To expand our ability to improve global health, the environment, and prosperity, a new level of innovation in nuclear energy is needed because nuclear must be an integral component of our low carbon strategy. However, nuclear faces both technical and non-technical challenges that prevent it from contributing as much as it could. This talk covers some of these challenges, what it might look like if we overcome those challenges, and some actions being taken in the U.S. to make this possible. Specifically, we’re building a pipeline:
1. To train university students and professionals in nuclear innovation to shift the mindset of the workforce by starting a Nuclear Innovation “Bootcamp” program.
2. To support startups and companies developing new ideas so they have a higher chance of success by building Nuclear Innovation Centers.
3. To allow private companies to access publicly-developed resources to lower the cost and technology barriers to success through the Gateway for Accelerated Innovation in Nuclear, or GAIN.
These initiatives, supported by appropriate legislative changes and international engagement, can create a new set of opportunities for global nuclear innovation.
Rachel Slaybaugh is an assistant professor of nuclear engineering at the University of California, Berkeley. At Berkeley, Prof. Slaybaugh’s research program is based in computational methods and applied to existing and advanced nuclear reactors, nuclear non-proliferation and security, and shielding applications.
Prof. Slaybaugh is also developing programs to train and inspire the next generation for nuclear innovation. She received a BS in nuclear engineering from Penn State in 2006, where she served as a licensed nuclear reactor operator. Slaybaugh went on to the University of Wisconsin–Madison to earn an MS in 2008 and a PhD in 2011 in nuclear engineering and engineering physics along with a certificate in energy analysis and policy. Throughout her career, Slaybaugh has been engaged in Software Carpentry education and training; she is particularly interested in reproducibility in computational science.
