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Flow past a high-lift low-pressure turbine (LPT) blade in a cascade could bequite complex as phenomena like separation and transition are often involved.For a highly loadedT106A blade at a high incidence and relatively low Reynoldsnumber(25, 000 < Re < 1, 00, 000), separation-induced transition is observed onthe suction side of the blade, making it a challenging problem for model-basedsimulations. In this work, computations for this flow are carried out usingRANS and hybrid LES/RANS approaches. The RANS simulations are performed withsix popular low- Re turbulence models. While turbulence models by themselvesfail to predict any separation on the T106A blade, the four-equationLangtry-Menter transition model predicts a short separation bubble. Thecharacteristic of this bubble, however, is very different from what is observedin experiments and DNS, and therefore transition is not accurately predicted.An embedded hybrid LES/RANS approach, Limited numerical scales(LNS), with anautomatic switch to LES in sufficiently resolved grids, is then used forpredictions on the sameRANS grid. With the statistical turbulence on finegrids, LES-like behavior of LNS results in an unphysical drop in Reynoldsstresses as the turbulent fluctuations are not appropriately represented on theresolved scale. Therefore, the LNS results are very similar to those obtainedwith turbulence models. However, when synthetic turbulence with correctstatistical characteristics is used to stimulate the large eddies in theembedded LES zone, LNS is able to predict separation and recovers a solutionvery close to DNS and experimental results.

Prediction of separation and transition on a low-pressure turbine blade using a RANS grid