A Workflow to Optimize Fast Neutron Irradiation in A Thermal Neutron Spectrum Test Reactor Leveraging Open-Source Tools

In this work, we presented the workflow of experiment design optimization at ATR test reactor to boost neutron fast spectrum in a thermal nuclear reactor. The workflow utilizes (Figure presented) open source-tools as Monte Carlo code OpenMC, machine learning libraries in Python scikit-learn, and multi-objective optimization Python module pymoo. The optimization workflow initially tested for two layers of material filtering at one irradiation position. Preliminary results shows that the algorithm was able to generate a set of Pareto Front from which an optimal solution was selected. Further improvement will be included into the models including adding additional data points to better represent objective functions, adding additional layer for filtering out epithermal neutrons, and applying the optimization algorithm to other irradiation positions in ATR. The ultimate goal of thiu work is to compare the design values against experimental data once become available.