NUCLEAR DATA UNCERTAINTY QUANTIFICATION IN MOLTEN SALT REACTORS WITH XGPT

In the framework of the European project SAMOFAR, a preliminary uncertainty propagation and quantification is carried out, focusing the attention on the influence of nuclear data uncertainty on some relevant neutronic parameters. Relying on the recently developed nuclear data sampler code SANDY and on the xGPT capabilities implemented in the Monte Carlo code SERPENT-2, the uncertainty contributions to the effective neutron multiplication factor, keff, due to the fissile and breeder nuclides can be quantified using a first-order sandwich rule. In order to verify the consistency of the uncertainty propagation performed with xGPT, the study is also performed with legacy GPT approach, available in SERPENT-2.