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.
The majority of drones are extremely simple, their functions include flight and sometimes recording video and audio. While drone technology has continued to improve these functions, particularly flight, additional functions have not been added to mainstream drones. Although these basic functions serve as a good framework for drone designs, it is now time to extend off from this framework. With this Honors Thesis project, we introduce a new function intended to eventually become common to drones. This feature is a grasping mechanism that is capable of perching on branches and carrying loads within the weight limit. This concept stems from the natural behavior of many kinds of insects. It paves the way for drones to further imitate the natural design of flying creatures. Additionally, it serves to advocate for dynamic drone frames, or morphing drone frames, to become more common practice in drone designs.
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.