Decay heat is the energy released by the disintegration of radionuclides present in spent fuel. Precise knowledge of its average value and range of variations is important for the design and safety of spent fuel transport and storage systems. Since this information cannot be measured exhaustively, numerical simulation tools are used to estimate the nominal value of decay heat and quantify its variations due to uncertainties in nuclear data.
In this PhD, the aim is to quantify the variations in decay heat induced by reactor operating data, particularly power histories, which are the instantaneous power of fuel assemblies during their residence in the core. This task presents a particular challenge as the input data are no longer scalar quantities but time-dependent functions. Therefore, a surrogate model of the scientific computing tool will be developed to reduce computation time. The global modeling of the problem will be carried out within a Bayesian framework using model reduction approaches coupled with multifidelity methods. Bayesian inference will ultimately solve an inverse problem to quantify uncertainties induced by power histories.

The doctoral student will join the Nuclear Projects Laboratory of the IRESNE institute at CEA Cadarache. He/she will develop skills in neutron simulation, data science, and nuclear reactors. He/she will be given the opportunity to present his/her work to various audiences and publish it in peer-reviewed journals.