Laminated composites are increasingly being used in various industries including <br/>automotive and aerospace. Under a variety of extreme loading conditions such as low and <br/>high-velocity impacts and crash, laminated composites delaminate. To understand how and<br/>when delamination occurs, two types of laboratory tests are conducted - End-notched <br/>Flexure (ENF) test and Double Cantilever Beam (DCB) test. The ENF test is designed to <br/>find the mode II interlaminar fracture toughness, and the DCB test, the mode I interlaminar <br/>fracture toughness. In this thesis, thermopressed Honeywell Spectra Shield® 5231 <br/>composite specimens made of ultra-high molecular weight polyethylene (UHMWPE), <br/>manufactured under two different pressures (3000 psi and 6000 psi), are tested in the <br/>laboratory to find its delamination properties. The test specimen preparation, experimental <br/>procedures, and data reduction to determine the mode I and mode II interlaminar fracture <br/>properties are discussed. The ENF test results show a 15.8% increase in strain energy <br/>release rate for the 6000 psi specimens when compared to the 3000 psi specimens. <br/>Conducting the DCB tests proved to be challenging due to the low compressive strength <br/>of the material and hence required modifications to the test specimens. An estimate of the <br/>mode I interlaminar fracture toughness was found for only two of the 6000 psi specimens.

Long distance travel around the globe can potentially be revolutionized with the use of an intercontinental rocket that uses low earth orbit as its medium. This transport system can increase growth in many new businesses like tourism travel between the continents. This research evaluates the technical and non-technical possibilities of using a double-stage reusable rocket, where the second stage is also a reusable, rocket-powered passenger vehicle using a low earth orbit space journey with a stabilized re-entry method that ensures passenger comfortability. A potential network of spaceports spanning the globe is postulated within a range of 4,000 km to 8,000 km(2,160 nm to 4,320 nm) of each other, and each located within an hour by any other means of ground transport to population hubs greater than four million. This will help further connect the world as the journey from one major city to another would take at most an hour, and no point on the habited continents would be more than 4,000 km(2,160 nm) from a spaceport. It is assumed that the costs of an international first class flight ticket are in the thousands of dollars range showing how there is a potential market for this type of travel network. The reasoning and analysis, through a literature review, for an intercontinental rocket vehicle is presented along with the various aspects of the possibility of this kind of travel network coming to fruition in the near future.

A novel concept for integration of flame-assisted fuel cells (FFC) with a gas turbine is analyzed in this paper. Six different fuels (CH4, C3H8, JP-4, JP-5, JP-10(L), and H2) are investigated for the analytical model of the FFC integrated gas turbine hybrid system. As equivalence ratio increases, the efficiency of the hybrid system increases initially then decreases because the decreasing flow rate of air begins to outweigh the increasing hydrogen concentration. This occurs at an equivalence ratio of 2 for CH4. The thermodynamic cycle is analyzed using a temperature entropy diagram and a pressure volume diagram. These thermodynamic diagrams show as equivalence ratio increases, the power generated by the turbine in the hybrid setup decreases. Thermodynamic analysis was performed to verify that energy is conserved and the total chemical energy going into the system was equal to the heat rejected by the system plus the power generated by the system. Of the six fuels, the hybrid system performs best with H2 as the fuel. The electrical efficiency with H2 is predicted to be 27%, CH4 is 24%, C3H8 is 22%, JP-4 is 21%, JP-5 is 20%, and JP-10(L) is 20%. When H2 fuel is used, the overall integrated system is predicted to be 24.5% more efficient than the standard gas turbine system. The integrated system is predicted to be 23.0% more efficient with CH4, 21.9% more efficient with C3H8, 22.7% more efficient with JP-4, 21.3% more efficient with JP-5, and 20.8% more efficient with JP-10(L). The sensitivity of the model is investigated using various fuel utilizations. When CH4 fuel is used, the integrated system is predicted to be 22.7% more efficient with a fuel utilization efficiency of 90% compared to that of 30%.

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.

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.

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.