Small scale distributed compressed air energy storage (D-CAES) has been recognized as promising technology which can play major role in enhancing the use of renewable energy. Due to the transient behavior of the compressed air during the discharging phase, there are significant variations in air pressure, temperature and mass flow rate resulting in low turbine efficiency. This research aims to improve the expansion process of the small scale D-CAES system through optimization of a small scale axial turbine. A small scale axial air turbine has been developed using 1D Meanline approach and CFD simulation using ANSYS CFX 16.2. For improving the turbine efficiency, different optimization approaches like single and multi-operating point optimization have been performed.
The turbine blade profiles for both stator and rotor have been optimized for minimum losses and maximum power output based on 3D CFD modelling and Multi Objective Genetic Algorithm (MOGA) optimization for single and multi-operating points. Using multi-operating point optimization, the maximum turbine efficiency of 82.767 % was achieved at the design point and this approach improved the overall efficiency of D-CAES system by 8.07% for a range of inlet mass flow rate indicating the potential of this optimization approach in turbine design development
