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- Genre: Doctoral Dissertation
Description
Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
ContributorsSong, Yang (Author) / Gurney, Kevin (Thesis advisor) / Kuby, Michael (Committee member) / Golub, Aaron (Committee member) / Chester, Mikhail (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2018
Description
Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.
ContributorsAhsan, Faiyaz (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Kwon, Beomjin (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021