Non-linear, time dependent target accuracy assessment algorithm for multi-physics, high dimensional nuclear reactor calculations

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Safety analysis and design optimization depend on the accurate prediction of various reactor core responses. Model predictions can be enhanced by reducing the uncertainty associated with the responses of interest. Accordingly, an inverse problem analysis can be designed to provide guidance to determine the optimum experimental program to reduce the uncertainties in model parameters, e.g. cross-sections and fuel pellet-clad thermal conductivity, so as to reduce the uncertainties in constrained reactor core responses. This process is referred to as target accuracy assessment. In this work a nonlinear algorithm to determine an optimum experimental program has been developed and tested.The algorithm is based on the construction of surrogate model to replace the original model used to predict the core responses and uncertainties, therefore, enabling the target accuracy assessment to treat non-linearity within reasonable computational cost. Subspace based projection techniques are used to identify the influential degrees of freedom, which are then used to construct the surrogate model. Once constructed, the new computationally efficient surrogate model is used to propagate uncertainties via Monte Carlo sampling. Moreover, this work replaces the classical objective function used for nuclear data target accuracy assessment with another that factors in the financial gains of the target accuracy assessment results and replaces [or can supplement] differential experiments with many times more readily available integral experiments.Finally, the proposed algorithm is applied on a 3-dimensional fuel assembly depletion problem with thermal-hydraulics feedback using the VERA-CS core simulator. Specifically, CASL Progression Problem Number 6 is the illustrative problem employed which resembles a pressurized water reactor fuel assembly.