Optimizing productivity and reducing process and product design times are major challenges for various industrial sectors. Digital tools are essential for developing and improving competitiveness. Through its multidisciplinary research fields, RAPSODEE develops multi-physical, multi-scale models to provide a better understanding of processes/systems and enable their optimization. 

 

RAPSODEE focuses on:

• the development of deterministic models based on experiments carried out at different scales
• the development of data models, collected on industrial pilots or processes, for their optimal control by deep learning
• implementation of coupled numerical methods to solve these models (DEM, FEM, stochastic numerical methods such as Markov chains and Monte Carlo methods, etc.)

The combination of these different models/methods is also used for certain applications.

 

Scientific objectives

To understand, optimize and control the processes, and to develop tools for scaling-up processes, the main objectives are:

• deepening our knowledge of the processes and physico-chemical phenomena involved
• introduce more agile methods for solving coupled models requiring long computing times or complex data sets
• exploring other processes and methods to reduce carbon footprint