Cryogenic Cavitation Mitigation in a Liquid Turbine Expander of an Air-Separation Unit through Collaborative Fine-Tuned Optimization of Impeller and Fairing Cone Geometries

Abstract

An air-separation unit (ASU) uses atmospheric air to produce essential pure gaseous and liquid products for many industrial sectors but requires intensive power consumption. In recent years, cryogenic liquid turbine expanders have been used to replace the traditional J-T valves in air-separation units to save energy. In this paper, an effective design optimization method is proposed to suppress swirling flow and mitigate cavitation in liquid turbines. A flexible tuning of the impeller and fairing cone geometries is simultaneously realized, where the optimization variables are identified via a geometric sensitivity study. A novel objective function is deliberately established by allowing both swirling flow and cavitation characteristics, driving the optimizer to search for deswirling and cavitation-resistant geometries. A kriging surrogate model with an adaptive sampling strategy and a cooperative co-evolution algorithm (CCEA) are incorporated to solve the highly nonlinear optimization problem, where the former reduced the costly evaluations but simultaneously maintained the model prediction accuracy and enabled the aim-oriented global searching (the latter decomposes the problem into several readily solved sub-problems that could be solved in parallel at a high-convergence rate). The optimized impeller and fairing cone geometries were quite favorable for suppressing swirling flow and mitigating cavitation. The impeller cavitation was significantly reduced, with the maximal vapor volume fraction reduced from 0.365 to 0.17 at the blade surface; the diffuser tube high-swirl flow was significantly deswirled and the intensive vapor fraction around the centerline largely reduced, with the maximal vapor volume fraction in the diffuser tube reduced from 0.387 to 0.121. As a result, the isentropic efficiency of the liquid turbine expander was improved from 88.4% to 91.43%.