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- All Subjects: engineering
- Creators: Sullivan, Kenneth
- Creators: Phelan, Patrick
- Creators: Tamizhmani, Govindasamy
Description
Thermal management is a critical aspect of microelectronics packaging and often centers around preventing central processing units (CPUs) and graphics processing units (GPUs) from overheating. As the need for power going into these processors increases, so too does the need for more effective thermal management strategies. One such strategy is to utilize additive manufacturing to fabricate heat sinks with bio-inspired and cellular structures and is the focus of this thesis. In this study, a process was developed for manufacturing the copper alloy CuNi2SiCr on the 100w Concept Laser Mlab laser powder bed fusion 3D printer to obtain parts that were 94% dense, while dealing with challenges of low absorptivity in copper and its high potential for oxidation. The developed process was then used to manufacture and test heat sinks with traditional pin and fin designs to establish a baseline cooling effect, as determined from tests conducted on a substrate, CPU and heat spreader assembly. Two additional heat sinks were designed, the first of these being bio-inspired and the second incorporating Triply Periodic Minimal Surface (TPMS) cellular structures, with the aim of trying to improve the cooling effect relative to commercial heat sinks. The results showed that the pure copper commercial pin-design heat sink outperformed the additive manufactured (AM) pin-design heat sink under both natural and forced convection conditions due to its approximately tenfold higher thermal conductivity, but that the gap in performance could be bridged using the bio-inspired and Schwarz-P heat sink designs developed in this work and is an encouraging indicator that further improvements could be obtained with improved alloys, heat treatments and even more innovative designs.
ContributorsYaple, Jordan Marie (Author) / Bhate, Dhruv (Thesis advisor) / Azeredo, Bruno (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021
Description
The workforce demographics are changing as a large portion of the population is approaching retirement and thus leaving vacancies in the construction industry. Succession planning is an aspect of talent management which aims to mitigate instability faced by a company when a new successor fills a vacancy. Research shows that in addition to a diminishing pool of available talent, the industry does not have widespread, empirically tested and implemented models that lead to effective successions. The objective of this research was to create a baseline profile for succession planning in the construction industry by identifying currently implemented best practices. The author interviewed six companies of varying sizes and demographics within the construction industry and compared their succession planning methodologies to identify any common challenges and practices. Little consensus between the companies was found. The results of the interviews were then compared to current research literature, but even here, little consensus was found. In addition, companies lacked quantitative performance metrics demonstrating the effectiveness, or ineffectiveness, of their current succession planning methodologies. The authors recommended that additional research is carried out to focus on empirical evidence and measurement of industry practices surrounding talent identification, development, and transition leading to succession.
ContributorsGunnoe, Jake A (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Kashiwagi, Dean (Committee member) / Arizona State University (Publisher)
Created2015
Description
Nuclear power has recently experienced a resurgence in interest due to its ability to generate significant amounts of relatively clean energy. However, the overall size of nuclear power plants still poses a problem to future advancements. The bulkiness of components in the plant contribute to longer construction times, higher building and maintenance costs, and the isolation of nuclear plants from populated areas. The goal of this project was to analyze the thermal performance of nanocrystalline copper tantalum (NC Cu-Ta) inside the steam generator of a pressurized water reactor to see how much the size of these units could be reduced without affecting the amount of heat transferred through it. The analysis revealed that using this material, with its higher thermal conductivity than the traditional Inconel Alloy 600 that is typically used in steam generators, it is possible to reduce the height of a steam generator from 21 meters to about 18.6 meters, signifying a 11.6% reduction in height. This analysis also revealed a diminishing return that occurs with increasing the thermal conductivity on both reducing the required heat transfer area and increasing the overall heat transfer coefficient.
ContributorsRiese, Alexander (Author) / Phelan, Patrick (Thesis director) / Bocanegra, Luis (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
By the evolution of technologies and computing power, it is possible to capture and save large amounts of data and then find patterns in large and complex datasets using data science and machine learning. This dissertation introduces machine-learning models and econometric models to use in infrastructure transportation projects. Among transportation infrastructure projects, the airline industry and highways are selected to implement the models.The first topic of this dissertation focuses on using machine-learning models in highway projects. The International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) dataset for 1,390 roads and highways in the 50 states of the United States and the District of Columbia from 1989 to 2018. The results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination than other studied models. Also, the most important factors in predicting the IRI are identified.
The second topic of this dissertation aims to develop machine-learning models to predict customer dissatisfaction in the airline industry. The relationship between measures of service failure (flight delay and mishandled baggage) and customer dissatisfaction is predicted by using longitudinal data from 2003 to 2019 from the U.S. airline industry. Data was obtained from the Air Travel Consumer Report (ATCR) published by the U.S. Department of Transportation. Flight delay is more important in low-cost airlines, while mishandled baggage is more important in legacy airlines. Also, the effect of the train-test split ratio on each machine-learning model is examined by running each model using four train-test splits. Results indicate that the train-test split ratio could influence the selection of the best model.
The third topic in this dissertation uses econometric analysis to investigate the relationship between customer dissatisfaction and two measures of service failure in the U.S. airline industry. Results are: 1) Mishandled baggage has more impact than flight delay on customer complaints. 2) The effect of an airline’s service failures on customer complaints is contingent on the category of the airline. 3) The effect of flight delay on customer complaints is lower for low-cost airlines compared to legacy airlines.
ContributorsDamirchilo, Farshid (Author) / Fini, Elham H (Thesis advisor) / Lamanna, Anthony J (Committee member) / Parast, Mahour M (Committee member) / Sullivan, Kenneth (Committee member) / Arizona State University (Publisher)
Created2021
Description
The world faces significant environmental and social challenges due to high economic development, population growth, industrialization, rapid urbanization, and unsustainable consumption. Global communities are taking the necessary measures to confront these international challenges and applying sustainable development principles across all sectors. Construction is a critical driving instrument of economic activity, and to achieve sustainable development, it is vital to transform conventional construction into a more sustainable model. The research investigated sustainable construction perceptions in Kuwait, a rapidly growing country with a high volume of construction activities. Kuwait has ambitious plans to transition into a more sustainable economic development model, and the construction industry needs to align with these plans. This research aims to identify the characteristics of sustainable construction applications in the Kuwaiti construction market, such as awareness, current perceptions, drivers and barriers, and the construction regulations' impact. The research utilized a qualitative approach to answer research questions and deliver research objectives by conducting eleven Semi-structured interviews with experienced professionals in the Kuwaiti construction market to collect rich data that reflects insights and understandings of the Kuwaiti construction industry. The Thematic analysis of the data resulted in six themes and one sub-theme that presented reflections, insights, and perspectives on sustainable construction perceptions in the Kuwaiti construction market. The research findings reflected poor sustainable construction awareness and poor environmental and social application in the construction industry, the determinant role of construction regulations in promoting sustainable construction. and barriers and drivers to sustainable construction applications. The research concluded with answers to research questions, delivery of research objectives, and an explanation of sustainable construction perceptions in the Kuwaiti construction market.
Contributorsalsalem, mohammad salem (Author) / Duran, Melanie (Thesis advisor) / Chong, Oswald (Committee member) / Sullivan, Kenneth (Committee member) / Grau, David (Committee member) / Arizona State University (Publisher)
Created2023