This project evaluates the flight mechanics of a class of Autonomous Underwater Vehicles (AUVs) known as gliders using digital simulation via Python code. When used in the real world, these gliders perform vertical profiling of important marine quantities like temperature and salinity. These are then used by oceanographers and others to help them gain a deeper understanding of the ocean environment. The overall goal of the project is to optimize the trajectories of a fleet of vehicles to minimize energy consumption while maximizing uniformity of ocean coverage. Using specific engineering data on real world glider flight as well as public domain ocean environmental models, I have coded a custom Python application with guidance from my mentor at the Applied Physics Lab. We chose a computer simulation of glider flight so that multiple variables could be easily manipulated without the risk of losing a valuable glider if certain parameters are not favorable. This simulation produces data that describes the vehicles’ locations and energy states over time. The software is structured such that important parameters are specified in an easily-modified configuration file. The parameters I alter include geographic area, the number of gliders, the maximum flight depth, the vehicle’s available buoyancy range, and the glide angle. Then, using the data that the simulation produces, I analyze variations in energy consumption, uniformity of coverage, and the time required for each glider to reach their destination. The oceans, which cover about 70% of the planet’s surface, have a huge impact on the climate and health of the Earth as a whole. The result of this analysis is useful to real-world AUV operations by helping determine how to program them to fly more efficiently and maximize their utility as scientific instruments.

As computing continues to evolve, there are few fields with more extensive and revolutionary prospects than quantum computing. Advanced quantum technologies have the potential to see into a world that classical computing cannot, enabling more advanced encryption methods, precision atomic interaction modeling, and molecular simulations for pharmaceutical drug research. Students should be exposed to modern quantum technologies, but providing students with hands-on experience is challenging at the undergraduate level. Our project aims to remove that barrier. Ion traps are the fundamental mechanisms for information storage in trapped ion quantum computers, so we designed and built a scaled-up version of one of these traps for use in a classroom setting. Our trap, a linear quadrupole trap, is based around 4 conductive electrodes that utilize alternating current (AC) to confine charged particles to a linear trapping axis, bounded on either end by 2 direct current (DC) rods. The trap features a 3-D printed polylactic acid (PLA) base and lid with a locking mechanism to prevent undesired air movement within the trapping region. We implemented a high-voltage lower DC plate in combination with a grounded upper plate to emulate an infinite parallel plate capacitor when the distance between the two is minimized and the plate area is maximized, allowing for additional vertical manipulation of the particles. To guarantee student safety, all high-voltage components remain covered while trapping, and each conductive element has undergone distance and breakdown voltage calculations to ensure that no electrical arcing can occur. As a result, undergraduate students in the lab are able to manipulate different aspects of the electric field geometry to observe micromotion, Coulomb Crystals, secular frequencies, and determine the charge-to-mass ratios of different charged particles such as lycopodium moss spores (25µm) or polyethylene microspheres (50µm).

Trapped ion quantum computing (TIQC), with its large decoherence times and small operation times relative to other physical quantum computing architectures, has garnered significant attention in the public and private sectors. Planar Paul traps, which simultaneously utilize radio frequency and static voltages in a two-dimensional electrode array to spatially confine ions, are the primary candidates for trapping ions for TIQC due to their manufacturability and ability to shuttle ions between multiple trapping zones for quantum logic gates and memory storage. The growing relevance of this technology necessitates educating students about the advanced electrodynamics of ion trapping and ion shuttling. Therefore, I developed a macroscopic planar Paul trap which utilizes 50µm diameter proxy-ions along with high voltage (HV) alternating currents (AC) at 60 Hz and HV direct currents (DC). These are applied to a 5-rail electrode geometry to demonstrate ion shuttling and ion-group splitting along a linear trapping axis. The goal is to educate students on the electrodynamics of ion traps by allowing them to experiment with the tunable trapping parameters, such as AC voltage amplitudes, DC voltage magnitudes, and applied shuttling waveforms and observe the changes in the dynamics of the proxy-ions relative to theoretical predictions. I designed the trap by implementing the recommended relative electrode dimensions into COMSOL Multiphysics and optimizing the geometry by maximizing the pseudopotential confinement while simultaneously minimizing electrode surface area. Afterwards, I utilized an analytic model of a 5-rail planar Paul trap, along with the method of Lagrange multipliers, to optimize the voltage magnitude and waveform of the segmented electrodes for smooth, effective shuttling and ion-group splitting. I then integrated an HV relay circuit and the 5-rail electrode geometry onto printed circuit boards to allow for student-controlled ion shuttling via an Arduino microcontroller.