|Supervisor :||Prof. F. Avellan
|Assistant :||Dr. Loïc Andolfatto|
|Theme :||Experimental data analysis|
The “Energy Strategy 2050” plan from the Swiss Federal Council is targeting a complete replacement of nuclear power by other sources in 2050. The hydropower will play two roles towards this objective: an increase of the energy production from an improvement in the efficiency of the hydraulic machine used and an electrical grid regulation role by taking advantage of its high flexibility to compensate from peak demand and intermittent energy sources such as wind power and photovoltaics.
One of the challenges to reach the “Energy Strategy 2050” will be to operate hydraulic machines under an extended range from overload to deep part load. This relies on an efficient understanding and modelling of the hydraulic machine behavior. For this purpose, experimental tests are conducted on a reduced-scale physical model.
The data gathered during the experimental tests are subject to measurement uncertainties. A robust characterisation of the machine behaviour and of its performance must accounted for these uncertainties. The aim of this project is to enhance an in-house uncertainty propagation strategy based on the OpenTURNS Python module.
The student will conduct measurement campaigns on an instrumented test rig to build the associated hydraulic machine hillchart. The collected data will serve investigations on the impact of several parameters of the uncertainty propagation strategy on its performance and stability. He will finally propose recommendation to improve the uncertainty propagation strategy robustness accordingly.