Improving Steady Computational Fluid Dynamics to Capture the Effects of Radial Mixing in Axial Compressors

Abstract

The compressors of power-generation gas turbines (GTs) have a high stage count, blades with low aspect ratios, and large clearances. These features promote strong secondary flows. An important outcome deriving from the convection of intense secondary flows is the enhanced span-wise transport of fluid properties mainly involving the rear stages, generally referred to as “radial mixing.” An incorrect prediction of this key phenomenon may result in inaccurate performance evaluation and could mislead designers. In the rear compressor stages, the stream-wise vorticity associated with tip clearance flows is one of the main drivers of the span-wise transport phenomenon. Limiting it by averaging the flow at row interfaces is the reason why a steady analysis underpredicts radial mixing. To properly forecast the span-wise transport, an unsteady analysis should be adopted. However, this approach has a computational cost not yet suitable for industrial purposes. Currently, only the steady simulation can fit in a lean design chain and any model upgrade improving its radial mixing prediction would be highly beneficial. To attain some progresses in Reynolds-averaged Navier–Stokes (RANS) model, its lack of convection of stream-wise vorticity must be addressed. This can be done by acting on another mixing driver that is turbulent diffusion; by enhancing turbulent viscosity, one can promote span-wise diffusion, thus improving the radial mixing prediction. In this paper, this strategy to update the RANS model and its application on an existing compressor is presented, together with the model tuning that has been performed using unsteady results as the target.