In the context of scientific simulation, some computational tools (codes) are built as an assembly of (physical) models coupled in a numerical framework. These models and their coupling use data sets fitted on results given by experiments or fine computations of “Direct Numerical Simulation” (DNS) type in an up-scaling approach. The observables of these codes, as well as the results of the experiments or the fine computations are mostly time dependent (time series). The objective of this thesis is then to set up a methodology to improve the reliability of these codes by adjusting their parameters through data assimilation from these time series.
Work on parameter fitting has already been performed in our laboratory in a previous thesis, but using scalars derived from the temporal results of the codes. The methodology developed during this thesis has integrated screening, surrogate models and sensitivity analysis that can be extended and adapted to the new data format. A preliminary step of transformation of the time series will be developed, in order to reduce the data while limiting the loss of information. Machine learning /deep learning tools could be considered.
The application of this method will be performed within the framework of the nuclear reactor severe accident simulation. During these accidents, the core loses its integrity and corium (fuel and structure elements resulting from the reactor core fusion) is formed and can relocate and interact with its environment (liquid coolant, vessel’s steel, concrete from the basemat…). Some severe accident simulation codes describe each step / interaction individually while others describe the whole accident sequence. They have in common that they are multiphysic and have a large number of models and parameters. They describe transient physical phenomena in which the temporal aspect is important.
The thesis will be hosted by the Severe Accident Modeling Laboratory (LMAG) of the IRESNE institute at CEA Cadarache, in a team that is at the top of the national and international level for the numerical study of corium-related phenomena, from its generation to its propagation and interaction with the environment. The techniques implemented for data assimilation also have an important generic potential which ensures important opportunities for the proposed work, in the nuclear world and elsewhere.