Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors
- North Carolina State Univ., Raleigh, NC (United States); Argonne National Lab. (ANL), Argonne, IL (United States). Nuclear Engineering Division
- Argonne National Lab. (ANL), Argonne, IL (United States). Nuclear Engineering Division
- North Carolina State Univ., Raleigh, NC (United States)
The optimization of a Sodium-cooled Fast Reactor (SFR) core is a challenging process, due to the large number of design parameters, the nonlinearities among inputs and outputs, and the complicated correlation among output parameters. This study attempts to develop a generalized framework for the SFR core optimization by coupling the sensitivity analysis, advanced optimization algorithm, and optionally the surrogate modeling. The framework is built based on the fast reactor modeling capability of the Argonne Reactor Computation (ARC) suite and the sensitivity analysis and optimization modules embedded in the DAKOTA code, both have been integrated within the NEAMS Workbench. The genetic algorithm is selected as the optimization method for its robustness, while the option of surrogate modeling is also explored to alleviate the computational burden caused by employing the ARC direct core physics simulation and thus enhance the efficiency of the optimization. Finally, the normalized deviations of performance parameters of the near-optimal solution from those of the ideal core are calculated and used as criteria to down select the final core design. The developed framework is applied to the Advanced Burner Test Reactor (ABTR) core, and optimal solutions are determined by balancing various objectives simultaneously, including peak fast flux, core volume, power, reactivity swing, plutonium mass feed, while at the same time satisfying the predefined constraints due to safety or economics considerations. The optimal ABTR core design obtained using the direct physical simulation and surrogated model are compared and discussed. It is found that using the accurately constructed surrogate models can significantly reduce the required computational time while maintaining satisfactory accuracy.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1606518
- Alternate ID(s):
- OSTI ID: 1682457
- Journal Information:
- Progress in Nuclear Energy, Vol. 120, Issue C; ISSN 0149-1970
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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