A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm

It is usually to conduct a full-scale three-dimensional flow analysis for a radial turbine to find a way to increase the efficiency of a Compressed Air Energy Storage (CAES) system. However, long solving time and huge consumption of computing resources become a major obstacle to the analysis. Therefore, in present study, a surrogate model with test data-based multi-layer perceptron (MLP) Neural Network is proposed to overcome the difficulty. Instead of complex flow field solving process, it provides reliable turbine aerodynamic performance and flow field distribution characteristics in a short solution time by “learning the measurement results”. The validation results illustrated that the predicted maximum relative errors of isentropic efficiency, corrected mass flow rate and corrected power are only 0.03%, 0.22% and 0.26% respectively. The predicted flow distribution parameters in chamber, shroud cavity and outlet region of rotor are also basically consistent with the experimental results. In the chamber, it can be found that a pressure stagnation point is observed at circumferential angle of 270° when total pressure ratio is decreased. In the shroud cavity, obvious pressure variation is found near outlet of shroud cavity which although labyrinth seals exist. At outlet of rotor, obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height. At the same time, obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height and this is because the influence of upper passage vortex, lower passage vortex and end wall secondary flow. The present study can provide further reference for the dynamic performance evaluation of CAES radial inflow turbine.