Genetic optimization design for aerodynamic structure of oblique flow compressor radial inlet chamber in compressed air energy storage system

Abstract

The compressor plays a significant role in the compressed air energy storage (CAES) system, and its performance directly determines the overall efficiency of the system and the economy of energy storage. Internal flow losses and outlet distortion due to poor design of its radial inlet chamber (RIC) geometry can degrade the performance of downstream components and reduce compressor unit performance. In this article, a multiobjective optimization design is carried out for the RIC of the oblique flow compressor in CAES system, and a parametric optimization design method of the RIC annular convergent channel meridian profile is developed. The optimal Latin hypercube experimental design (opt LHD) method is used to generate the sample library, and the radial basis function neural network (RBFNN) is used to establish the surrogate model of each design parameter and RIC performance. The multiobjective optimization is executed by the second‐generation non‐dominated sorting genetic algorithm (NSGA‐II). After optimization, the area of the annular convergent channel of the RIC is expanded, the transition of the meridian profile is smoother, the distortion coefficient of the RIC is reduced by 0.58%, and the total pressure loss coefficient is reduced by 16.45%. By using the optimized RIC, the performance of oblique flow compressor has been improved. Compared with the original model, the total pressure ratio and isentropic efficiency of the whole machine at the design point have been increased by 0.6% and 0.32%, respectively.

Study on the Influence of Radial Inlet Chamber Splitter Blades on the Oblique Flow Compressor Performance

“…The results proved that the proposed optimization method was credible, and the performance and stage efficiency of the RIC could be effectively improved after optimization. Chen [28] parametrically modeled the RIC annular convergence channel and established the surrogate model between the geometric parameters of the annular channel and RIC performance through the parallel neural network algorithm, and finally optimized by the genetic algorithm. The annular channel expanded, and the meridian profile was smoother after the optimization.…”