Uncertainty Quantification in high-density fluid radial-inflow turbines for renewable low-grade temperature cycles

•An advanced stochastic framework is applied to a high-density radial-inflow turbine.•The sensitivity of turbine efficiencies is dependent on the Equation of State choice.•The Equation of State greatly affects turbine efficiency at off-design conditions.•Inlet temperature and critical pressure strongly influence radial turbine efficiency. The inclusion of uncertainties in the design of turbines for renewable low-grade temperature power cycles is becoming a crucial aspect in the development of robust and reliable power blocks capable of handling a better range of efficiencies over a wider range of operational conditions. Modelling high-density fluids using existing Equations of State adds complexity to improving the system efficiency and little is known on the effect that the uncertainties of Equations of State parameters may have on the turbine efficiency. The purpose of this paper is to quantify the influence of coupled uncertain variables on the total-to-static efficiency of a radial-inflow Organic Rankine Cycle turbine with a high-density fluid R143a in a low-grade temperature renewable power block. To this end, a stochastic solution is obtained by combining a multi-dimensional generalized Polynomial Chaos approach with a full three-dimensional viscous turbulent Computational Fluid Dynamics solver for high-density radial-inflow turbines. Both operational conditions (inlet total temperature, rotational speed and mass flow rate) and Equations of State parameters (critical pressure and critical temperature) are investigated, highlighting their importance for turbine efficiency based on the consideration of three Equations of State, namely, Peng-Robinson, Soave-Redlich-Kwong, and HHEOS. This study, which is performed for both nominal and off-design operational conditions, highlights the inlet temperature as the most influential operational uncertain parameters, while the critical pressure is the most sensitive parameter for the three Equations of State tested. More importantly, it demonstrates a higher level of sensitivity of the SRK Equations of State, in particular at off-design operational conditions. This is a crucial aspect to take into account for the robust designs of Organic Rankine Cycle turbines for low-grade temperature renewable power cycles working at various conditions. It is expected that the proposed stochastic approach may consequently positively support the renewable energy sector to develop more robust and viable systems.