Numerical Investigation of Air-Oil Two-Phase Flow Pattern Transition in the Scavenge Line of an Aeroengine

Abstract

Since most research investments in aeroengines have been targeted at the hot and cold sections, the oil system has remained an area poorly understood. Optimum sizing of the oil system can directly reduce the engine’s weight and specific fuel consumption while maximizing service life. The understanding of air/oil interaction in scavenge lines is required to influence the design of the oil systems and achieve those objectives. The challenge is in the existence of numerous possible flow regimes and phase interactions. In scavenge lines, a complex two-phase flow results from the interaction of sealing airflow and lubrication oil. Scavenge lines can have bends, junctions and sudden area changes which complicates their modeling by amplifying pressure gradients and turbulence generation, and causing the flow to change morphology (annular, slug, stratified, bubbly, mist, etc.). Several multiphase flow approaches have been developed to model two-phase flow in straight scavenge lines. However, up until now, no methodology is preferred by the community for simulating two-phase flow in such application. There are still many unknowns regarding the modeling of turbulence, phase interaction and the compressibility of immiscible mixtures such as air and oil. The present study compares the performance of two numerical models: Volume of Fluid (VOF) and Algebraic Interfacial Area Density (AIAD), for simulating the air/oil flow in a suddenly expanding scavenge line against the experimental data of Ahmed et al. [1–2]. The AIAD model is a two-fluid Eulerian approach newly implemented on Ansys Fluent. Discrepancies between the two models for predicting pressure loss and void fraction are evaluated and discussed into details. The flow regime before and after the sudden expansion is identified using iso-surfaces of the void-fraction and compared against visual data. Based on the results presented, recommendations are formulated for further work regarding the calibration of AIAD modeling parameters.