The project's goal is to provide a feasible design of an integrated energy system to use a nuclear power plant to produce hydrogen. This will help to lower greenhouse gas emissions in the future by replacing fossil fuel sources with hydrogen, a clean burning fuel. This motivates our team to work toward a clean energy future with nuclear at its heart.

Amid the ongoing COVID-19 pandemic, there is a pressing need to explore more effective sterilization methods for personal protective equipment (PPE), particularly N95 masks. Traditional techniques have proven insufficient to meet the increasing demand for sanitized PPE, highlighting the importance of investigating alternative solutions. In this context, the utilization of advanced computational modeling, specifically through the Monte Carlo N-Particle (MCNP) code, presents a promising avenue for enhancing sterilization efficacy. 

The motivation for this project was spurred from findings of large nuclear data uncertainties associated with NuScale’s VOYGR small modular reactor (SMR) discovered by Universidad Politecnica de Mardid (UPM). Given that NuScale’s reactor is the first NRC approved Generation IV reactor design, this work is highly relevant to the current and future state of nuclear. The National Criticality Experiments Research Center (NCERC) team at Los Alamos National Lab plans on investigating this large uncertainty with the goal of a reduction through experimental procedures.

Nuclear power plants currently operate at inefficient energy conversion rates, in terms of fuel burnup and thermal efficiency, when compared to other power generation technologies. The U.S. nuclear fleet generates approximately 800 TWh of electricity per year and 2,000 tons of used nuclear fuel, most of which is Uranium-238 (approximately 93%), for an average rate of 2.5 tons per TWh. This issue affects nuclear plant owners, U.S. citizens and the Department of Energy.