Neural network and CFD-based optimisation of square cavity and curved cavity static labyrinth seals

The pressure drop characteristics for leakage of water through circular grooved, square cavity and curved cavity static labyrinth seals are investigated. A semi-theoretical model employing two new terms named virtual cavity velocity and vortex loss coefficient, to determine the pressure drop across the seal is presented. Five different square cavity labyrinth seals (SCLS) were subjected to flow visualisation tests to observe the leakage flow patterns. Computational fluid dynamic (CFD) analysis was done using Fluent commercial code. The values of the vortex loss coefficient for the SCLS at turbulent flow conditions were obtained experimentally. Using the data pool, an artificial neural network (ANN) simulation model was employed to identify the optimal SCLS configuration. Based on the insights gained, two different curved cavity labyrinth seal (CCLS) geometries were developed and optimised using parametric CFD analysis. They were visualisation tested and experimentally found to have higher pressure drops and vortex loss coefficients as compared to the SCLS configurations. The studies show that the enhanced performance is due to the presence of multiple recirculation zones within their cavities, which dissipate higher amount of leakage flow momentum.