Experiment Optimization to Reduce Nuclear Data Uncertainties in Support of Reactor Design

The safe and economical design of new, innovative nuclear reactors will require uncertainty reduction in basic nuclear data that are input to simulations used during reactor design. These data uncertainties propagate to uncertainties in design responses, which in turn require the reactor designer to incorporate additional safety margins into the design, often increasing the cost of the reactor. Therefore, basic nuclear data need to be improved, and this is accomplished through experimentation, which is often done using cold critical experiments. Considering the high cost of nuclear experiments, it is desired to have an optimized experiment that will provide the experimental data needed for maximum uncertainty reduction in the design responses. However, the optimization of the experiment is coupled to the reactor design itself because with reduced uncertainty in the design responses the reactor design can be re-optimized. It is thus desired to find the experiment design that gives the most optimized reactor design. Solution of this nested optimization problem is made possible by the use of the simulated annealing algorithm. Cost values for experiment design specifications and reactor design specifications are estimated and used to compute a total savings by comparing the a posteriori reactor cost to the a priori cost accounting for the offsetting cost of the experiment. This was done for the Argonne National Laboratory-developed Advanced Burner Test Reactor design concept employing a modified Zero Power Physics Reactor as the experimental facility.