City Research Online

Abstract

Within this study, the blade shape of a large-scale axial turbine operating with sCO2 blended with dopants is optimised using an integrated aerodynamic-structural numerical model to maximise the aerodynamic efficiency whilst meeting stress constraints. Three candidate mixtures are considered, namely CO2 blended with titaniumtetrachloride (TiCl4), hexafluorobenzene (C6F6) or sulfur dioxide (SO2) defined by the EU project, SCARABEUS. The aerodynamic performance is simulated using a single passage, 3D, steady-state, viscous computational fluid dynamic (CFD) model while the blade stress distribution is obtained from a static structural finite element analysis (FEA). A genetic algorithm is used to optimise parameters defining the blade angle and thickness distributions along the chord line while a surrogate model is used to provide fast and reliable model predictions during optimisation using genetic aggregation response surface. The uncertainty of the surrogate model is evaluated using a set of verification points and found less than 0.3% for aerodynamic efficiency and 1% for both the mass flow rate and the maximum equivalent stresses. The comparison between the final optimised blade cross-sections have shown some common trends in optimising the blade design by decreasing stator and rotor trailing edge thickness, increasing stator thickness near the trailing edge, decreasing rotor thickness near the trailing edge and decreasing the rotor outlet angle. Further investigations of the loss breakdown of the optimised and reference blade designs are presented. It has been noted that the performance improvement achieved is mainly due to decreasing the endwall losses of both blade rows.

Publication Type:	Article
Additional Information:	Copyright © 2022 by ASME
Publisher Keywords:	Blades, Shape optimization, Supercritical carbon dioxide, Turbines, Rotors, Computational fluid dynamics, Finite element analysis, Stators, Stress, Carbon dioxide, Chords (Trusses), Computer simulation, Cross section (Physics), Design, Flow (Dynamics), Genetic algorithms, Optimization, Response surface methodology, Shapes, Steady state, Stress concentration, Sulfur, Uncertainty
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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