Uncertainty quantification (UQ) is an emerging field in scientific computing that is here to stay. One of the objectives of UQ is to enable a more rigorous comparison between numerical predictions and experimental measurements both with their respective uncertainty margins for the validation of ever more complex simulation codes. In fact, the quality of the numerical prediction could be strongly influenced by the intrinsic random character of the experimental data, which affect the definition of boundary conditions, initial conditions, and the geometry. Moreover, physical models features a structural uncertainty since they are defined from theoretical assumptions or from calibration using manipulated experimental data (as a result of filtering, averaging, ..), thus yielding some empirical model-parameters. The capacity to assess the impact of these uncertainties within a computer model describing a given phenomenon, which should be consistent with respect to the experimental observations, is of fundamental importance for computing a reliable and robust prediction. This problem is especially complex for flow simulation. 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).


Research directions

  • Computation of low-probability
  • Clustering methods for propagation through systems of codes
  • Robust optimization of ORC and wind turbines

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