Performance prediction, numerical and experimental investigation to characterize the flow field and thermal behavior of a cryogenic turboexpander

Abstract

Radial inflow turbine and nozzle among the other components of the cryogenic turboexpander has a significant effect on the efficiency of the system. This study proposes an effective one-dimensional design approach of a radial turbine by introducing different loss correlations. The methodology also describes the effect of non-dimensional design variables on the performance of the turbine. These variables (blade speed ratio, pressure ratio, hub and shroud to turbine inlet radius ratio) undergo artificial intelligence-based model to predict their optimal range for better efficiency and power output of the turbine. Based on these optimal ranges, two turbine and nozzle models are generated. The results of the optimized configuration show that the turbine total-to-static efficiency and power output are higher by 4% and 18.9% respectively as compared to the existing literature. Thereafter, the three-dimensional computational fluid dynamics (CFD) analysis is carried out to visualize the fluid flow and thermal characteristics at different inlet temperatures in the flow passage using ANSYS CFX®. The study also focuses to identify the flow separation zone, tip leakage flow, vortex formation, secondary losses and its reasons at different spans of the turbine. An experimental platform is also established to validate the CFD results of a case study. The experimental results show that the mass flow rate and rotational speed has major effect on temperature drop and isentropic efficiency of the turboexpander. The study highlights the importance of the design methodology, the estimation capability of artificial intelligence models, the experimental techniques and benchmarking model for numerical analysis at different cryogenic temperature.