We are developing a novel open-source framework for real-time high-fidelity neutron kinetics based on the hybrid transient fission matrix (TFM) methodology. 

Hybrid particle transport enables the solution of the 3-D time-dependent neutron transport equation with Monte Carlo-level accuracy while reducing the computation time by 3 or more orders of magnitude.

Artificial intelligence (AI) and machine learning (ML) applied to the TFM method can enable first-of-a-kind high-fidelity digital twins based on full 3-D time-dependent neutron transport, instead of lower-fidelity approaches (e.g., point kinetics and nodal diffusion). 

ML/AI algorithms can be used for optimizing the generation of high-fidelity response functions, learning dependencies of the TFM coefficients, and accelerating the time-convolution of the fission source.

The placement of detectors and instrumentation within the core of small and micro modular reactor is a challenge given the very limited real estate within the core. Scientists at Argonne National Laboratory have developed a methodology using on a machine-learned Green's function based on the neutron diffusion equation to achieve detailed in-core flux reconstruction using out-of-core detectors only. We are working with Argonne to validate their algorithm and evaluate the validity of the diffusion approximation.

Research reactors are an essential tool in support of the design, operation, and licensing of the next generation of advanced power reactors, as well as for the production of essential radioisotopes for industrial and biomedical applications, including diagnostics and therapy. In collaboration with Argonne National Laboratory, we are developing a specification and modern model using the OpenMC software of the IAEA-TECDOC-643 core for plate-type reactors, which will serve as the main benchmark for the development of a fully open-source suite for operation-informed depletion and fuel management software based on OpenMC and ADDER. This work supports the DOE-NNSA's Proliferation Resistance Optimization (PRO-X) project.