REU Site: Pioneering Opportunities Within Energy & Resource Systems for Undergraduate Research (POWER-UP)

The POWER-UP Research Experiences for Undergraduates (REU) Site engages undergraduate students in cutting-edge research in energy and power systems through a multidisciplinary framework spanning engineering, mathematics, and computer science. POWER-UP is designed to expand participation in STEM with the inclusion of all students including those from rural institutions, community colleges, Tribal colleges, and individuals from non-traditional pathways such as military service. The program provides a collaborative, hands-on research environment that promotes critical thinking, professional development, and scientific integrity. Participants contribute to projects with tangible societal and technological impact, including grid resilience, power systems optimization, and energy technologies. This REU Site supports NSF’s mission to promote the progress of science and national welfare by advancing energy research, expanding STEM participation, and preparing a cohort of future innovators. POWER-UP integrates undergraduate research across STEM disciplines to address grand challenges in energy and power systems through high-impact, faculty-mentored projects. Students work in areas spanning aerospace, civil, electrical, and mechanical engineering, mathematics, and computer science. One project investigates net-zero emission strategies in scramjet propulsion, focusing on hydrogen combustion in cavity flameholders at Mach 2 and comparing emissions and performance with standard AFRL fuels such as ethylene. Another project explores the effects of renewable energy source (RES) integration on baseload power plants, analyzing the impacts on CO₂ and NOₓ emissions, efficiency, and cycling-induced degradation. Civil engineering students develop finite element models of utility poles and vegetation to predict power grid vulnerabilities during extreme windstorms, supported by lidar-based mobile applications for field data collection. In computational domains, students apply statistical and machine learning methods to study the long-term effects of climate variability on electrical usage patterns. In contrast, others design physics-informed machine learning models to solve high-dimensional PDEs relevant to fluid dynamics and smart grid resilience. These projects provide undergraduates with immersive experiences in modeling, simulation, data analysis, and experimental methods. Students receive additional training in research ethics, communication, and teamwork, equipping them with the technical and professional skills needed to excel in graduate education and the future STEM workforce. 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.