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Home   /   Thesis   /   Development of a soft sensor for the fuel mixing grinding process

Development of a soft sensor for the fuel mixing grinding process

Artificial intelligence & Data intelligence Engineering sciences Mathematics - Numerical analysis - Simulation Technological challenges


This thesis aims to develop an innovative soft sensor for the mixing-grinding process, crucial for nuclear fuel fabrication. The ambition is to create a soft sensor powered by experimental devices, providing real-time estimation of the properties of the product.

The central goal is to design a method that accurately and at high frequency estimates the properties of the granular medium, such as grain size. This project envisions establishing a direct relationship between physical sensors and material properties through simulation and a learning model. The challenge lies in predicting these properties without directly assessing the internal state of the process.

The candidate will work at the Fuel Research Department (IRESNE Institute, CEA-Cadarache). The candidate will first inventory suitable physical sensors, before constructing a synthetic database from simulations. This data will feed a deep recurrent learning model, adjustable by a Kalman filter. The challenge also lies in effectively integrating real test bench data with simulations, while managing inaccuracies and discrepancies.


Département d’Etudes des Combustibles
Service d’Etudes de Simulation du Comportement du combustibles
Laboratoire des méthodes numériques et composants physiques de PLEIADES
Ecole Polytechnique
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