Design and Optimization of Multistage Axial-Flow Turbine for Compressed Air Energy Storage System

In compressed air energy storage systems, turbines play a critical role in energy recovery and improving overall system efficiency. To further enhance the aerodynamic performance of turbines and reduce carbon emissions, this study developed a one-dimensional design and optimization program for multistage axial-flow turbines. Based on the constructed one-dimensional aerodynamic framework, a genetic algorithm was integrated to achieve multi-objective optimization of isentropic efficiency and hub-tip ratio. After several iterations, the turbine's isentropic efficiency improved from 92.34% to 93.50%. Building upon the one-dimensional design, a three-dimensional model of the multistage axial turbine was established and verified through numerical simulations. The simulation result showed an efficiency of 93.25%, with a relative deviation of 0.27% compared to the one-dimensional design, further confirming the accuracy and feasibility of the optimization. The results demonstrate that this method can provide key technical support for the design of low-carbon energy storage equipment in compressed air energy storage systems.