The axial compressors of power-generation gas turbines have a high stage count, blades with low aspect ratios and relatively large clearances in the rear section. These features promote the development of 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 the designers during the compressor design phase.
As shown in a previous work, in the rear stages of an axial compressor the stream-wise vorticity associated with tip clearance flows is one of the main drivers of the overall span-wise transport phenomenon. Limiting it by circumferentially averaging the flow at row interfaces is the reason why a steady-state analysis strongly under-predicts radial mixing. To properly forecast the span-wise transport within the flow-path, an unsteady analysis should be adopted. However, due to the high blade count, this approach has a computational cost not yet suitable for industrial purposes.
Currently, only the steady-state full-compressor simulation can fit in a lean industrial design chain and any model upgrade improving its radial mixing prediction would be highly beneficial for the daily design practice. To attain some progresses in RANS model, its inherent lack of convection of stream-wise vorticity must be addressed. This can be done by acting on another mixing driver, able to provide the same outcome, that is turbulent diffusion. In particular, by enhancing turbulent viscosity one can promote span-wise diffusion, thus improving the radial mixing prediction of the steady approach.
In this paper, this strategy to update the RANS model and its application in simulations on a compressor of the Ansaldo Energia fleet is presented, together with the model tuning that has been performed using the results of unsteady simulations as the target.