In an ideal world, the electrical grid could fully decarbonize with just solar and wind as forms of energy generation. However, more traditional and firm forms of generation, such as natural gas or nuclear power, support the stability of the electric grid and lower the cost of transition to a net-zero carbon grid. To properly integrate emerging technologies, such as carbon capture and sequestration (CCS), as sources of firm generation, there needs to be an understanding of their economic behavior, and larger impact on other forms of electrical generation. CCS is of interest because the natural gas industry supplies the cheapest electricity and plays a major role in planning the energy transition. This project seeks to understand the economic viability of CCS to inform policy encouraging the deployment of emerging electricity resources. How do the investment costs associated with CCS need to change to result in significant buildout of natural gas plants with CCS? How does the increase of CCS buildout impact other forms of generation within a given system, and contribute to the overarching goal of creating a sustainable energy future? Using the MIT Energy Initiative's capacity-expansion model GenX, the investment costs associated with adding new natural gas plants with CCS and with retrofitting existing plants with CCS are varied in a sweep and the impact on the amount of added capacity of CCS and other forms of generation is analyzed. It has been found that an 80% reduction of the investment costs associated with CCS begins to promote the buildout of new CCS plants. Further investigation on the impact to other power sources, particularly battery storage, will provide insight into the impact of expanding CCS capacity on the rest of the system, with the anticipated result that increasing CCS buildout discourages the buildout of already established power sources.
 

Accurately modeling atmospheric re-entry has become incredibly important with the advent of reusable spacecraft. Computational Fluid Dynamic (CFD) employs solvers, a combination of mathematical models, to attempt to replicate real-world physical characteristics, such as when a spacecraft is re-entering the atmosphere. This research attempts to validate the OpenFOAM hy2Foam solver–which was created to model the environment of atmospheric re-entry–by comparing CFD results to real-world wind tunnel data of the hypervelocity ballistic model 1 (HB-1) at mach 5.1. We show with 99% confidence that the CFD simulations do not produce numerically accurate results when compared to historical wind tunnel data at seven varying angles of attack: -1, 0, 2, 4, 6, 8, 10, and 12 degrees. For all angles of attack, the forebody axial-force coefficient disagrees with historical wind tunnel testing, being 2.38 times less on average. Additionally, for all but the -1 and 0 degree angle of attack, the pitching-moment coefficient disagrees with the historical data, being 52.6 times less on average. Additional research conducted on the HB-2 model has found similar disagreement of aerodynamic results demonstrating a need for additional research to ensure the solver produces numerically accurate results. Accurate solvers are vital to ensure that CFD simulations accurately model real-world conditions, such as during spacecraft re-entry when safety of astronauts could be at stake if a spacecraft is designed based on invalid data. 

Data show the one-sided diverging diamond interchange (OSDDI), which I developed, can substantially outperform both the conventional diamond interchange (CDI) and diverging diamond interchange (DDI) in traffic operations. The OSDDI, CDI, and DDI are all forms of the diamond interchange—the most common type of freeway-to-arterial link in the United States. The CDI and DDI are widely used in the United States. While my preliminary analyses give promise that the OSDDI is not less safe than the CDI, its overall safety performance has yet to be comprehensively explored. According to the Federal Highway Administration (FHWA), over half of all injury or fatal crashes occur at an intersection. This statistic makes it important to analyze the safety performance of the OSDDI in greater detail. I fill this research gap in this study by analyzing the vehicular and pedestrian safety performance of the OSDDI relative to the CDI and DDI. I use Verkehr In Städten—SIMulationsmodell (VISSIM), a microscopic traffic simulation software, to simulate how each design performs in multiple traffic demand scenarios. I then use the Surrogate Safety Assessment Model (SSAM), developed by FHWA, to predict the safety performance of each design using trajectory data from VISSIM and traffic conflict analysis. Finally, I conduct statistical tests to find if the OSDDI is statistically significantly safer than the CDI and DDI. I expect the OSDDI to be statistically significantly safer than the CDI and comparable to the DDI for both vehicles and pedestrians. The results of this study may encourage transportation agencies to consider the OSDDI as an alternative diamond interchange design to improve safety and mobility for people walking, biking, and driving.

Quasars are the most luminous type of Active Galactic Nuclei. Both consist of a supermassive blackhole at the center with a surrounding luminous accretion disk. Quasars can expel clouds of gas outwards, which are called outflows. Historically quasar outflows have been detected by the presence of absorption lines in quasar spectra, with the most typical ion detected being CIV (Carbon ionized 3 times, or C3+). Some of these outflows reach relativistic speeds beyond 10% of the speed of light. Our team has built the largest database of these outflows, called extremely high velocity outflows. All outflows have been found to be variable between observations, but the exact cause of this variability is unknown. The most prominent theories are variability due to motion in and out of our line-of-sight, and the recombination/ionization of the outflow gas. While carrying out our study of quasar outflow variability, we found that several cases have a common minimum flux value but vary almost everywhere else within their absorption trough; we call this ‘strange’ variability. Its importance is that it can help us constrain the cause of variability since it sets restrictions on several absorption parameters: coverage fraction, how much the source of light is covered, and the optical depth. With this data, theoretical astrophysicists can further narrow down the causes of quasar outflows. We will present (1) the results of our analysis of how the observational data helps constrain these parameters in the current sample, including cases with multiple observational epochs, and (2) our search for more of cases of strange variability in a larger sample of quasar spectra in the Sloan Digital Sky Survey. We have developed automated tools in Python to aid our search, which we plan to present as well as the simulation process used to develop them.