A Data Identification Method for the Improvement of a Cross-Section Model

The accuracy of a neutronics model depends not only on the validity of the equations that are solved but also on the quality of the cross-section model. This last is currently constituted by a set of correlations, the parameterized tables, relating the data of the neutronics problem to the local conditions. The more the correlations represent the local conditions, the more the results will be accurate. For a simulation model, this means that the results will be closer to the measurements. The goal of the data identification method presented is to solve a constrained inverse problem and to obtain the parameters of some further correlations that will enhance the accuracy of the results. The constraint imposed minimizes the error committed in solving the diffusion equation, using as reference the results of a more accurate computer code or the measurements performed for in-core flux maps. Some purely numerical examples and an application in conjunction with in-core measurements illustrate the method.