Enhancing energy efficiency in aero-engines through multi-fidelity optimization of labyrinth seal design

Effective leakage control and thermal management in aero-engines are critical for reducing fuel consumption and enhancing energy efficiency. This study simplifies the computational model for inter-stage labyrinth seals by incorporating prior knowledge and employs a hierarchical Kriging-based multi-fidelity surrogate model to reduce computational costs. To establish an efficient optimization framework, a robust sampling strategy and an adaptive infill method are introduced. A Latin hypercube sampling method integrated with a genetic algorithm is developed to explore the constrained design space, while the variable-fidelity pseudo expected improvement matrix method enhances surrogate model accuracy. By utilizing Pearson correlation analysis, optimization objectives are streamlined to ensure adaptability across various operating conditions. The results demonstrate that the optimized labyrinth seals achieve over 5.56 % leakage reduction and a 0.46 % decrease in total temperature rise under design conditions, resulting in an increase in engine thrust and a reduction in specific fuel consumption. Furthermore, the reduction in fin height challenges traditional perceptions and effectively reduces the risk of rubbing. The coupling analysis of thermodynamics, flow mechanism, and heat transfer reveals the superiority of the optimized structure. Compared with single-fidelity approaches, the proposed multi-fidelity method not only reduces computational resources by 36.67 % but also offers a scalable solution for designing aero-engines labyrinth seals. This work develops a new, efficient method to optimize energy efficiency in aviation, supporting global efforts to reduce carbon emissions and improve high-performance aero-engines.