A methodology for the performance prediction: flow field and thermal analysis of a helium turboexpander

Abstract

Helium liquefaction systems are widely used in nuclear fission, superconductivity, space industries, and other scientific instruments. However, the efficiency of these systems is quite low due to the cryogenic operating temperature. In this regard, the one-dimensional design methodology of the helium turbine and nozzle (hereafter, renowned as turboexpander) is important to increase the efficiency of the system. This paper demonstrates the sensitivity analysis and optimal range of non-dimensional design variables on which the radial inflow turbine has maximum efficiency, minimum losses, and maximum power output using artificial intelligence techniques. On this basis, three turboexpander models are developed within the optimal range of predicted non-dimensional variables. After that, a comparative numerical study is carried out to highlight the flow field and thermal characteristics of helium fluid. The standard two equations \(k{-}\omega \) SST model is used to solve the three-dimensional incompressible flow inside the computational domain. The numerical results are validated with the available experimental data from the existing literature. The variation of Mach number, Reynolds number, Prandtl number, static entropy, static enthalpy, temperature, and pressure inside the turboexpander is significantly affected by blade profile which is enormously affected by the design methodology. The study also demonstrates the flow separation region, vortex formation, tip leakage flow, secondary losses, and its reasons along with the spanwise location. The results highlight the importance of the design methodology, sensitivity analysis, the prediction capability of the artificial intelligence network, numerical methodology, and development of the helium turboexpander prototype models.