MENG 472/474 Projects - Spring 2021

In MENG 472/474, students work on independent projects that cover a wide range of topics, from traditional mechanical engineering topics (e.g., mechanical device design, fluid flow, and materials analysis) to interdisciplinary topics at the interface between mechanical engineering and other branches of engineering such as biomedical, chemical, electrical, or environmental engineering. Under the supervision of faculty advisers, students investigate physical phenomena through experimental measurement and/or numerical simulation, and they design and construct functioning prototypes to solve engineering problems. The majority of the faculty advisers come from within the mechanical engineering department, with the remaining advisers distributed across the University (and occasionally outside the University). Funding for projects is generously provided by the Yale SEAS Dean's Office and, in some cases, through the faculty advisers. The students were asked to write the following short summaries two-thirds of the way through the semester, when they still had a few weeks to go on their projects. All projects are represented here, except for those that cannot be publicized due to information of a proprietary nature.

About 8 million pieces of plastic enter the ocean from coastal areas each day. Clean up of plastic in the ocean has been focused on open ocean or riverine systems by large programs and companies. My research has focused on developing a prototype of a small coastal clean-up machine. It has the ability to collect macroplastics out of the water in a region based on GPS points. This project has also helped determine gaps in technology of current clean ups that have lasting effects on the oceanic ecosystem. I used this information to design a 3D design based on future technology (e.g. bladeless propellers) that can protect the ocean ecosystem from disruption.

Metallic glass, which is a metallic alloy that lacks a regular crystalline microstructure, offers many advantages over crystalline alloys of the same composition, such as increased strength and manufacturability. Until recently, there were few ways of discovering new metallic glasses besides trial and error, which is meticulous and expensive, but machine learning allows for current data to be used in models that can predict new glass formers and their properties. First, a database of known glass and non- glass formers was accumulated from 40 sources of published literature and research papers, and some early analysis was conducted to understand better the dataset, which consists primarily of ternary alloys. From these compositions, 201 elemental attribute statistics are generated from a software package called MAGPIE, written by a group at Northwestern for similar research. These 201 attributes range from valence electron number to packing efficiency of the elements. Finally, a random forest algorithm is applied to the dataset. The first output tested was the prediction of glass forming ability, which the model was able to predict accurately 89.1% of the time during 10-fold cross-validation, the standard for testing predictive models. Future models will be created to predict critical casting diameter and the supercooled liquid region.

The Mark A. Reed Group is focusing on ways to leverage nanowire field-effect transistor (nano-FET) devices in biosensing applications. Nano-FET biosensors are nanoscale electronic devices that are utilized to detect and measure biological molecules with high sensitivity and rapid response time. Our lab group is pairing the powerful biosensing qualities of nano-FET devices with the activation of cells that are essential in immune response in order to maximize diagnostic application. Employing previously fabricated devices in the lab, we measure and analyze current vs. voltage characteristics through a number of specialized electrical equipment before packaging the chip for biosensing experimentation, as shown in the associated image. The conventional die, which contains 32 nano-FET devices, must also be assessed for design structure and insulation capabilities. Finding dice that present the best current vs. voltage characteristics and microfluidic setups is essential for garnering the greatest biosensing response in these devices. During the remainder of the semester, we hope to provide substantial results that show how suitable nano-FET biosensors are highly effective when used in coordination with cells that play a strong role in immune response. Such qualities of nano-FET biosensors have strong potential for application in rapid clinical testing, antibody testing, and overall control of pathogenic microorganisms.

In the past decade numerous small electronics companies have capitalized on environmental sentiments and the need to charge personal electronics with portable solar and wind generators. Available wind generators are generally larger than one meter in diameter, designed for stationary use on a house, RV, or boat. There have been efforts recently both in the laboratory and commercial settings to shrink turbines with innovative blade designs for more practical personal applications. For this project I have begun optimizing a vertical axis wind turbine which is much smaller than others available commercially, and those generally looked at in existing research. I have done this through a mixture of computer modeling and laboratory testing. I built a basic semi static aerodynamic model to understand the characteristics of a vertical axis wind turbine, and then progressed to a more complex simulation available through open source software to begin optimizing my turbine. In parallel to this I have built a turbine to attempt to replicate the models’ behavior. To run tests I fitted the turbine with a small DC motor which can both measure the RPM and torque of the turbine while it is spinning. These measurements have allowed me to compare real life power curves to those produced by the model for many different turbine configurations with different blade shapes and blade angles.