Associate team COMMUNES – Computational Methods for Uncertainties in Fluids and Energy Systems
This project aims to develop numerical methods capable to take into account efficiently unsteady experimental data, synthetic data coming from numerical simulation and the global amount of uncertainty associated to measurements, and physical-model parameters. We aim to propose novel algorithms combining data-inferred stochastic modeling, uncertainty propagation through computer codes and data assimilation techniques. The applications of interest are both related to the exploitation of renewable energy sources: wind farms and solar Organic Rankine Cycles (ORCs).
Keywords: uncertainty quantification, CFD, data-inferred stochastic modeling, data assimilation, renewable energy, wind farm, ORC.