudh (Author) / Turaga, Pavan (Thesis advisor) / Krishnamurthi, Narayanan (Committee member) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2020
Description
The alternative project delivery methods (APDMs) today are being increasingly used by owner organizations in the architecture, engineering, and construction (AEC) industry. Yet the adoption of these methods can be extremely difficult to accomplish and requires significant change management efforts. To facilitate the APDM adoption, this research aimed to better understand how AEC owner organizations have changed from only using the design-bid-build method to also successfully implementing APDMs from an organizational change perspective. This research utilized a literature review, survey and interviews to fulfill the research objectives. The dissertation follows a three paper format. The first paper focuses on identifying organizational change management (OCM) practices that, when effectively executed, lead to increased success rates of adopting APDMs in owner AEC organizations. The results of the first paper indicated that the five OCM practices with the strongest correlations to successful APDM adoption were realistic timeframe, effective change agents, workload adjustments, senior-leadership commitment, and sufficient change-related training. The second paper focuses on investigating AEC employees’ reactions to the adoption of APDMs. The findings of the second paper revealed that employees in AEC organizations react favorably to adopting a change in their project delivery systems. The findings further revealed that increasing the use of OCM practices is related to decreased employee resistance to change. The third paper aimed to provide guidelines detailing on how to lead APDM adoption. The findings of the third paper indicated that there was a general sequence of four implementation phases, which were preparing and planning, pilot project testing, expanding to the intended scale, and sustaining and evaluating. The phases include specific OCM practices that increase the probability of successful APDM adoption. The dissertation results can help in guiding the senior managers of construction organizations and OCM consultants to effectively implement APDMs for the first time in the construction sector.
ContributorsAldossari, Khaled Medath (Author) / Sullivan, Kenneth T. (Thesis advisor) / Hurtado, Kristen C (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2020
Description
Deformable heat exchangers could provide a multitude of previously untapped advantages ranging from adaptable performance via macroscale, dynamic shape change (akin to dilation/constriction seen in blood vessels) to enhanced heat transfer at thermal interfaces through microscale, surface deformations. So far, making deformable, ‘soft heat exchangers’ (SHXs) has been limited by the low thermal conductivity of materials with suitable mechanical properties. The recent introduction of liquid-metal embedded elastomers by Bartlett et al1 has addressed this need. Specifically, by remaining soft and stretchable despite the addition of filler, these thermally conductive composites provide an ideal material for the new class of “soft thermal systems”, which is introduced in this work. Understanding such thermal systems will be a key element in enabling technology that require high levels of stretchability, such as thermoregulatory garments, soft electronics, wearable electronics, and high-powered robotics. Shape change inherent to SHX operation has the potential to violate many conventional assumptions used in HX design and thus requires the development of new theoretical approaches to predict performance. To create a basis for understanding these devices, this work highlights two sequential studies. First, the effects of transitioning to a surface deformable, SHX under steady state static conditions in the setting of a liquid cooling device for thermoregulation, electronics and robotics applications was explored. In this study, a thermomechanical model was built and validated to predict the thermal performance and a system wide analysis to optimize such devices was carried out. Second, from a more fundamental perspective, the effects of SHXs undergoing transient shape deformation during operation was explored. A phase shift phenomenon in cooling performance dependent on stretch rate, stretch extent and thermal diffusivity was discovered and explained. With the use of a time scale analysis, the extent of quasi-static assumption viability in modeling such systems was quantified and multiple shape modulation regime limits were defined. Finally, nuance considerations and future work of using liquid metal-silicone composites in SHXs were discussed.
ContributorsKotagama, Praveen (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Committee member) / Phelan, Patrick (Committee member) / Herrmann, Marcus (Committee member) / Green, Matthew (Committee member) / Arizona State University (Publisher)
Created2020