Dr. Daniel Wooten
Computational Nuclear Engineer
Monte Carlo Transport · Depletion · Fuel Cycle Simulation
Ph.D. in Nuclear Engineering, UC Berkeley
My doctoral work extended SERPENT 2 — an industry-standard continuous-energy Monte Carlo reactor physics code written in C — by roughly 30,000 lines, producing a first-of-its-kind fuel-cycle depletion capability for circulating, liquid-fueled reactors. Six publications and a granted US patent, plus conference presentations including the International Serpent User Group Meeting.
Since 2020 I have shipped large-scale numerical and machine-learning systems into production under regulatory audit. I am looking to bring that engineering discipline back to reactor analysis — a domain where the verification burden is the whole point rather than an afterthought.
The R in ADER
I wrote ADER because the fuel-cycle tools available for reactors that move and reprocess their fuel rested on assumptions I could not defend, and I wanted depletion results I would be willing to put in front of a regulator.
In a recycling fuel cycle, composition stops being a given and becomes a decision variable coupled to the depletion state. That is a constrained optimization problem sitting inside a Monte Carlo transport calculation, and it is precisely what ADER was built to solve. Any program pursuing fuel recycling alongside the reactor itself is working on the problem I spent my doctorate on.
Oak Ridge National Laboratory · TerraPower · TVA
ORNL, Reactor & Nuclear Systems Division. Researched and documented computational modeling options for kinetic response simulation in circulating-fuel reactors. The resulting survey became a peer-reviewed review paper co-authored with ORNL's Jeffrey J. Powers.
TerraPower, reactor safety. Sodium fast reactor work. I built an automated Python RELAP5 input generator for the safety group, removing hand-construction of thermal-hydraulic input decks.
Tennessee Valley Authority, reactor analysis. Designed a BWR control blade history monitoring and assessment library, with data scrapers running against live reactor operational data.
It reads as a detour. It is not.
Six years validating stochastic models in a domain where every decision is auditable to a regulator — including an automated evaluation harness for exactly that purpose.
C, C++, Python and Fortran; MPI, OpenMP and CUDA; constrained optimization, sequential Monte Carlo and Bayesian inference on real hardware.
A working command of current ML and LLM tooling that most neutronics groups do not have in the room — brought back to reactor analysis, not away from it.
Wooten, D. (2019). Predicting Fuel Salt Composition via Linear Optimization in Molten Salt Reactors. University of California, Berkeley.
SERPENT 2 (code-modification level) · Monte Carlo neutron transport · depletion and fuel cycle analysis · multigroup neutron diffusion · reactor kinetics · RELAP5 · constrained and linear optimization · MPI, OpenMP, CUDA