Effect of the Gas to Wall Temperature Ratio on the Bypass Transition

Abstract

The study of boundary layer transition plays a fundamental role in the field of turbomachinery owing to its strong influence on skin friction and heat transfer. The understanding of the laminar to turbulent transition can help designers to improve the aerodynamic and thermodynamic performances both of the components and of the whole machine. Turbulent transition models are nowadays commonly used tools in both research and design practice. In the context of high-pressure turbines design, it is then of particular interest to understand if such models are able to predict the effect of temperature on bypass transition and, in case of positive answer, the reasons of their behaviour. This becomes even more interesting as the effect of the flow aero-thermal coupling becomes prominent in the analysis of such phenomena, as this effect is typically not accountedfor in the validation of turbulence models. Two state-of-the-art transition models are examined in the present contribution: the γ–Reθ model developed by Langtry and Menter [1] and the k–kl–ω model by Walters and Cokljat [2]. The two models have been chosen also as they use two radically different approaches to describe the transition process: an empirical, correlation-based one for the former model opposed to a phenomenological, based on local transport, for the latter. To isolate the effects of the temperature ratio on the transition, the simulations have been performed keeping the same values of Reynolds and Mach numbers and changing the value of the wall to free stream Temperature Ratio (TR). The results of the two transition models have been compared between them as well as with experimental results obtained as part of a parallel effort. The results show that both models are sensitive to TR and can have qualitative agreement with the observations from experimental data. Most importantly the present results show how a transition modelling based on local transport, rather than empirical correlations should be favoured.