Innovative Fast Reactors: impact of fuel composition on reactivity coefficients

Innovative fast reactors will have to comply with the major objectives as stated, e.g. within the Generation-IV initiative, namely sustainability and waste minimization, keeping their safety characteristics at least as good as Generation-III evolutionary reactors. As a consequence, a specific feature of most innovative fuels envisaged for future fast reactors is the potential high content of Minor Actinides (MA: Np, Am, Cm etc). This is the case whatever the mission assigned to a fast reactor: to be a breeder, an 'isogenerator' or a TRU burner, i.e. fast reactors with very different conversion ratios (CRs), and whatever the strategy adopted for the spent fuel processing. The MA content can vary in a wide range, according to the core design characteristics and the fuel type. In practice, the MA content can vary from a few percent (e.g. <10%) in a core with a CR ≥ 1 and with the homogeneous recycle of not-separated TRU, aiming to a MA inventory stabilisation in the fuel cycle, to ∼20-30% in the case of the fuel of a burner fast reactor (CR<1)with a Pu/MA ratio ∼1. It is well known that the introduction of such significant amounts of MA can have an impact on the core reactivity coefficients (e.g. coolant void and Doppler coefficients) and, in the case of cores with a very low CR value, on the delayed neutron fraction. Several recent studies have been devoted to quantify these effects. However, it is important to use a flexible and powerful physics tool, in order to understand the physics features of the calculated effects, to predict trends and potential limitations during preliminary design phases. This flexible and powerful physics tool is provided by the Equivalent Perturbation Theory (EGPT), as implemented in the ERANOS code system. The full paper will give a detailed analysis of these phenomena for both Na-void and Doppler reactivity coefficients in a variety of different FRs with different core characteristics. It will also be shown how to apply this type of analysis for optimizing core design features