The Off-Grid Power Module (OGPM) is a solar panel system designed to power TransAstra’s Sutter Turnkey Observatory (TKO). The module would allow the operation of TKO without being tied to the electrical grid, dramatically expanding the sites that the system can be installed in. The main objectives for the design were for the module to collect and store enough power to operate three TKO telescope modules 95% of the time, for all the components to fit in a standard 20 ft shipping container, and for the system to be able to be feasibly assembled and installed. The resulting design of the system consists of 65 solar panels, 22 lithium batteries, 2 hybrid solar inverters, and a solar array mounting kit. By comparing the theoretical amount of power produced and required to operate the system, and by simulating the probability that the system is able to operate based on weather patterns throughout a calendar year, the selected number of solar panels and batteries in the system were confirmed to meet requirements. The design outlined in this report addresses all design objectives and outlines avenues through which the OGPM could be further developed.

Technology is ever-evolving, and its usefulness in sports training applications has increased with each evolution. Coaches and teams turn to technology to analyze player movements, break down game footage, and improve athletic performance. The sport of ice hockey has seen rapid player development over its history, with the game becoming faster, player equipment constantly improving, and the sport more popular and accessible than ever [4]. However, despite the abundance of training technology available for ice hockey players, one position still lags behind in development: the goalie. Ice hockey goalies often develop at a slower pace than their skater teammates due to a lack of expert coaching and resources at the lower levels, as well as the specialized equipment required, which makes the position more expensive and creates barriers to entry [19]. Additionally, goalies see the most growth in their playing ability when they get meaningful ice and game time. This thesis explores various designs of automated ball pitching and launching machines, as well as previous attempts to develop hockey puck shooting machines for goalie training. It also investigates why these earlier designs failed to gain widespread adoption within the goalie training community. Drawing on these insights and market research conducted for this project, the goal of this senior thesis is to design and build a functional puck launcher that is portable, versatile in capability, and can operate both on and off the ice, while being easily transported between different training locations.

The project aims to integrate solid-state cooling via Peltier devices into a wearable device for use during hypertrophic exercise sets lasting 30 to 60 seconds. The device aims to prompt the nervous system to send signals to the brain to perceive a cooling sensation, thereby improving workout performance in suboptimal gym environments. Through experiments with various Peltier modules, heat sinks, fabrics, and supply voltages in a lab setting, the TEC1-12706 module, paired with an aluminum pin-fin heat sink featuring an integrated fan, polyester fabric, and a supply voltage of approximately 3.0V, provided the greatest cooling power. Quantitative evaluation using recorded temperature data showed that this configuration lowered skin temperature by 6°C, while analysis of the Coefficient of Performance and Fourier’s Law confirmed its optimal performance. According to qualitative feedback from the author, this configuration proved the most effective. The final design was incorporated into a wearable prototype made from thin polyester fabric, Velcro, and rechargeable portable power supplies, and tested on both the author and an athletic test subject. While both reported a cooling sensation, it was not as strong as in the earlier experiments, likely due to a pocket of air forming between the device and the skin. During the participant’s test, a modification was made to reduce this air gap, resulting in a significantly stronger cooling effect. Future iterations should focus on eliminating this air pocket to reduce insulation and enhance cooling efficiency. Future work should consider whether dispersing a moderate temperature difference across a larger skin area or concentrating a higher temperature difference on a smaller skin area leads to greater perceived cooling, carrying important implications for cost-effectiveness and efficient product design.

The primary objective of this senior thesis is to continue the development of the AgIRoM, focusing on the integration of various planner methods to demonstrate the capabilities of the platform in a live navigation example. AgIRoM is a vision-based quadrotor platform that largely extends upon the work conducted by the Robotics and Perception Group (RPG) at the University of Zurich (UZH) on Agilicious through the addition of a depth-based motion planning pipeline. In particular, the development of AgIRoM was the main focus of my work during the past two years in the Intelligent Robot Motion Lab (IRoM). This report aims to discuss the process of successfully integrating two novel planner methods: the first is a method described in Perceive with Confidence (PwC) developed by researchers in IRoM, and the second is Ego-Planner (and its successor, Ego-Planner Swarm), a lightweight gradient-based planner developed specifically for quadrotors. The project was able to reach the live-testing phase with Perception Guarantees (the name of the GitHub repository of PwC, these terms will be used interchangeably) with some initial success but was unable to conduct full extensive testing due to the deprecation of AgIRoM's state estimation systems (discontinued end-of-life support for the visual-inertial odometry camera, and calibration deterioration for the motion capture system). Consequently, testing for the integration of Ego-Planner was done fully in simulation. During the integration and testing phase, it was found that a majority of the challenges arose from incompatibilities in hardware and their respective proprietary software packages. In an effort to address this, a Zed Mini camera - which has both state estimation tracking and depth estimation capabilities - was tested as a substitute for both cameras onboard the Agilicious framework.

Methanol is an important platform chemical in the chemical, fuel, and polymer industries, which, conventionally, requires energy intensive conditions to produce. Methane partial oxidation to methanol by hydrogen peroxide offers a sustainable alternative which, depending on catalyst design, may occur at milder conditions and enable methanol production from methane at remote locations. This study investigated palladium nanoparticles supported on and encapsulation in MFI as catalysts for hydrogen peroxide decomposition and methane partial oxidation to methanol. Pd-proximal H+ sites were found to decrease rates of hydrogen peroxide decomposition by a factor of 23 for nanoparticles supported in H-MFI compared to amorphous silica. Pd sites were found to be inactive towards MPO by hydrogen peroxide to liquid products at the conditions studied but Pd-free MFI catalysts showed appreciable liquid product formation, with yields and selectivities varying based on cations present in the support. These results demonstrated the promise of bifunctional H- and Na-MFI supported or encapsulated palladium nanoparticles for hydrogen peroxide synthesis and methane partial oxidation to methanol by hydrogen peroxide.

Space weather (SWx), the complex set of conditions between the Sun and the Earth, is difficult to predict. However, accurate forecasting of the conditions in the interplanetary medium is essential due to the dangers that solar storms pose to technology on the Earth's surface and in the atmosphere. One way to improve forecasting is with data assimilation (DA), a technique that integrates downstream observations into estimates of the solar wind near the Sun. In this thesis, the efficacy of an iterative Ensemble Kalman Smoother (iEnKS) coupled with a reduced-dimension propagation model (HUXt) is investigated. Prior work has been done to assimilate observations from two satellites— STEREO-A and STEREO-B—into this DA algorithm. However, STEREO-B no longer provides operational data. The iEnKS algorithm has thus been tweaked to assimilate observations from a tertiary source — the Advanced Composition Explorer (ACE). An experiment was designed to test the performance of the iEnKS with and without STEREO-B observations over the year of 2012, chosen due to the interception of a large coronal mass ejection (CME) by STEREO-A near the midpoint of the temporal window. iEnKS performance was compared with the performance of a Variational DA technique developed a few years prior.

It was found that when STEREO-B observations were removed from the iEnKS, root-mean squared error (RMSE) at each of the three satellites over the forecast period increased between 3-4%. This consistency was not observed with the Variational DA method in the same circumstances. Additionally, even after the removal of STEREO-B observations, the iEnKS performed more accurately than the Variational method in either case. This points to the effectiveness of the iEnKS as well as its response to changes in observation sources. Further research is needed to determine if error could further decrease with full-dimensional model coupling or the further optimization of the cost-function solving algorithm present in the iEnKS. However, these results are promising in terms of the operational implementation of DA for SWx applications.

Achieving multi-medium locomotion, especially between water and air, has been difficult for engineers. However, it presents no challenge to nature’s flying fish, who can easily traverse both the sea and the air utilizing the same anatomical structure. Better understanding flying fish anatomy could help unlock hydro-aero locomotion; unfortunately, studying flying fish has proven to be very difficult. They are too fast in water, they cannot be held captive, and their material properties and structure significantly change post-mortem. Princeton’s Bioinspired Adaptive Morphology (BAM) Lab aims to solve the mystery of how and why flying fish fly by studying their structure through an engineering approach: a bioinspired robotic model organism (RMO). A component of biological interest in the flying fish is its asymmetric caudal fin, which is common across all species of flying fish and is not present in many other types. The BAM Lab has studied the effect of the asymmetric caudal fin of the RMO for swimming, which has revealed that an asymmetric caudal fin produces higher thrust and lift than a symmetric one does. One goal of this thesis is to examine the effect of the caudal fin shape in taxiing. To make this possible, this thesis focuses on advancing the RMO to give it the ability to taxi. The design changes to the RMO focused on reducing the overall weight to lower the thrust requirements to exit the water. Due to the lack of stability in free swimming, a new experimental setup was also designed that prescribed the trajectory to a 30 degree exit angle. Using this rig, the design changes proved to be very successful as the new RMO was able to reach taxiing height at a wide range of flapping frequencies, whereas in relative comparison, the original RMO was only barely able to reach taxiing height at very high frequencies. Additionally, further force and moment characterization tests were performed with the new RMO at a 30 degree angle at a submerged and taxiing height with the different fins. The results support previous findings that the asymmetric tail provides higher thrust and lift and likely contributes to the flying fish’s incredible ability to navigate both mediums.

This thesis focuses on the design of a two-element rear wing with a driver-actuated drag reduction system (DRS) for an electric Formula Student racecar. A physical design space is first defined around Princeton Racing Electric’s MK3 car and brief vehicle dynamics calculations are done to establish a minimum downforce requirement for tested conditions in the Formula Hybrid Electric competition. High-lift, low Reynold’s number airfoils are modified to be compliant with the Formula Hybrid competition rules and analyzed initially in Xfoil. This is followed by two- and three-dimensional CFD analyses using Fidelity Pointwise for meshing and ANSYS Fluent for flow simulation and analysis. These simulations inform the optimal geometric configuration of the two wing elements and endplates to maximize the downforce/drag ratio under the analyzed conditions. The DRS design is informed by further CFD analysis and is implemented as an electromechanical system capable of being switched between high downforce and low drag configurations where needed to achieve the fastest lap times. The final rear wing design is capable of producing 128.5 N of downforce, 96% above the calculated requirement, while reducing drag by 83% when the DRS is activated. The thesis presents a finalized wing geometry with measurements, highlights and discusses general aerodynamic trends, and presents opportunities for further study and improvement on the design.

Decarbonizing Brazil is crucial for reducing global greenhouse gas emissions. To that end, the Net-Zero Brazil (NZB) modeling study aims to provide viable pathways for the country to achieve net-zero emissions by 2050. The modeling will be done with unprecedented spatial, technological, and temporal resolution. It relies on a least-cost, multi-sector optimization model being developed by the Princeton ZERO Lab called MACRO. Given Brazil’s prominence in biofuels and land-use challenges, a strategic approach to bioenergy deployment is essential. My study presents a high-resolution bioenergy supply chain optimization model, Downscale, designed to integrate into NZB to determine cost-effective bioenergy production, processing, and distribution pathways at fine spatial, temporal, and technological resolutions. A key feature is its downscaling capability, which enhances MACRO by translating state-level energy system results into actionable strategies for local deployment. Downscale is a mixed-integer linear programming model that optimally locates bioenergy crops, conversion facilities, and transportation while incorporating economic, environmental, and land-use constraints within municipalities. It is a myopic optimization model with no look-ahead, called at every time step of MACRO optimization. Cost-supply data for biomass resources and techno-economic characteristics of a portfolio of conversion technologies were gathered at the municipality-level (5570 municipalities in Brazil) for future use in MACRO via state-level aggregation. Downscale was then tested for Mato Grosso do Sul, a key biofuel-producing state. Four scenarios were analyzed: uniform demand growth for bio-derived energy carriers, low environmental protection (allowing bioenergy crop production in the Pantanal region), modest electrification of energy demands, and high electrification of demands. Results indicate that strategic infrastructure expansion can meet rising bioenergy demands while minimizing costs and environmental impacts, while also highlighting trade-offs in land-use decisions and resource allocation. This model provides actionable insights for policymakers and investors while serving as both an enhancement to MACRO and NZB and a standalone tool for downscaling optimization problems.

NASA has selected Ice Giants exploration as the priority flagship mission of the next decade, with an orbiter and atmospheric probe being identified as the primary architecture. This design report proposes a Low-Cost Uranus Magnetosphere Observing Satellite (LUMOS) architecture to supplement the planned Uranus Orbiter and Probe (UOP) mission, leveraging unused launch vehicle capabilities in the current UOP design. The LUMOS microsat will pursue magnetosphere mapping and ring imaging objectives in parallel with the primary orbiter’s tour of the Uranian moons, improving the science return of the overall mission by increasing spatial and temporal coverage of the magnetosphere and rings and pursuing higher-risk science that is prohibitive for the primary orbiter. The feasibility of such an architecture is demonstrated with a low-fidelity trajectory design for the microsat, high-level design of the science payload and all key spacecraft subsystems, and a mission cost assessment. Each element of the mission design is presented with requirements definition, design approach, trade studies, key analysis, and verification and validation. The trajectory design closes with high coverage for mapping magnetic longitudes and latitudes and imaging ring longitudes. The spacecraft design closes within constraints, with a total mass footprint of 290 kg (microsat wet mass of 156 kg plus 134 kg of additional orbiter fuel for interplanetary cruise and insertion), maximum power draw of 345 W, and total launch volume envelope of 0.87 x 0.69 x 1.16 m3, for a total volume of 0.7 m3. The total cost of the mission is estimated at $180M (FY$25). We find that LUMOS is a feasible mission concept that can significantly improve the science return of of the UOP Mission. Future work for this design concept will involve transitioning designs into high-fidelity models and analysis, optimizing the mission for science return and fuel consumption, and fully integrating with the UOP design.

While electrification has transformed automotive transportation, the marine industry—particularly in high-power recreational and commercial applications—continues to lag behind. Gasoline-powered outboards remain the standard, despite their high emissions, noise, and maintenance demands. This thesis presents the design, fabrication, and validation of a high-performance, low-voltage electric marine outboard system tailored to fill this market and environmental gap. The system can deliver 20 kW of continuous shaft power at a nominal voltage of 55 V, integrating a dual-motor drivetrain, liquid-cooled inverters, and a modular 6.5 kWh battery pack within a corrosion-resistant, water-tight frame. The design also includes a long-range wireless control interface with video feedback and autonomous steering capabilities, laying the groundwork for unmanned and fully autonomous operation. Finite element analysis and physical testing verified the system’s mechanical stiffness, electrical performance, and thermal reliability. Compared to commercial offerings, the final system provides superior power-to-weight ratio and significantly reduced installation complexity. This work demonstrates the feasibility of compact, modular electric propulsion for a broad range of marine applications and serves as a platform for future development in scalable and autonomous electric boating.

Pressure-gain combustion presents an exciting potential to increase engine efficiency over conventional engine cycles. The rotating detonation engine (RDE) is an engine concept which utilizes detonation to generate a pressure-gain combustion cycle. RDEs have a cylindrical geometry where a detonation wave continuously rotates around an annulus to produce thrust in the axial direction. However, since detonation is an extremely fast and volatile combustion mode, nonidealities in the cycle cause the control and stability of the engine to be difficult. These nonidealities include parasitic deflagration, mixture inhomogeneities, and multiple competing detonation waves. This study focuses on a stoichiometric, variable hydrogen and methane composition fuel RDE with air as the oxidizer. Specifically, an analysis was done of the effect of inlet number and increasing methane composition in combination with hydrogen fuel on the stability and performance parameters of an RDE. As the composition of methane increased, the performance values such as specific impulse and detonation wave velocity decreased up to 1000 seconds and 60 meters per second, respectively. The addition of methane in the fuel also decreased the range of inlet number in which the detonation wave was able to sustain itself. Two different scenarios produced the destabilization of the detonation wave: the coupling of parasitic deflagration and a slower detonation wave and the coupling of the Kevin-Helmholtz effect and the Rayleigh-Taylor phenomenon. However, the inclusion of methane in the fuel caused weaker reverse compression waves to be created and produced more uniform thrust. Therefore, for methane mixed with hydrogen fuel up to 15% of the mole fraction, performance and efficiency will decrease, but the detonation wave is more stable and has more uniform performance values over the cycle for certain inlet configurations. Once the methane mole fraction is greater than 15% of the fuel, the detonation wave can no longer sustain itself.

This thesis explores the design and simulation of a control system that would enable omnicopter drones to simulate close proximity spacecraft rendezvous maneuvers. Close proximity rendezvous simulations typically exist either as simplified online models that lack real-world dynamics or as prohibitively expensive physical testbeds. By leveraging the six degrees of freedom (6-DOF) capability of omnicopters—drones with non-planar rotor configurations allowing complete spatial movement developed by Dario Brescianini and Raffaello D’Andrea—this research provides a cost-effective middle ground between purely digital and full-scale physical simulations. The study implements relative orbital motion using the Clohessy-Wiltshire equations to model the dynamics between a ”chief” and ”deputy” omnicopter, simulating target and approaching spacecraft respectively in close proximity operations. A cascaded control architecture that separately handles attitude and position control enables the simulated deputy omnicopter to approach and maintain specific poses relative to the chief within small distances, mirroring the final critical phase of spacecraft rendezvous. Using MATLAB, the research validates this control strategy through simulation, demonstrating its effectiveness for precise close proximity maneuvers. This work’s contributions lie in: (1) implementing close proximity orbital rendezvous control for omnicopters in simulation, (2) providing improved documentation of omnicopter capabilities to address gaps in publicly available resources, and (3) establishing a foundation for future physical implementation and testing. While physical deployment remains outside the scope of this thesis, the comprehensive modeling and simulation work presented here creates a viable pathway between theoretical spacecraft dynamics and accessible hardware implementation for future research in close proximity operations.

Flexible, polymer-based wearable sensors have been extensively studied over the past 30 years. Conventional sensors usually consist of a conductive material deposited into or onto a polymer substrate. Laser direct writing (LDW), a more recent innovation, allows conductive graphitic carbon to be directly patterned onto the surface of a polymer sheet, eliminating the need for the addition of a separate conductor. However, there are still ways to further streamline the fabrication process, including the use of LDW to induce formation of the substrate and conductive material simultaneously. This thesis presents a route towards the functionalization of this technique through the laser irradiation of uncured, liquid-phase polydimethylsiloxane (PDMS), a thermosetting elastomer. Scanning a near-infrared, continuous wave laser in a grid pattern across the surface of PDMS produces a mesh of conductive, graphitic carbon lines within a matrix of cured, solid polymer. The resulting structure shows potential as a wearable sensor, with a resistance at rest of about 26.9 k$\Omega$. Resistance increases with the application of external forces, and the sensor is able to detect changes in pressure and strain. As a possible avenue toward higher conductivity or graphene content, small concentrations of carbon black were added to uncured PDMS. No conductivity was observed for the resulting grid structures, possibly due to changes in material absorption.

Reactivity-controlled compression ignition (RCCI) presents a promising avenue for improvements in internal combustion engine efficiency and emissions. As combustion phenomena are critical to optimizing RCCI engine performance independently of flow characteristics, a turbulent combustion model is developed within the PDRs manifold framework. The aim is to contribute to ongoing research on simplified computational models for multimodal combustion and developing a fundamental RCCI combustion model that can be applied across a wide range of fuels. A linked one-dimensional manifold model is proposed to simulate RCCI combustion by decoupling auto-ignition and homogeneous burning. High-reactivity fuel auto-ignition is modeled as an asymptotically non-premixed case, and low-reactivity fuel combustion as an asymptotically premixed case. The ignition delay time (IDT) hypothesis is presented, suggesting that true combustion behavior can be extracted by minimizing characteristic burn times. Preliminary investigations suggest that the IDT hypothesis does not hold for the particular parameters chosen here. However, the analyses conducted in this thesis provide a starting point and some generalized algorithms for future work in simplified multimodal combustion modeling

This thesis presents the design and implementation of a fully autonomous, electric go-kart, developed from a refurbished Yerf-Dog frame. The primary objective was to demonstrate vision-based autonomous navigation using low-cost hardware and open-source software. Major subsystems include a 72V electric drivetrain, a custom gear-reduction assembly, a steer-by-wire mechanism actuated via a high-torque motor, and a perception pipeline driven by real-time computer vision. A laptop running Python processes front-facing camera input using YOLOv8 for object detection and SegFormer for semantic segmentation. These outputs are encoded and transmitted to a Teensy 4.1 microcontroller, which actuates steering and throttle commands. The final system reliably performed lane following and object recognition (specifically for pedestrians and stop signs), validated through over 20 hours of autonomous testing on campus roads. Peak velocity reached 14 mph with excess torque available. The system operates for approximately 5 hours on a single charge and was built under a $2100 budget, with a total expenditure of $2040.17. Limitations in model inference speed and decision granularity were encountered, suggesting opportunities for optimization in both perception latency and control smoothing. This work serves as a proof of concept for low-cost, modular autonomous vehicles and highlights the practical integration of mechanical, electrical, and software subsystems under real-world constraints. Future development may focus on improving perception capabilities and reducing latencies of all types for higher-speed operation.

FyreFly is a technology demonstrator designed to aid first responders in a variety of fire scenarios through semi-autonomous systems targeting search, rescue, and delivery. Motivated by the need to reduce fatalities caused by smoke inhalation and remove barriers of access to trapped victims, this project presents a novel drone platform capable of assisting in identifying at-risk individuals, breaching glass windows, and delivering survival equipment. Leveraging the payload capacity of a Holybro X650, FyreFly integrates a spring-loaded window breaker, a precision package delivery system, and a smart camera framework utilizing YOLO and AprilTags for live object recognition. Throughout the design process, emphasis was placed on the modularity of parts and field repairability, incorporating 3D-printed components for rapid prototyping. FyreFly’s various subsystems were validated using experimental testing, achieving over 98% delivery accuracy and consistent success in breaking through various types of structural glass. Through this report, FyreFly is demonstrated to be a proof-of-concept serving as the foundational architecture for future drone swarms tasked with life-saving missions in urban disaster environments.

Germany’s goal of achieving net-zero emissions by 2045 has accelerated its transition to renewable energy after the previous decade’s push towards a nuclear phase-out, but simultaneously contributed to rising electricity prices and economic strain. The Russian-Ukrainian conflict further exposed vulnerabilities in Germany’s energy system, driving up energy costs and impacting both citizens and the industrial sector. As a result, debate over the role of nuclear power has reignited in political and public discourse. This thesis evaluates the economic feasibility of reactivating decommissioned nuclear power plants using the PyPSADE energy system model. Through the simulation of investment and dispatch scenarios through 2050 across Germany and neighboring countries, this study compares the impacts of nuclear reactivation with continued renewable expansion in terms of cost-effectiveness and CO2 emissions. Results from the model indicate that the reintroduction of nuclear power is too time-consuming and therefore not economically feasible due to the rise of solar and wind power, suggesting the need for additional infrastructure to support the growing renewable energy sector.

While electrification has transformed automotive transportation, the marine industry—particularly in high-power recreational and commercial applications—continues to lag behind. Gasoline-powered outboards remain the standard, despite their high emissions, noise, and maintenance demands. This thesis presents the design, fabrication, and validation of a high-performance, low-voltage electric marine outboard system tailored to fill this market and environmental gap. The system can deliver 20 kW of continuous shaft power at a nominal voltage of 55 V, integrating a dual-motor drivetrain, liquid-cooled inverters, and a modular 6.5 kWh battery pack within a corrosion-resistant, water-tight frame. The design also includes a long-range wireless control interface with video feedback and autonomous steering capabilities, laying the groundwork for unmanned and fully autonomous operation. Finite element analysis and physical testing verified the system’s mechanical stiffness, electrical performance, and thermal reliability. Compared to commercial offerings, the final system provides superior power-to-weight ratio and significantly reduced installation complexity. This work demonstrates the feasibility of compact, modular electric propulsion for a broad range of marine applications and serves as a platform for future development in scalable and autonomous electric boating.

While electrification has transformed automotive transportation, the marine industry—particularly in high-power recreational and commercial applications—continues to lag behind. Gasoline-powered outboards remain the standard, despite their high emissions, noise, and maintenance demands. This thesis presents the design, fabrication, and validation of a high-performance, low-voltage electric marine outboard system tailored to fill this market and environmental gap. The system can deliver 20 kW of continuous shaft power at a nominal voltage of 55 V, integrating a dual-motor drivetrain, liquid-cooled inverters, and a modular 6.5 kWh battery pack within a corrosion-resistant, water-tight frame. The design also includes a long-range wireless control interface with video feedback and autonomous steering capabilities, laying the groundwork for unmanned and fully autonomous operation. Finite element analysis and physical testing verified the system’s mechanical stiffness, electrical performance, and thermal reliability. Compared to commercial offerings, the final system provides superior power-to-weight ratio and significantly reduced installation complexity. This work demonstrates the feasibility of compact, modular electric propulsion for a broad range of marine applications and serves as a platform for future development in scalable and autonomous electric boating.