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Abstract 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The focus of this overview for this issue of Nuclear Technology, which contains papers presented at the American Nuclear Society Advances in Nuclear Fuel Management III (ANFM-III) 2004 topical meeting, is to introduce the subject of nuclear fuel management for light water reactors. A total of 23 papers was presented on this topic at ANFM-III. Nuclear fuel management involves making the so-called out-of-core and in-core decisions. Simply put, the out-of-core decisions address the attributes of the new (fresh) fuel that will be fabricated and the partially burnt (shuffled) fuel to reinsert into the core for additional energy production. The in-core decisions address where the fresh and burnt fuel along with burnable poisons should be located in the core. The above applies to batch refueling strategies, e.g., pressurized water reactors and boiling water reactors (BWRs). For BWRs, additional in-core decisions enter to address control rod pattern paired with core flow rate as a function of burnup. It is obvious that the out-of-core and in-core decisions are coupled. The objective of nuclear fuel management is to minimize the cost of electrical energy generation subject to operational and safety constraints. Since fuel resides in the core for several cycles, a multicycle assessment is required to make nuclear fuel management decisions. For nearly four decades there has been an effort to develop automated computational capability to assist the reload core nuclear design engineer in making nuclear fuel management decisions. This development has ranged from employment of heuristic rules to utilization of mathematical optimization approaches. This overview reviews the development of nuclear fuel management optimization capabilities by first defining the problem, then describing current capabilities, and finally projecting where future capabilities need to be developed to support the needs of reload core nuclear design engineers. [ABSTRACT FROM AUTHOR]