Ryan McClarren

Research

Prof. McClarren is interested in the modeling and simulation of a variety of energy systems with particular emphasis on particle transport calculations related to neutron behavior in fission systems and radiative transfer in inertial confinement fusion experiments. The McClarren group works on developing algorithms that perform on the world's most powerful supercomputers, building technologies for quantifying the uncertainties in simulation-based predictions, and using machine learning to enhance simulations.

Research Interests

Fusion Energy, Fission Energy, Numerical Simulation of Nuclear Energy Systems, High-Performance Computing, Machine Learning, Uncertainty Quantification

Relevant Energy Publications

1. McClarren, Ryan G., and Cory D. Hauck. "Robust and accurate filtered spherical harmonics expansions for radiative transfer." Journal of Computational Physics 229, no. 16 (2010): 5597-5614.
2. McClarren, Ryan G. Uncertainty quantification and predictive computational science. Springer International Publishing, 2018.
3. McClarren, Ryan G. Computational nuclear engineering and radiological science using python. Academic Press, 2017.
4. McClarren, Ryan G. "Calculating time eigenvalues of the neutron transport equation with dynamic mode decomposition." Nuclear Science and Engineering 193, no. 8 (2019): 854-867.
5. Bennett, William, and Ryan G. McClarren. "Self-similar solutions for high-energy density radiative transfer with separate ion and electron temperatures." Proceedings of the Royal Society A 477, no. 2249 (2021): 20210119.