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AUTOMATIC DESIGN OF ORC TURBINE PROFILES BY USING EVOLUTIONARY ALGORITHMS


Go-down asme-orc2015 Tracking Number 133

Presentation:
Session: Session 10: Turbine design II
Room: 1A Europe
Session start: 10:30 Tue 13 Oct 2015

Pablo Rodriguez-Fernandez   pablo.rf91@gmail.com
Affifliation: Politecnico di Milano

Giacomo Persico   giacomo.persico@polimi.it
Affifliation: Politecnico di Milano


Topics: - Turbines (Topics), - Simulation and Design Tools (Topics), - I prefer Oral Presentation (Presentation Preference)

Abstract:

In this paper, an automated design tool for Organic Rankine Cycle (ORC) turbines is presented. Supersonic flows and real-gas effects featuring ORC turbines complicate significantly their aerodynamic design, which may benefit significantly from the application of systematic optimization methods. This study proposes a complete method to perform shape optimization of ORC turbine blades, constructed as a combination of a generalized geometrical parametrization technique, a high-fidelity Computational Fluid Dynamic (CFD) solver (including real gas and turbulence models) and an evolutionary algorithm. As a result, a non-intrusive tool, with no need for gradients definition, is developed. The high computational burden typical of evolutionary methods is here tackled by the use of a surrogate-based optimization strategy, for which a Gaussian model is applied. % Application to ORC turbines has been proved to be successful, resulting in a comprehensive method for a very wide range of applications. In particular, the present optimization scheme has been applied to the re-design of the supersonic nozzle of an axial-flow turbine. In this design exercise very strong shocks are generated in the rear blade suction side and shock-boundary layer interaction mechanisms occur. Optimization aiming at a more uniform flow at the blade outlet section is shown to minimize the shock losses, resulting in a significant improvement in the nozzle efficiency. The optimal configuration determined with the present design tool is also successfully validated against the outcome of a previous optimization performed with a gradient-based method, demonstrating the reliability and the potential of the design methodology here proposed.