While many 3D printed structures are rigid and stationary, the potential for complex geometries offers a chance for creative and useful motion. Printing structures larger than the print bed, reducing the need for support materials, maintaining multiple states without actuation, and mimicking origami folding are some of the opportunities offered by 3D printed hinges. Current efforts frequently employ advanced materials and equipment that are not available to all users. The purpose of this project was to develop a parametric, print-in-place, self-locking hinge that could be printed using very basic materials and equipment. Six main designs were developed, printed, and tested for their strength in maintaining a locked position. Two general design types were used: 1) sliding hinges and 2) removable pin hinges. The test results were analyzed to identify and explain the causes of observed trends. The amount of interference between the pin vertex and knuckle hole edge was identified as the main factor in hinge strength. After initial testing, the designs were modified and applied to several structures, with successful results for a collapsible hexagon and a folding table. While the initial goal was to have one CAD model as a final product, the need to evaluate tradeoffs depending on the exact application made this impossible. Instead, a set of design guidelines was created to help users make strategic decisions and create their own design. Future work could explore additional scaling effects, printing factors, or other design types.

Increasing reliable produce farming and clean energy generation in the southwestern United States will be important for increasing the food supply for a growing population and reducing reliance on fossil fuels to generate energy. Combining greenhouses with photovoltaic (PV) films can allow both food and electric power to be produced simultaneously. This study tests if the combination of semi-transparent PV films and a transmission control layer can generate energy and spectrally control the transmission of light into a greenhouse. Testing the layer combinations in a variety of real-world conditions, it was shown that light can be spectrally controlled in a greenhouse. The transmission was overall able to be controlled by an average of 11.8% across the spectrum of sunlight, with each semi-transparent PV film able to spectrally select transmission of light in both the visible and near-infrared light wavelength. The combination of layers was also able to generate energy at an average efficiency of 8.71% across all panels and testing conditions. The most efficient PV film was the blue dyed, at 9.12%. This study also suggests additional improvements for this project, including the removal of the red PV film due to inefficiencies in spectral selection and additional tests with new materials to optimize plant growth and energy generation in a variety of light conditions.

Oscillatory perturbations with varying amplitudes and frequencies have been found to significantly affect human standing balance. However, previous studies have only applied perturbation in either the anterior-posterior (AP) or the medio-lateral (ML) directions. Little is currently known about the impacts of 2D oscillatory perturbations on postural stability, which are more commonly seen in daily life (i.e., while traveling on trains, ships, etc.). This study investigated the effects of applying 2D perturbations vs 1D perturbations on standing stability, and how increasing the frequency and amplitude of perturbation impacts postural stability. A dual-axis robotic platform was utilized to simulate various oscillatory perturbations and evaluate standing postural stability. Fifteen young healthy subjects were recruited to perform quiet stance on the platform. Impacts of perturbation direction (i.e., 1D versus 2D), amplitude, and frequency on postural stability were investigated by analyzing different stability measures, specifically AP/ML/2D Center-of-Pressure (COP) path length, AP/ML/2D Time-to-Boundary (TtB), and sway area. Standing postural stability was compromised more by 2D perturbations than 1D perturbations, evidenced by a significant increase in COP path length and sway area and decrease in TtB. Further, the stability decreased as 2D perturbation amplitude and frequency increased. A significant increase in COP path length and decrease in TtB were consistently observed as the 2D perturbation amplitude and frequency increased. However, sway area showed a considerable increase only with increasing perturbation amplitude but not with increasing frequency.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

Stardust grains can provide useful information about the Solar System environment before the Sun was born. Stardust grains show distinct isotopic compositions that indicate their origins, like the atmospheres of red giant stars, asymptotic giant branch stars, and supernovae (e.g., Bose et al. 2010). It has been argued that some stardust grains likely condensed in classical nova outbursts (e.g., Amari et al. 2001). These nova candidate grains contain 13C, 15N and 17O-rich nuclides which are produced by proton burning. However, these nuclides alone cannot constrain the stellar source of nova candidate grains. Nova ejecta is rich in 7Be that decays to 7Li (which has a half-life of ~53 days). I want to measure 6,7Li isotopes in nova candidate grains using the NanoSIMS 50L (nanoscale secondary ion mass spectrometry) to establish their nova origins without ambiguity. Several stardust grains that are nova candidate grains were identified in meteorite Acfer 094 on the basis of their oxygen isotopes. The identified silicate and oxide stardust grains are <500 nm in size and exist in the meteorite surrounded by meteoritic silicates. Therefore, 6,7Li isotopic measurements on these grains are hindered because of the large 300-500 nm oxygen ion beam in the NanoSIMS. I devised a methodology to isolate stardust grains by performing Focused Ion Beam milling with the FIB – Nova 200 NanoLab (FEI) instrument. We proved that the current FIB instrument cannot be used to prepare stardust grains smaller than 1 𝜇m due to lacking capabilities of the FIB. For future analyses, we could either use the same milling technique with the new and improved FIB – Helios 5 UX or use the recently constructed duoplasmatron on the NanoSIMS that can achieve a size of ~75 nm oxygen ion beam.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Debris disks are a collection of dust grains and planetesimals around a star and are thought to contain the remnants of planet formation. Directly imaging debris disks and studying their morphologies is valuable for studying the planet formation process. In some stellar systems that have a directly imaged debris disk, there are also directly imaged planets. Debris disk structures like gaps and asymmetries can show the gravitational e↵ects of planets that are below the brightness threshold for being detected via direct imaging. We investigate a sample of debris disks in Scorpius-Centaurus (Sco-Cen) that were imaged with the Gemini Planet Imager (GPI), which is an adaptive optics system with a coronagraph to block starlight. We look at two GPI data sets, the GPIES campaign Sco-Cen targets, and a follow-up observing program for Sco-Cen targets. We resolve 5 debris disks in the follow-up program and 13 from the GPIES campaign. By calculating contrast curves, we determine the planet detection limit in each of the GPI images. We find that we could have detected 5 Jupiter mass planets at angular separations greater than about 0.6 arcseconds in our GPIES campaign images. In three of our images we could have detected 2 Jupiter mass planets in wide orbits, but 2 Jupiter masses below the detection limit in our other images. We identify one point source around HD 108904 as a sub-stellar companion candidate. To further check for evidence of planets that are below the detection limit, we measure the surface brightness profile of the disks to check for asymmetries in brightness. We find that one of the edge-on disks has an asymmetric surface brightness profile, HD 106906, and three other edge-on disks have symmetric surface brightness profiles. We also find that two disks, HD 106906 and HD 111520, are asymmetric in radial extent, which is possibly evidence for gravitational interactions with planets.

This thesis explores the potential for software to act as an educational experience for engineers who are learning system dynamics and controls. The specific focus is a spring-mass-damper system. First, a brief introduction of the spring-mass-damper system is given, followed by a review of the background and prior work concerning this topic. Then, the methodology and main approaches of the system are explained, as well as a more technical overview of the program. Lastly, a conclusion and discussion of potential future work is covered. The project was found to be useful by several engineers who tested it. While there is still plenty of functionality to add, it is a promising first attempt at teaching engineers through software development.

Papago Park in Tempe, Arizona (USA) is host to several buttes composed of landslide breccias. The focus of this thesis is a butte called “Contact Hill,” which is composed of metarhyolitic debris flows, granitic debris flows, and Barnes Butte Breccia. The Barnes Butte Breccia can be broken down into several different compositional categories that can be dated based on their relative ages. The depositional timeline of these rocks is explored through their mineral and physical properties. The rhyolitic debris flow is massively bedded and dips at 26° to the southeast. The granitic debris flow is not bedded and exhibits a mixture of granite clasts of different grain sizes. In thin section analysis, five mineral types were identified: opaque inclusions, white quartz, anhedral and subhedral biotite, yellow stained K-feldspar, and gray plagioclase. It is hypothesized that regional stretching and compression of the crust, accompanied with magmatism, helped bring the metarhyolite and granite to the surface. Domino-like fault blocks caused large brecciation, and collapse of a nearby quartzite and granite mountain helped create the Barnes Butte Breccia: a combination of quartzite, metarhyolite, and granite clasts. Evidence of Papago Park’s ancient terrestrial history is seen in metarhyolite clasts containing sand grains. These geologic events, in addition to erosion, are responsible for Papago Park’s unique appearance today.