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Phase Field Modelling Under the Rapid Solidification Process in Additive Manufacturing
No full textDespite various phase field models that have been developed to predict the microstructure under the rapid solidification process for additive manufacturing (AM), the capability of the phase field models to capture the physics in the non-equilibrium condition remains to be explored. The first part of the work compares three models: (i) Kim-Kim-Suzuki (KKS), (ii) partition coefficient relaxation (PCR), and (iii) finite interface dissipation (FID) under the highly non-equilibrium conditions. The microstructure results (i) primary dendrite arm spacing, (ii) partition coefficient, and (iii) transition velocity between cellular and planar growth predicted by three models are compared to evaluate the ability of the models to describe both the equilibrium and non-equilibrium states.
The second part of the work focuses on the effect of the processing parameter on the microstructure variability. A methodology for predicting a hybrid microstructure and defect processing map for laser powder bed fusion additive manufacturing since FID can describe both the equilibrium and strongly non-equilibrium conditions successfully. Based on the map, a region of the parameter space that leads to successful AM of defect-free and microstructures-homogeneous parts is then identified. This methodology could be generalized and applied to other alloy systems and has the potential to help alloy design.
The relationship between the melt pool microstructure distribution and the process parameter (laser power and laser speed) in AM was further investigated in the third part of the work. The process parameters are associated with two types of general microstructure distribution types in the second work: (i) homogenous, and (ii) heterogenous. On one hand, the homogenous microstructure is characterized by a planar microstructure throughout the melt pool. On the other hand, a
heterogeneous microstructure corresponds to conditions in which at least a portion of the melt pool presents dendritic solidification patterns. Heterogeneous microstructures are further categorized into three distinct conditions: (i) fully dendritic, (ii) top dendritic, and (iii) bottom dendritic. We further analyze the microstructure selection by investigating the relationship between the microstructure distribution and the corresponding local thermal histories (temperature gradient and the growth rate)
Economic Indicators of the College Station-Bryan MSA, May 2024
No full textThe Business-Cycle Index increased 0.3% from February 2024 to March 2024. The local unemployment rate for March 2024 was 3.1%, unchanged from February 2024. March��������s local nonfarm employment was essentially the same as in February 2024, and was up 0.8% from March 2023.Inflation-adjusted taxable sales were up by almost 1.2% from February 2024 to March 2024. Inflation-adjusted quarterly total wage payments were down by 1.6% in the third quarter of 2023 compared to the previous quarter. The median sales price for single family homes in Brazos County increased 8% from the previous year
The Ecological Effect of Dams on Downstream Ecosystems
Get PDFIn the United States, damming rivers has been common practice since colonization. Their installation allows for the manipulation and control of rivers and streams, preventing natural flooding and in some cases, allowing for the generation of power. When a river is contained by a dam, regardless of the size of the dam, the ecosystem that naturally surrounds the river is fragmented, which has consequences for the vegetation and wildlife of the area. In this study, we look two dams to gain a better understanding of the physical changes that ecosystems go through post dam installation. The first dam is Applegate Dam, built in 1980 in Jackson County, Oregon. The second dam is Galesville Dam, built in 1985 in Douglas County, Oregon. This study looks at NDVI and peak river discharge levels to see the abiotic and biotic impacts of the dam installation. Data was gathered in five-year intervals, from 1970 to 2020. We found that in the case of Applegate dam, vegetation cover and river discharge were both changed after the installation of the dam. In the case of Galesville dam, vegetation cover and river discharge were changed but that change cannot be statistically attributed to the installation of the Galesville dam. Although the results from the Applegate dam are supported by other studies, it would take further research to determine the impact that these dams had on their surrounding ecosystems
