Equipment: Equipping Undergraduates with Computational Engineering Skills for the Challenges of Tomorrow through Project-based Learning

The University Of Texas Rio Grande Valley, Edinburg TX

Investigators

Abstract

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Educational Instrumentation project at The University of Texas Rio Grande Valley (UTRGV) will strengthen undergraduate learning in Mechanical Engineering as well as Information and Engineering Systems. Specifically, this project will secure a High-Performance Computing Cluster, which will allow students to conduct virtual experiments on fluid flow and heat transfer, simulate structural stress and strain, develop programs for secure digital transactions, and design and test models for cybersecurity using machine learning algorithms in Computational Fluid Dynamics, Finite Element Analysis, BLOCKCHAIN, and Cybersecurity Machine Learning courses. An estimated 200 students and 5 faculty will utilize the project-funded equipment each year. In addition to providing improved experiences in Mechanical Engineering as well as Information and Engineering Systems courses, the new equipment will also be used in Senior Design, and Undergraduate Research courses at UTRGV as well as in engaging approximately 30 high school students and 5 teachers in a yearly one-week long summer camp that would extend beyond the two-year funding duration of the project. The goals of this project are to enrich the learning and experiences of undergraduate students by providing critical equipment in Mechanical Engineering as well as Information and Engineering Systems. The high-performance computing cluster, consisting of both CPU and GPU nodes, will offer the computational power necessary for conducting extensive Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) simulations. It will also support the development of Decentralized Applications (DApps) and Non-Fungible Tokens (NFTs), as well as the analysis of a comprehensive Machine Learning (ML) pipeline on realistic datasets. Students will acquire skills in accessing high-performance computing resources, utilizing massively-parallel Navier-Stokes solvers, developing robust smart contract codes, and efficiently building, running, and analyzing Machine Learning algorithms for cybersecurity applications. The project will assess the impact of the project funded equipment using a comprehensive assessment approach tailored to the involved courses. Each assessment will consist of two components: an objective component and a subjective component. The objective component will measure how well students have understood the key concepts of their respective classes, while the subjective component will gauge students’ overall satisfaction with the project and the high-performance computer cluster. Additionally, an assessment plan will be developed for the proposed one-week summer camp designed for high school students. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.