No abstract

The recent expansion of civil aviation industry into the new market demands modern aero-engines to operate in hot, harsh and polluted environments. Moreover there is a significant increase in the flight paths across the sea leading to large amount of micro salt particle ingestion into the engines. These contaminants can cause severe damage to the turbine parts through hot corrosion fatigue. The mechanism of the very small particle transport in the secondary air system and their deposition on turbine parts is less reported and not well understood. This study explores the physics of the particle transport (< 0.5–10 micron) and their deposition characteristics in the secondary air paths. Specifically, a three-dimensional computational fluid dynamics (CFD) model is developed for an engine representative turbine cavity with blade shank utilizing commercial finite volume-based software incorporating the SST k-ω turbulence model. The particle transport is captured using discrete random walk model and their wall interaction (bounce and stick) is simulated using the critical velocity model. A comprehensive parametric study is conducted using 2 and 6 micron CaSO4 particles covering a wide range of operating and design variables. From the parametric study it has been observed that rotor speed, swirl and the radial location of the feed holes strongly influence the flow structure in the shank cavity and particle deposition.

Impingement cooling is commonly employed in gas turbines to control the turbine tip clearance. During the design phase, computational fluid dynamics (CFD) is an effective way of evaluating such systems but for most turbine case cooling (TCC) systems resolving the small scale and large number of cooling holes is impractical at the preliminary design phase. This paper presents an alternative approach for predicting aerodynamic performance of TCC systems using a “smart” porous media (PM) to replace regions of cooling holes. Numerically CFD defined correlations have been developed, which account for geometry and local flow field, to define the PM loss coefficient. These are coded as a user-defined function allowing the loss to vary, within the calculation, as a function of the predicted flow and hence produce a spatial variation of mass flow matching that of the cooling holes. The methodology has been tested on various geometrical configurations representative of current TCC systems and compared to full cooling hole models. The method was shown to achieve good overall agreement while significantly reducing both the mesh count and the computational time to a practical level.