Large-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turbine Cascade, Part I: Flow and Secondary Vorticity Fields Under Varying Inlet Condition

Abstract

In low-pressure turbines (LPTs), around 60–70% of losses are generated away from end-walls, while the remaining 30–40% is controlled by the interaction of the blade profile with the end-wall boundary layer. Experimental and numerical studies have shown how the strength and penetration of the secondary flow depends on the characteristics of the incoming end-wall boundary layer. Experimental techniques did shed light on the mechanism that controls the growth of the secondary vortices, and scale-resolving computational fluid dynamics (CFD) allowed to dive deep into the details of the vorticity generation. Along these lines, this paper discusses the end-wall flow characteristics of the T106 LPT profile at Re = 120 K and M = 0.59 by benchmarking with experiments and investigating the impact of the incoming boundary layer state. The simulations are carried out with proven Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) solvers to determine if Reynolds-averaged models can capture the relevant flow details with enough accuracy to drive the design of this flow region. Part I of the paper focuses on the critical grid needs to ensure accurate LES and on the analysis of the overall time-averaged flow field and comparison between RANS, LES, and measurements when available. In particular, the growth of secondary flow features, the trace and strength of the secondary vortex system, and its impact on the blade load variation along the span and end-wall flow visualizations are analyzed. The ability of LES and RANS to accurately predict the secondary flows is discussed together with the implications this has on design.

Unsteady Flows and Component Interaction in Turbomachinery

“…However, a detailed description of unsteady vane/blade interaction in lowpressure turbine stages is out of the scope of this paper. Information on this topic can be found in the selected contributions by Qiu and Simon [105], Stieger et al [106], Hodson and Howell [107], Suzen and Huang [108], Sarkar and Voke [109], Mahallati et al [110], Mahallati and Sjolander [111], Michelassi et al [112], Pichler et al [113], Marconcini et al [114], Simonassi et al [115], Lopes et al [116].…”

Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective

“…In Figure 9 losses are generally split into profile and endwall, while losses generated by the unsteady stator-rotor interaction are lumped together with profile losses, partly because measurements have large difficulties to accurately resolve the unsteady flow field. The split between profile and endwall losses is somewhat arbitrary, as demonstrated by (Marconcini et al, 2019), who used both LES and RANS to dissect the spanwise loss generation mechanism in a legacy low-pressure turbine. The relative importance of profile and endwall losses is of paramount importance to blend the spanwise airfoil load.…”

Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows

“…The H block, stitched to the outlet boundary of the O mesh, is characterized by uniform mesh spacing in the axial and tangential directions in order to allow optimal resolution in the wake region and a realistic prediction of the mixing phenomenon. The grid structure and density were selected on the basis of previous experience and validation for cascade flows in incompressible and subsonic conditions and low or moderate Reynolds number [4,17,25,26]. For the Reynolds numbers considered in the present work, the y + value of the grid nodes closest to the blade surface is between 1 and 2.…”